UNIT I
Introduction to AI Programming Languages
Artificial Intelligence is a branch of engineering, which basically aims for making the computers which can
think intelligently, in the similar manner the intelligent humans think. Artificial intelligence is the field of
computer science associated with making the machines that are programmed to be capable of thinking and
solving problems like the human brain. These machines can perform human-like tasks and can also learn from
past experiences like human beings. Artificial intelligence involves advanced algorithms and theories of
computer science. It is used in robotics and gaming extensively.
Here are the top languages that are most commonly used for making the AI projects:
Python - Python is considered to be in the first place in the list of all AI development languages due to the
simplicity. The syntaxes belonging to python are very simple and can be easily learnt. Therefore, many AI
algorithms can be easily implemented in it. Python takes short development time in comparison to other
languages like Java, C++ or Ruby. Python supports object oriented, functional as well as procedure-oriented
styles of programming. There are plenty of libraries in python, which make our tasks easier. For example:
NumPy is a library for python that helps us to solve many scientific computations. Also, we have Pybrain,
which is for using machine learning in Python.
Advantages
 Python has a rich and extensive variety of library and tools.
 Supports algorithm testing without having to implement them.
 Python supporting object-oriented design increases a programmer’s productivity.
 Compared to Java and C++, Python is faster in development.
Drawbacks
 Developers accustomed to using Python face difficulty in adjusting to completely different syntax
when they try using other languages for AI programming.
 Unlike C++ and Java, python works with the help of an interpreter which makes compilation and
execution slower in AI development.
 Not suitable for mobile computing. For AI meant for mobile applications, Python unsuitable due to its
weak language for mobile computing.
C++ - C++ is the fastest computer language, its speed is appreciated for AI programming projects that are
time sensitive. It provides faster execution and has less response time which is applied in search engines and
development of computer games. In addition, C++ allows extensive use of algorithms and is efficient in using
statistical AI techniques. Another important factor is that C++ supports re-use of programs in development
due to inheritance and data-hiding thus efficient in time and cost saving.
Advantages
 Good for finding solutions for complex AI problems.
 Rich in library functions and programming tools collection.
 C++ is a multi-paradigm programming that supports object-oriented principles thus useful in achieving
organized data.
Drawbacks
 Poor in multitasking; C++ is suitable only for implementing core or the base of specific systems or
algorithms.
 It follows the bottom-up approach thus, highly complex making it hard for newbies developers at using
it for writing AI programs.
R - R is one of the most effective language and environment for analyzing and manipulating the data for
statistical purposes. Using R, we can easily produce well-designed publication-quality plot, including
mathematical symbols and formulae where needed. Apart from being a general purpose language, R has
numerous of packages like RODBC, Gmodels, Class and Tm which are used in the field of machine learning.
These packages make the implementation of machine learning algorithms easy, for cracking the business
associated problems.
Lisp - Lisp is one of the oldest and the most suited languages for the development in AI. It was invented by
John McCarthy, the father of Artificial Intelligence in 1958. It has the capability of processing the symbolic
information
effectively.
It is also known for its excellent prototyping capabilities and easy dynamic creation of new objects, with
automatic garbage collection. Its development cycle allows interactive evaluation of expressions and
recompilation of functions or file while the program is still running. Over the years, due to advancement, many
of these features have migrated into many other languages thereby affecting the uniqueness of Lisp.
Advantages
 Fast and efficient in coding as it is supported by compilers instead of interpreters.
 Automatic memory manager was invented for LISP, therefore, it has a garbage collection.
 LISP offers specific control over systems resulting to their maximum use.
Drawbacks
 Few developers are well acquainted with Lisp programming.
 Being a vintage programming language artificial intelligence, LISP requires configuration of new
software and hardware to accommodate it use.
Prolog - This language stays alongside Lisp when we talk about development in AI field. The features
provided by it include efficient pattern matching, tree-based data structuring and automatic backtracking. All
these features provide a surprisingly powerful and flexible programming framework. Prolog is widely used
for working on medical projects and also for designing expert AI systems.
Advantages
 Prolog has a built-in list handling essential in representing tree-based data structures.
 Efficient for fast prototyping for AI programs to be released modules frequently.
 Allows database creation simultaneous with running of the program.
Drawbacks
 Despite prolog old age, it has not been fully standardized in that some features differ in implementation
making the work of the developer cumbersome.
Java - Java can also be considered as a good choice for AI development. Artificial intelligence has lot to do
with search algorithms, artificial neural networks and genetic programming. Java provides many benefits:
easy use, debugging ease, package services, simplified work with large-scale projects, graphical representation
of data and better user interaction. It also has the incorporation of Swing and SWT (the Standard Widget
Toolkit). These tools make graphics and interfaces look appealing and sophisticated.
Advantages
 Very portable; it is easy to implement on different platforms because of Virtual Machine Technology.
 Unlike C++, Java is simple to use and even debug.
 Has an automatic memory manager which eases the work of the developer.
Disadvantages
 Java is, however, slower than C++, it has less speed in execution and more response time.
 Though highly portable, on older platforms, java would require dramatic changes on software and
hardware to facilitate.
 Java is also a generally immature programming AI language as there are still some developments
ongoing such as JDK 1.1 in beta.
Blind/Uninformed Search Strategies
Uninformed search is a class of general-purpose search algorithms which operates in brute force-way.
Uninformed search algorithms do not have additional information about state or search space other than how
to traverse the tree, so it is also called blind search.
Following are the various types of uninformed search algorithms:
1. Breadth-first Search
2. Depth-first Search
3. Depth-limited Search
4. Iterative deepening depth-first search
5. Uniform cost search
6. Bidirectional Search
1. Breadth-first Search:
 Breadth-first search is the most common search strategy for traversing a tree or graph. This algorithm
searches breadthwise in a tree or graph, so it is called breadth-first search.
 BFS algorithm starts searching from the root node of the tree and expands all successor node at the
current level before moving to nodes of next level.
 The breadth-first search algorithm is an example of a general-graph search algorithm.
 Breadth-first search implemented using FIFO queue data structure.
Advantages:
 BFS will provide a solution if any solution exists.
 If there are more than one solution for a given problem, then BFS will provide the minimal solution
which requires the least number of steps.
Disadvantages:
 It requires lots of memory since each level of the tree must be saved into memory to expand the next
level.
 BFS needs lots of time if the solution is far away from the root node.
Example:
In the below tree structure, we have shown the traversing of the tree using BFS algorithm from the root node
S to goal node K. BFS search algorithm traverse in layers, so it will follow the path which is shown by the
dotted arrow, and the traversed path will be:
1. S---> A--->B---->C--->D---->G--->H--->E---->F---->I---->K
2. Time Complexity: Time Complexity of BFS algorithm can be obtained by the number of nodes
traversed in BFS until the shallowest Node. Where the d= depth of shallowest solution and b is a
node at every state.
3. T (b) = 1+b2+b3+.......+ bd= O (bd)
4. Space Complexity: Space complexity of BFS algorithm is given by the Memory size of frontier
which is O(bd).
5. Completeness: BFS is complete, which means if the shallowest goal node is at some finite depth,
then BFS will find a solution.
6. Optimality: BFS is optimal if path cost is a non-decreasing function of the depth of the node.
2. Depth-first Search
 Depth-first search is a recursive algorithm for traversing a tree or graph data structure.
 It is called the depth-first search because it starts from the root node and follows each path to its greatest
depth node before moving to the next path.
 DFS uses a stack data structure for its implementation.
 The process of the DFS algorithm is similar to the BFS algorithm.
Advantage:
 DFS requires very less memory as it only needs to store a stack of the nodes on the path from root
node to the current node.
 It takes less time to reach to the goal node than BFS algorithm (if it traverses in the right path).
Disadvantage:
 There is the possibility that many states keep re-occurring, and there is no guarantee of finding the
solution.
 DFS algorithm goes for deep down searching and sometime it may go to the infinite loop.
Example:
In the below search tree, we have shown the flow of depth-first search, and it will follow the order as:
Root node--->Left node ----> right node.
It will start searching from root node S, and traverse A, then B, then D and E, after traversing E, it will
backtrack the tree as E has no other successor and still goal node is not found. After backtracking it will
traverse node C and then G, and here it will terminate as it found goal node.
Completeness: DFS search algorithm is complete within finite state space as it will expand every node within
a limited search tree.
Time Complexity: Time complexity of DFS will be equivalent to the node traversed by the algorithm. It is
given by:
T(n)= 1+ n2+ n3 +.........+ nm=O(nm)
Where, m= maximum depth of any node and this can be much larger than d (Shallowest solution depth)
Space Complexity: DFS algorithm needs to store only single path from the root node, hence space complexity
of DFS is equivalent to the size of the fringe set, which is O(bm).
Optimal: DFS search algorithm is non-optimal, as it may generate a large number of steps or high cost to
reach to the goal node.
3. Depth-Limited Search Algorithm:
A depth-limited search algorithm is similar to depth-first search with a predetermined limit. Depth-limited
search can solve the drawback of the infinite path in the Depth-first search. In this algorithm, the node at the
depth limit will treat as it has no successor nodes further.
Depth-limited search can be terminated with two Conditions of failure:
 Standard failure value: It indicates that problem does not have any solution.
 Cutoff failure value: It defines no solution for the problem within a given depth limit.
Advantages:
 Depth-limited search is Memory efficient.
Disadvantages:
 Depth-limited search also has a disadvantage of incompleteness.
 It may not be optimal if the problem has more than one solution.
Example:
Completeness: DLS search algorithm is complete if the solution is above the depth-limit.
Time Complexity: Time complexity of DLS algorithm is O(bℓ).
Space Complexity: Space complexity of DLS algorithm is O(b×ℓ).
Optimal: Depth-limited search can be viewed as a special case of DFS, and it is also not optimal even if ℓ>d.
4. Uniform-cost Search Algorithm:
Uniform-cost search is a searching algorithm used for traversing a weighted tree or graph. This algorithm
comes into play when a different cost is available for each edge. The primary goal of the uniform-cost search
is to find a path to the goal node which has the lowest cumulative cost. Uniform-cost search expands nodes
according to their path costs form the root node. It can be used to solve any graph/tree where the optimal cost
is in demand. A uniform-cost search algorithm is implemented by the priority queue. It gives maximum
priority to the lowest cumulative cost. Uniform cost search is equivalent to BFS algorithm if the path cost of
all edges is the same.
Advantages:
 Uniform cost search is optimal because at every state the path with the least cost is chosen.
Disadvantages:
 It does not care about the number of steps involve in searching and only concerned about path cost.
Due to which this algorithm may be stuck in an infinite loop.
Example:
Completeness:
Uniform-cost search is complete, such as if there is a solution, UCS will find it.
Time Complexity:
Let C* is Cost of the optimal solution, and ε is each step to get closer to the goal node. Then the number of
steps is = C*/ε+1. Here we have taken +1, as we start from state 0 and end to C*/ε.
Hence, the worst-case time complexity of Uniform-cost search isO(b1 + [C*/ε])/.
Space Complexity:
The same logic is for space complexity so, the worst-case space complexity of Uniform-cost search is O(b1 +
[C*/ε]).
Optimal:
Uniform-cost search is always optimal as it only selects a path with the lowest path cost.
5. Iterative deepening depth-first Search:
The iterative deepening algorithm is a combination of DFS and BFS algorithms. This search algorithm finds
out the best depth limit and does it by gradually increasing the limit until a goal is found.
This algorithm performs depth-first search up to a certain "depth limit", and it keeps increasing the depth limit
after each iteration until the goal node is found.
This Search algorithm combines the benefits of Breadth-first search's fast search and depth-first search's
memory efficiency.
The iterative search algorithm is useful uninformed search when search space is large, and depth of goal node
is unknown.
Advantages:
 Itcombines the benefits of BFS and DFS search algorithm in terms of fast search and memory
efficiency.
Disadvantages:
 The main drawback of IDDFS is that it repeats all the work of the previous phase.
Example:
Following tree structure is showing the iterative deepening depth-first search. IDDFS algorithm performs
various iterations until it does not find the goal node. The iteration performed by the algorithm is given as:
1'st Iteration-----> A
2'nd Iteration----> A, B, C
3'rd Iteration------>A, B, D, E, C, F, G
4'th Iteration------>A, B, D, H, I, E, C, F, K, G
In the fourth iteration, the algorithm will find the goal node.
Completeness:
This algorithm is complete is ifthe branching factor is finite.
Time Complexity:
Let's suppose b is the branching factor and depth is d then the worst-case time complexity is O(bd).
Space Complexity:
The space complexity of IDDFS will be O(bd).
Optimal:
IDDFS algorithm is optimal if path cost is a non- decreasing function of the depth of the node.
6. Bidirectional Search Algorithm:
Bidirectional search algorithm runs two simultaneous searches, one form initial state called as forward-search
and other from goal node called as backward-search, to find the goal node. Bidirectional search replaces one
single search graph with two small subgraphs in which one starts the search from an initial vertex and other
starts from goal vertex. The search stops when these two graphs intersect each other.
Bidirectional search can use search techniques such as BFS, DFS, DLS, etc.
Advantages:
 Bidirectional search is fast.
 Bidirectional search requires less memory
Disadvantages:
 Implementation of the bidirectional search tree is difficult.
 In bidirectional search, one should know the goal state in advance.
Example:
In the below search tree, bidirectional search algorithm is applied. This algorithm divides one graph/tree into
two sub-graphs. It starts traversing from node 1 in the forward direction and starts from goal node 16 in the
backward direction.
The algorithm terminates at node 9 where two searches meet.
Completeness: Bidirectional Search is complete if we use BFS in both searches.
Time Complexity: Time complexity of bidirectional search using BFS is O(bd).
Space Complexity: Space complexity of bidirectional search is O(bd).
Optimal: Bidirectional search is Optimal.
Breadth First Search
Breadth First Search (BFS) searches breadth-wise in the problem space. Breadth-First search is like
traversing a tree where each node is a state which may a be a potential candidate for solution. It expands nodes
from the root of the tree and then generates one level of the tree at a time until a solution is found. It is very
easily implemented by maintaining a queue of nodes. Initially the queue contains just the root. In each
iteration, node at the head of the queue is removed and then expanded. The generated child nodes are then
added to the tail of the queue.
Algorithm: Breadth-First Search
1. Create a variable called NODE-LIST and set it to the initial state.
2. Loop until the goal state is found or NODE-LIST is empty.
a. Remove the first element, say E, from the NODE-LIST. If NODE-LIST was empty then quit.
b. For each way that each rule can match the state described in E do:
i) Apply the rule to generate a new state.
ii) If the new state is the goal state, quit and return this state.
iii) Otherwise add this state to the end of NODE-LIST
Since it never generates a node in the tree until all the nodes at shallower levels have been generated, breadthfirst search always finds a shortest path to a goal. Since each node can be generated in constant time, the
amount of time used by Breadth first search is proportional to the number of nodes generated, which is a
function of the branching factor b and the solution d. Since the number of nodes at level d is bd, the total
number of nodes generated in the worst case is b + b2 + b3 +… + bd i.e. O(bd) , the asymptotic time complexity
of breadth first search.
Breadth First Search
Look at the above tree with nodes starting from root node, R at the first level, A and B at the second level and
C, D, E and F at the third level. If we want to search for node E then BFS will search level by level. First it
will check if E exists at the root. Then it will check nodes at the second level. Finally, it will find E the third
level.
Advantages of Breadth-First Search
1. Breadth first search will never get trapped exploring the useless path forever.
2. If there is a solution, BFS will definitely find it out.
3. If there is more than one solution then BFS can find the minimal one that requires less number of steps.
Disadvantages of Breadth-First Search
1. The main drawback of Breadth first search is its memory requirement. Since each level of the tree
must be saved in order to generate the next level, and the amount of memory is proportional to the
number of nodes stored, the space complexity of BFS is O(bd). As a result, BFS is severely spacebound in practice so will exhaust the memory available on typical computers in a matter of minutes.
2. If the solution is farther away from the root, breath first search will consume lot of time.
Depth First Search
In depth-first search, the frontier acts like a last-in first-out stack. The elements are added to the stack one at
a time. The one selected and taken off the frontier at any time is the last element that was added.
Figure 1: The order nodes are expanded in depth-first search
Example: Consider the tree-shaped graph in Figure 1. Suppose the start node is the root of the tree (the node
at the top) and the nodes are ordered from left to right so that the leftmost neighbour is added to the stack last.
In depth-first search, the order in which the nodes are expanded does not depend on the location of the goals.
The first sixteen nodes expanded are numbered in order of expansion in Figure 1. The shaded nodes are the
nodes at the ends of the paths on the frontier after the first sixteen steps.
Notice how the first six nodes expanded are all in a single path. The sixth node has no neighbours. Thus, the
next node that is expanded is a child of the lowest ancestor of this node that has unexpanded children.
Implementing the frontier as a stack results in paths being pursued in a depth-first manner - searching one path
to its completion before trying an alternative path. This method is said to involve backtracking: The algorithm
selects a first alternative at each node, and it backtracks to the next alternative when it has pursued all of the
paths from the first selection. Some paths may be infinite when the graph has cycles or infinitely many nodes,
in which case a depth-first search may never stop.
This algorithm does not specify the order in which the neighbours are added to the stack that represents the
frontier. The efficiency of the algorithm is sensitive to this ordering.
Because depth-first search is sensitive to the order in which the neighbours are added to the frontier, care must
be taken to do it sensibly. This ordering can be done statically (so that the order of the neighbours is fixed) or
dynamically (where the ordering of the neighbours depends on the goal).
Advantages
 space is restricted;
 many solutions exist, perhaps with long path lengths, particularly for the case where nearly all paths
lead to a solution; or
 the order of the neighbours of a node are added to the stack can be tuned so that solutions are found
on the first try.
Disadvantages
 it is possible to get caught in infinite paths; this occurs when the graph is infinite or when there are
cycles in the graph; or
 solutions exist at shallow depth, because in this case the search may look at many long paths before
finding the short solutions.
Heuristic Search Techniques
Informed search algorithm contains an array of knowledge such as how far we are from the goal, path cost,
how to reach to goal node, etc. This knowledge help agents to explore less to the search space and find more
efficiently the goal node.
The informed search algorithm is more useful for large search space. Informed search algorithm uses the idea
of heuristic, so it is also called Heuristic search.
Heuristics function: Heuristic is a function which is used in Informed Search, and it finds the most promising
path. It takes the current state of the agent as its input and produces the estimation of how close agent is from
the goal. The heuristic method, however, might not always give the best solution, but it guaranteed to find a
good solution in reasonable time. Heuristic function estimates how close a state is to the goal. It is represented
by h(n), and it calculates the cost of an optimal path between the pair of states. The value of the heuristic
function is always positive.
Admissibility of the heuristic function is given as:
1. h(n) <= h*(n)
Here h(n) is heuristic cost, and h*(n) is the estimated cost. Hence heuristic cost should be less than or
equal to the estimated cost.
Pure Heuristic Search:
Pure heuristic search is the simplest form of heuristic search algorithms. It expands nodes based on their
heuristic value h(n). It maintains two lists, OPEN and CLOSED list. In the CLOSED list, it places those nodes
which have already expanded and in the OPEN list, it places nodes which have yet not been expanded.
On each iteration, each node n with the lowest heuristic value is expanded and generates all its successors and
n is placed to the closed list. The algorithm continues unit a goal state is found.
In the informed search we will discuss two main algorithms which are given below:
 Hill Climbing
 Best First Search Algorithm (Greedy search)
 A* Search Algorithm
Hill Climbing
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Hill climbing algorithm is a local search algorithm which continuously moves in the direction of
increasing elevation/value to find the peak of the mountain or best solution to the problem. It terminates
when it reaches a peak value where no neighbour has a higher value.
Hill climbing algorithm is a technique which is used for optimizing the mathematical problems. One
of the widely discussed examples of Hill climbing algorithm is Traveling-salesman Problem in which
we need to minimize the distance travelled by the salesman.
It is also called greedy local search as it only looks to its good immediate neighbour state and not
beyond that.
A node of hill climbing algorithm has two components which are state and value.
Hill Climbing is mostly used when a good heuristic is available.
In this algorithm, we don't need to maintain and handle the search tree or graph as it only keeps a single
current state.
Features of Hill Climbing:
Following are some main features of Hill Climbing Algorithm:
 Generate and Test variant: Hill Climbing is the variant of Generate and Test method. The Generate
and Test method produce feedback which helps to decide which direction to move in the search space.
 Greedy approach: Hill-climbing algorithm search moves in the direction which optimizes the cost.
 No backtracking: It does not backtrack the search space, as it does not remember the previous states.
State-space Diagram for Hill Climbing:
The state-space landscape is a graphical representation of the hill-climbing algorithm which is showing a
graph between various states of algorithm and Objective function/Cost.
On Y-axis we have taken the function which can be an objective function or cost function, and state-space on
the x-axis. If the function on Y-axis is cost then, the goal of search is to find the global minimum and local
minimum. If the function of Y-axis is Objective function, then the goal of the search is to find the global
maximum and local maximum.
Different regions in the state space landscape:
Local Maximum: Local maximum is a state which is better than its neighbour states, but there is also another
state which is higher than it.
Global Maximum: Global maximum is the best possible state of state space landscape. It has the highest
value of objective function.
Current state: It is a state in a landscape diagram where an agent is currently present.
Flat local maximum: It is a flat space in the landscape where all the neighbour states of current states have
the same value.
Shoulder: It is a plateau region which has an uphill edge.
Types of Hill Climbing Algorithm:
 Simple hill Climbing:
 Steepest-Ascent hill-climbing:
 Stochastic hill Climbing:
1. Simple Hill Climbing:
Simple hill climbing is the simplest way to implement a hill climbing algorithm. It only evaluates the
neighbour node state at a time and selects the first one which optimizes current cost and set it as a
current state. It only checks it's one successor state, and if it finds better than the current state, then move
else be in the same state. This algorithm has the following features:
 Less time consuming
 Less optimal solution and the solution is not guaranteed
Algorithm for Simple Hill Climbing:
 Step 1: Evaluate the initial state, if it is goal state then return success and Stop.
 Step 2: Loop Until a solution is found or there is no new operator left to apply.
 Step 3: Select and apply an operator to the current state.
 Step 4: Check new state:
1. If it is goal state, then return success and quit.
2. Else if it is better than the current state then assign new state as a current state.
3. Else if not better than the current state, then return to step2.
 Step 5: Exit.
2. Steepest-Ascent hill climbing:
The steepest-Ascent algorithm is a variation of simple hill climbing algorithm. This algorithm examines all
the neighbouring nodes of the current state and selects one neighbour node which is closest to the goal state.
This algorithm consumes more time as it searches for multiple neighbours
Algorithm for Steepest-Ascent hill climbing:
 Step 1: Evaluate the initial state, if it is goal state then return success and stop, else make current state
as initial state.
 Step 2: Loop until a solution is found or the current state does not change.
1. Let SUCC be a state such that any successor of the current state will be better than it.
2. For each operator that applies to the current state:
I.
Apply the new operator and generate a new state.
II.
Evaluate the new state.
III.
If it is goal state, then return it and quit, else compare it to the SUCC.
IV.
If it is better than SUCC, then set new state as SUCC.
V.
If the SUCC is better than the current state, then set current state to SUCC.
 Step 5: Exit.
3. Stochastic hill climbing:
Stochastic hill climbing does not examine for all its neighbour before moving. Rather, this search algorithm
selects one neighbor node at random and decides whether to choose it as a current state or examine another
state.
Problems in Hill Climbing Algorithm:
1. Local Maximum: A local maximum is a peak state in the landscape which is better than each of its
neighbouring states, but there is another state also present which is higher than the local maximum.
Solution: Backtracking technique can be a solution of the local maximum in state space landscape. Create a
list of the promising path so that the algorithm can backtrack the search space and explore other paths as well.
2. Plateau: A plateau is the flat area of the search space in which all the neighbour states of the current state
contains the same value, because of this algorithm does not find any best direction to move. A hill-climbing
search might be lost in the plateau area.
Solution: The solution for the plateau is to take big steps or very little steps while searching, to solve the
problem. Randomly select a state which is far away from the current state so it is possible that the algorithm
could find non-plateau region.
3. Ridges: A ridge is a special form of the local maximum. It has an area which is higher than its surrounding
areas, but itself has a slope, and cannot be reached in a single move.
Solution: With the use of bidirectional search, or by moving in different directions, we can improve this
problem.
Simulated Annealing:
A hill-climbing algorithm which never makes a move towards a lower value guaranteed to be incomplete
because it can get stuck on a local maximum. And if algorithm applies a random walk, by moving a successor,
then it may complete but not efficient. Simulated Annealing is an algorithm which yields both efficiency and
completeness.
In mechanical term Annealing is a process of hardening a metal or glass to a high temperature then cooling
gradually, so this allows the metal to reach a low-energy crystalline state. The same process is used in
simulated annealing in which the algorithm picks a random move, instead of picking the best move. If the
random move improves the state, then it follows the same path. Otherwise, the algorithm follows the path
which has a probability of less than 1 or it moves downhill and chooses another path.
Best-first Search Algorithm (Greedy Search)
Greedy best-first search algorithm always selects the path which appears best at that moment. It is the
combination of depth-first search and breadth-first search algorithms. It uses the heuristic function and search.
Best-first search allows us to take the advantages of both algorithms. With the help of best-first search, at each
step, we can choose the most promising node. In the best first search algorithm, we expand the node which is
closest to the goal node and the closest cost is estimated by heuristic function, i.e.
1. f(n)= g(n).
Where, h(n)= estimated cost from node n to the goal.
The greedy best first algorithm is implemented by the priority queue.
Best first search algorithm:
 Step 1: Place the starting node into the OPEN list.
 Step 2: If the OPEN list is empty, Stop and return failure.
 Step 3: Remove the node n, from the OPEN list which has the lowest value of h(n), and places it in
the CLOSED list.
 Step 4: Expand the node n, and generate the successors of node n.
 Step 5: Check each successor of node n, and find whether any node is a goal node or not. If any
successor node is goal node, then return success and terminate the search, else proceed to Step 6.
 Step 6: For each successor node, algorithm checks for evaluation function f(n), and then check if the
node has been in either OPEN or CLOSED list. If the node has not been in both list, then add it to the
OPEN list.
 Step 7: Return to Step 2.
Advantages:
 Best first search can switch between BFS and DFS by gaining the advantages of both the algorithms.
 This algorithm is more efficient than BFS and DFS algorithms.
Disadvantages:
 It can behave as an unguided depth-first search in the worst case scenario.
 It can get stuck in a loop as DFS.
 This algorithm is not optimal.
Example:
Consider the below search problem, and we will traverse it using greedy best-first search. At each iteration,
each node is expanded using evaluation function f(n)=h(n) , which is given in the below table.
In this search example, we are using two lists which are OPEN and CLOSED Lists. Following are the iteration
for traversing the above example.
Expand the nodes of S and put in the CLOSED list
Initialization: Open [A, B], Closed [S]
Iteration 1: Open [A], Closed [S, B]
Iteration 2: Open [E, F, A], Closed [S, B]
: Open [E, A], Closed [S, B, F]
Iteration 3: Open [I, G, E, A], Closed [S, B, F]
: Open [I, E, A], Closed [S, B, F, G]
Hence the final solution path will be: S----> B----->F----> G
Time Complexity: The worst-case time complexity of Greedy best first search is O(bm).
Space Complexity: The worst case space complexity of Greedy best first search is O(bm). Where, m is the
maximum depth of the search space.
Complete: Greedy best-first search is also incomplete, even if the given state space is finite.
Optimal: Greedy best first search algorithm is not optimal.
A* Search Algorithm
A* search is the most commonly known form of best-first search. It uses heuristic function h(n), and cost to
reach the node n from the start state g(n). It has combined features of UCS and greedy best-first search, by
which it solve the problem efficiently. A* search algorithm finds the shortest path through the search space
using the heuristic function. This search algorithm expands less search tree and provides optimal result faster.
A* algorithm is similar to UCS except that it uses g(n)+h(n) instead of g(n).
In A* search algorithm, we use search heuristic as well as the cost to reach the node. Hence we can combine
both costs as following, and this sum is called as a fitness number.
At each point in the search space, only that node is expanded which have the lowest value of f(n), and the
algorithm terminates when the goal node is found.
Algorithm of A* search:
 Step1: Place the starting node in the OPEN list.
 Step 2: Check if the OPEN list is empty or not, if the list is empty then return failure and stops.
 Step 3: Select the node from the OPEN list which has the smallest value of evaluation function (g+h),
if node n is goal node then return success and stop, otherwise
 Step 4: Expand node n and generate all of its successors, and put n into the closed list. For each
successor n', check whether n' is already in the OPEN or CLOSED list, if not then compute evaluation
function for n' and place into Open list.
 Step 5: Else if node n' is already in OPEN and CLOSED, then it should be attached to the back pointer
which reflects the lowest g(n') value.
 Step 6: Return to Step 2.
Advantages:
 A* search algorithm is the best algorithm than other search algorithms.
 A* search algorithm is optimal and complete.
 This algorithm can solve very complex problems.
Disadvantages:
 It does not always produce the shortest path as it mostly based on heuristics and approximation.
 A* search algorithm has some complexity issues.
 The main drawback of A* is memory requirement as it keeps all generated nodes in the memory, so it
is not practical for various large-scale problems.
Example:
In this example, we will traverse the given graph using the A* algorithm. The heuristic value of all states is
given in the below table so we will calculate the f(n) of each state using the formula f(n)= g(n) + h(n), where
g(n) is the cost to reach any node from start state.
Here we will use OPEN and CLOSED list.
Solution:
Initialization: {(S, 5)}
Iteration1: {(S--> A, 4), (S-->G, 10)}
Iteration2: {(S--> A-->C, 4), (S--> A-->B, 7), (S-->G, 10)}
Iteration3: {(S--> A-->C--->G, 6), (S--> A-->C--->D, 11), (S--> A-->B, 7), (S-->G, 10)}
Iteration 4 will give the final result, as S--->A--->C--->G it provides the optimal path with cost 6.
Points to remember:
 A* algorithm returns the path which occurred first, and it does not search for all remaining paths.
 The efficiency of A* algorithm depends on the quality of heuristic.
 A* algorithm expands all nodes which satisfy the condition f(n) <="" li="">
Complete: A* algorithm is complete as long as:
 Branching factor is finite.
 Cost at every action is fixed.
Optimal: A* search algorithm is optimal if it follows below two conditions:
 Admissible: the first condition requires for optimality is that h(n) should be an admissible heuristic
for A* tree search. An admissible heuristic is optimistic in nature.
 Consistency: Second required condition is consistency for only A* graph-search.
If the heuristic function is admissible, then A* tree search will always find the least cost path.
Time Complexity: The time complexity of A* search algorithm depends on heuristic function, and the
number of nodes expanded is exponential to the depth of solution d. So, the time complexity is O(b^d), where
b is the branching factor.
Space Complexity: The space complexity of A* search algorithm is O(b^d)
AO* Algorithm
AO* Algorithm basically based on problem decomposition (Breakdown problem into small pieces)
When a problem can be divided into a set of sub problems, where each sub problem can be solved separately
and a combination of these will be a solution, AND-OR graphs or AND - OR trees are used for representing
the solution.
The decomposition of the problem or problem reduction generates AND arcs.
AND-OR Graph
The figure shows an AND-OR graph
1. To pass any exam, we have two options, either cheating or hard work.
2. In this graph we are given two choices, first do cheating or (The red line) work hard and (The arc)
pass.
3. When we have more than one choice and we have to pick one, we apply OR condition to choose
one.(That's what we did here).
4. Basically the ARC here denote AND condition.
5. Here we have replicated the arc between the work hard and the pass because by doing the hard work
possibility of passing an exam is more than cheating.
A* Vs AO*
1. Both are part of informed search technique and use heuristic values to solve the problem.
2. The solution is guaranteed in both algorithms.
3. A* always gives an optimal solution (shortest path with low cost) But It is not guaranteed to that AO*
always provide an optimal solution.
4. Reason: Because AO* does not explore all the solution path once it got solution.
How AO* works
Let's try to understand it with the following diagram
The algorithm always moves towards a lower cost value.
Basically, we will calculate the cost function here (F(n)= G (n) + H (n))
H: heuristic/ estimated value of the nodes. and G: actual cost or edge value (here unit value).
Here we have taken the edges value 1, meaning we have to focus solely on the heuristic value.
1. The Purple colour values are edge values (here all are same that is one).
2. The Red colour values are Heuristic values for nodes.
3. The Green colour values are New Heuristic values for nodes.
Procedure:
1. In the above diagram we have two ways from A to D or A to B-C (because of and condition). calculate
cost to select a path
2. F(A-D) = 1+10 = 11
and
F(A-BC) = 1 + 1 + 6 +12 = 20
3. As we can see F(A-D) is less than F(A-BC) then the algorithm chooses the path F(A-D).
4. Form D we have one choice that is F-E.
5. F(A-D-FE) = 1+1+ 4 +4 =10
6. Basically 10 is the cost of reaching FE from D. And Heuristic value of node D also denote the cost
of reaching FE from D. So, the new Heuristic value of D is 10.
7. And the Cost from A-D remain same that is 11.
Suppose we have searched this path and we have got the Goal State, then we will never explore the other
path. (this is what AO* says but here we are going to explore another path as well to see what happen)
Let's Explore the other path:
1. In the above diagram we have two ways from A to D or A to B-C (because of and condition). calculate
cost to select a path
2. F(A-D) = 1+10 = 11
and
F(A-BC) = 1 + 1 + 6 +12 = 20
3. As we know the cost is more of F(A-BC) but let's take a look
4. Now from B we have two path G and H , let's calculate the cost
5. F(B-G) = 5+1 =6 and F(B-H)= 7 + 1 = 8
6. So, cost from F(B-H) is more than F(B-G) we will take the path B-G.
7. The Heuristic value from G to I is 1 but let's calculate the cost form G to I.
8. F(G-I) = 1 +1 = 2. which is less than Heuristic value 5. So, the new Heuristic value form G to I is
2.
9. If it is a new value, then the cost from G to B must also have changed. Let's see the new cost form (B
to G)
10. F(B-G) = 1+2 =3. Mean the New Heuristic value of B is 3.
11. But A is associated with both B and C.
12. As we can see from the diagram C only have one choice or one node to explore that is J. The
Heuristic value of C is 12.
13. Cost form C to J= F(C-J) = 1+1= 2 Which is less than Heuristic value
14. Now the New Heuristic value of C is 2.
15. And the New Cost from A- BC that is F(A-BC) = 1+1+2+3 = 7 which is less than F(A-D)=11.
16. In this case Choosing path A-BC is more cost effective and good than that of A-D.
But this will only happen when the algorithm explores this path as well. But according to the algorithm,
algorithm will not accelerate this path (here we have just did it to see how the other path can also be
correct).
But it is not the case in all the cases that it will happen in some cases that the algorithm will get optimal
solution.
Advantages:
 It is an optimal algorithm.
 If traverse according to the ordering of nodes. It can be used for both OR and AND graph.
Disadvantages:
 Sometimes for unsolvable nodes, it can’t find the optimal path. Its complexity is than other algorithms.
Mini-Max Algorithm
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Mini-max algorithm is a recursive or backtracking algorithm which is used in decision-making and
game theory. It provides an optimal move for the player assuming that opponent is also playing
optimally.
Mini-Max algorithm uses recursion to search through the game-tree.
Min-Max algorithm is mostly used for game playing in AI. Such as Chess, Checkers, tic-tac-toe, go,
and various tow-players game. This Algorithm computes the minimax decision for the current state.
In this algorithm two players play the game, one is called MAX and other is called MIN.
Both the players fight it as the opponent player gets the minimum benefit while they get the maximum
benefit.
Both Players of the game are opponent of each other, where MAX will select the maximized value and
MIN will select the minimized value.
The minimax algorithm performs a depth-first search algorithm for the exploration of the complete
game tree.
The minimax algorithm proceeds all the way down to the terminal node of the tree, then backtrack the
tree as the recursion.
Pseudo-code for MinMax Algorithm:
function minimax(node, depth, maximizingPlayer) is
if depth ==0 or node is a terminal node then
return static evaluation of node
if MaximizingPlayer then
// for Maximizer Player
maxEva= -infinity
for each child of node do
eva= minimax(child, depth-1, false)
maxEva= max(maxEva,eva)
//gives Maximum of the values
return maxEva
else
// for Minimizer player
minEva= +infinity
for each child of node do
eva= minimax(child, depth-1, true)
minEva= min(minEva, eva)
return minEva
//gives minimum of the values
Initial call:
Minimax(node, 3, true)
Working of Min-Max Algorithm:
 The working of the minimax algorithm can be easily described using an example. Below we have
taken an example of game-tree which is representing the two-player game.
 In this example, there are two players one is called Maximizer and other is called Minimizer.
 Maximizer will try to get the Maximum possible score, and Minimizer will try to get the minimum
possible score.
 This algorithm applies DFS, so in this game-tree, we have to go all the way through the leaves to reach
the terminal nodes.
 At the terminal node, the terminal values are given so we will compare those value and backtrack the
tree until the initial state occurs. Following are the main steps involved in solving the two-player game
tree:
Step-1: In the first step, the algorithm generates the entire game-tree and apply the utility function to get the
utility values for the terminal states. In the below tree diagram, let's take A is the initial state of the tree.
Suppose maximizer takes first turn which has worst-case initial value =- infinity, and minimizer will take next
turn which has worst-case initial value = +infinity.
Step 2: Now, first we find the utilities value for the Maximizer, its initial value is -∞, so we will compare each
value in terminal state with initial value of Maximizer and determines the higher nodes values. It will find the
maximum among the all.
max(-1,- -∞) => max(-1,4)= 4
 For node D
max(2, -∞) => max(2, 6)= 6
 For Node E
max(-3, -∞) => max(-3,-5) = -3
 For Node F
max(0, -∞) = max(0, 7) = 7
 For node G
Step 3: In the next step, it's a turn for minimizer, so it will compare all nodes value with +∞, and will find the
3rd layer node values.
 For node B= min(4,6) = 4
 For node C= min (-3, 7) = -3
Step 4: Now it's a turn for Maximizer, and it will again choose the maximum of all nodes value and find the
maximum value for the root node. In this game tree, there are only 4 layers, hence we reach immediately to
the root node, but in real games, there will be more than 4 layers.
 For node A max(4, -3)= 4
That was the complete workflow of the minimax two player game.
Properties of Mini-Max algorithm:
 Complete- Min-Max algorithm is Complete. It will definitely find a solution (if exist), in the finite
search tree.
 Optimal- Min-Max algorithm is optimal if both opponents are playing optimally.
 Time complexity- As it performs DFS for the game-tree, so the time complexity of Min-Max
algorithm is O(bm), where b is branching factor of the game-tree, and m is the maximum depth of the
tree.
 Space Complexity- Space complexity of Mini-max algorithm is also similar to DFS which is O(bm).
Limitation of the minimax Algorithm:
The main drawback of the minimax algorithm is that it gets really slow for complex games such as Chess, go,
etc. This type of games has a huge branching factor, and the player has lots of choices to decide. This limitation
of the minimax algorithm can be improved from alpha-beta pruning which we have discussed in the next
topic.
Alpha-Beta Pruning
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Alpha-beta pruning is a modified version of the minimax algorithm. It is an optimization technique for
the minimax algorithm.
As we have seen in the minimax search algorithm that the number of game states it has to examine are
exponential in depth of the tree. Since we cannot eliminate the exponent, but we can cut it to half.
Hence there is a technique by which without checking each node of the game tree we can compute the
correct minimax decision, and this technique is called pruning. This involves two threshold parameter
Alpha and beta for future expansion, so it is called alpha-beta pruning. It is also called as AlphaBeta Algorithm.
Alpha-beta pruning can be applied at any depth of a tree, and sometimes it not only prune the tree
leaves but also entire sub-tree.
The two-parameter can be defined as:
1. Alpha: The best (highest-value) choice we have found so far at any point along the path of
Maximizer. The initial value of alpha is -∞.
2. Beta: The best (lowest-value) choice we have found so far at any point along the path of
Minimizer. The initial value of beta is +∞.
The Alpha-beta pruning to a standard minimax algorithm returns the same move as the standard
algorithm does, but it removes all the nodes which are not really affecting the final decision but making
algorithm slow. Hence by pruning these nodes, it makes the algorithm fast.
Condition for Alpha-beta pruning:
The main condition which required for alpha-beta pruning is:
1. α>=β
Key points about alpha-beta pruning:
 The Max player will only update the value of alpha.
 The Min player will only update the value of beta.
 While backtracking the tree, the node values will be passed to upper nodes instead of values of alpha
and beta.
 We will only pass the alpha, beta values to the child nodes.
Pseudo-code for Alpha-beta Pruning:
function minimax(node, depth, alpha, beta, maximizingPlayer) is
if depth ==0 or node is a terminal node then
return static evaluation of node
if MaximizingPlayer then
// for Maximizer Player
maxEva= -infinity
for each child of node do
eva= minimax(child, depth-1, alpha, beta, False)
maxEva= max(maxEva, eva)
alpha= max(alpha, maxEva)
if beta<=alpha
break
return maxEva
else
// for Minimizer player
minEva= +infinity
for each child of node do
eva= minimax(child, depth-1, alpha, beta, true)
minEva= min(minEva, eva)
beta= min(beta, eva)
if beta<=alpha
break
return minEva
Working of Alpha-Beta Pruning:
Let's take an example of two-player search tree to understand the working of Alpha-beta pruning
Step 1: At the first step the, Max player will start first move from node A where α= -∞ and β= +∞, these value
of alpha and beta passed down to node B where again α= -∞ and β= +∞, and Node B passes the same value
to its child D.
Step 2: At Node D, the value of α will be calculated as its turn for Max. The value of α is compared with
firstly 2 and then 3, and the max (2, 3) = 3 will be the value of α at node D and node value will also 3.
Step 3: Now algorithm backtrack to node B, where the value of β will change as this is a turn of Min, Now
β= +∞, will compare with the available subsequent nodes value, i.e. min (∞, 3) = 3, hence at node B now α=
-∞, and β= 3.
In the next step, algorithm traverse the next successor of Node B which is node E, and the values of α= -∞,
and β= 3 will also be passed.
Step 4: At node E, Max will take its turn, and the value of alpha will change. The current value of alpha will
be compared with 5, so max (-∞, 5) = 5, hence at node E α= 5 and β= 3, where α>=β, so the right successor
of E will be pruned, and algorithm will not traverse it, and the value at node E will be 5.
Step 5: At next step, algorithm again backtrack the tree, from node B to node A. At node A, the value of alpha
will be changed the maximum available value is 3 as max (-∞, 3)= 3, and β= +∞, these two values now passes
to right successor of A which is Node C.
At node C, α=3 and β= +∞, and the same values will be passed on to node F.
Step 6: At node F, again the value of α will be compared with left child which is 0, and max(3,0)= 3, and then
compared with right child which is 1, and max(3,1)= 3 still α remains 3, but the node value of F will become
1.
Step 7: Node F returns the node value 1 to node C, at C α= 3 and β= +∞, here the value of beta will be changed,
it will compare with 1 so min (∞, 1) = 1. Now at C, α=3 and β= 1, and again it satisfies the condition α>=β,
so the next child of C which is G will be pruned, and the algorithm will not compute the entire sub-tree G.
Step 8: C now returns the value of 1 to A here the best value for A is max (3, 1) = 3. Following is the final
game tree which is the showing the nodes which are computed and nodes which has never computed. Hence
the optimal value for the maximiser is 3 for this example.
Move Ordering in Alpha-Beta pruning:
The effectiveness of alpha-beta pruning is highly dependent on the order in which each node is examined.
Move order is an important aspect of alpha-beta pruning.
It can be of two types:
 Worst ordering: In some cases, alpha-beta pruning algorithm does not prune any of the leaves of the
tree, and works exactly as minimax algorithm. In this case, it also consumes more time because of
alpha-beta factors, such a move of pruning is called worst ordering. In this case, the best move occurs
on the right side of the tree. The time complexity for such an order is O(b m).
 Ideal ordering: The ideal ordering for alpha-beta pruning occurs when lots of pruning happens in the
tree, and best moves occur at the left side of the tree. We apply DFS hence it first search left of the tree
and go deep twice as minimax algorithm in the same amount of time. Complexity in ideal ordering is
O(bm/2).
Rules to find good ordering:
Following are some rules to find good ordering in alpha-beta pruning:
 Occur the best move from the shallowest node.
 Order the nodes in the tree such that the best nodes are checked first.
 Use domain knowledge while finding the best move. Ex: for Chess, try order: captures first, then
threats, then forward moves, backward moves.
 We can bookkeep the states, as there is a possibility that states may repeat.
UNIT II
Introduction to Expert Systems
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An expert system is a computer program that is designed to solve complex problems and to provide
decision-making ability like a human expert. It performs this by extracting knowledge from its
knowledge base using the reasoning and inference rules according to the user queries.
The performance of an expert system is based on the expert's knowledge stored in its knowledge base.
The more knowledge stored in the KB, the more that system improves its performance. One of the
common examples of an ES is a suggestion of spelling errors while typing in the Google search box.
Capabilities of the Expert System
Below are some capabilities of an Expert System:
 Advising: It is capable of advising the human being for the query of any domain from the particular
ES.
 Provide decision-making capabilities: It provides the capability of decision making in any domain,
such as for making any financial decision, decisions in medical science, etc.
 Demonstrate a device: It is capable of demonstrating any new products such as its features,
specifications, how to use that product, etc.
 Problem-solving: It has problem-solving capabilities.
 Explaining a problem: It is also capable of providing a detailed description of an input problem.
 Interpreting the input: It is capable of interpreting the input given by the user.
 Predicting results: It can be used for the prediction of a result.
 Diagnosis: An ES designed for the medical field is capable of diagnosing a disease without using
multiple components as it already contains various inbuilt medical tools.
Advantages of Expert System
 These systems are highly reproducible.
 They can be used for risky places where the human presence is not safe.
 Error possibilities are less if the KB contains correct knowledge.
 The performance of these systems remains steady as it is not affected by emotions, tension, or fatigue.
 They provide a very high speed to respond to a particular query.
Limitations of Expert System
 The response of the expert system may get wrong if the knowledge base contains the wrong
information.
 Like a human being, it cannot produce a creative output for different scenarios.
 Its maintenance and development costs are very high.
 Knowledge acquisition for designing is much difficult.
 For each domain, we require a specific ES, which is one of the big limitations.
 It cannot learn from itself and hence requires manual updates.
Applications of Expert System
 In designing and manufacturing domain - It can be broadly used for designing and manufacturing
physical devices such as camera lenses and automobiles.
 In the knowledge domain - These systems are primarily used for publishing the relevant knowledge
to the users. The two popular ES used for this domain is an advisor and a tax advisor.
 In the finance domain - In the finance industries, it is used to detect any type of possible fraud,
suspicious activity, and advise bankers that if they should provide loans for business or not.
 In the diagnosis and troubleshooting of devices - In medical diagnosis, the ES system is used, and
it was the first area where these systems were used.
 Planning and Scheduling - The expert systems can also be used for planning and scheduling some
particular tasks for achieving the goal of that task.
Architecture of Expert System
Below is the block diagram that represents the working of an expert system:
Components of Expert System
An expert system mainly consists of three components:
 User Interface
 Inference Engine
 Knowledge Base
1. User Interface
With the help of a user interface, the expert system interacts with the user, takes queries as an input in a
readable format, and passes it to the inference engine. After getting the response from the inference engine, it
displays the output to the user. In other words, it is an interface that helps a non-expert user to communicate
with the expert system to find a solution.
2. Inference Engine (Rules of Engine)
 The inference engine is known as the brain of the expert system as it is the main processing unit of the
system. It applies inference rules to the knowledge base to derive a conclusion or deduce new
information. It helps in deriving an error-free solution of queries asked by the user.
 With the help of an inference engine, the system extracts the knowledge from the knowledge base.
There are two types of inference engine:
 Deterministic Inference engine: The conclusions drawn from this type of inference engine are
assumed to be true. It is based on facts and rules.
 Probabilistic Inference engine: This type of inference engine contains uncertainty in conclusions,
and based on the probability.
Inference engine uses the below modes to derive the solutions:
 Forward Chaining: It starts from the known facts and rules, and applies the inference rules to add
their conclusion to the known facts.
 Backward Chaining: It is a backward reasoning method that starts from the goal and works backward
to prove the known facts.
3. Knowledge Base
 The knowledgebase is a type of storage that stores knowledge acquired from the different experts of
the particular domain. It is considered as big storage of knowledge. The more the knowledge base, the
more precise will be the Expert System.
 It is similar to a database that contains information and rules of a particular domain or subject.
 One can also view the knowledge base as collections of objects and their attributes. Such as a Lion is
an object and its attributes are it is a mammal, it is not a domestic animal, etc.
Components of Knowledge Base:
 Factual Knowledge: The knowledge which is based on facts and accepted by knowledge engineers
comes under factual knowledge.
 Heuristic Knowledge: This knowledge is based on practice, the ability to guess, evaluation, and
experiences.
Representation and organization of knowledge
An Artificial intelligence system has the following components for displaying intelligent behavior:
 Perception
 Learning
 Knowledge Representation and Reasoning
 Planning
 Execution
The above diagram is showing how an AI system can interact with the real world and what components help
it to show intelligence. AI system has Perception component by which it retrieves information from its
environment. It can be visual, audio or another form of sensory input. The learning component is responsible
for learning from data captured by Perception comportment. In the complete cycle, the main components are
knowledge representation and Reasoning. These two components are involved in showing the intelligence in
machine-like humans. These two components are independent with each other but also coupled together. The
planning and execution depend on analysis of Knowledge representation and reasoning.
Approaches to knowledge representation:
There are mainly four approaches to knowledge representation, which are given below:
1. Simple relational knowledge:
 It is the simplest way of storing facts which uses the relational method, and each fact about a set of the
object is set out systematically in columns.
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This approach of knowledge representation is famous in database systems where the relationship
between different entities is represented.
This approach has little opportunity for inference.
Example: The following is the simple relational knowledge representation.
Player Weight Age
Player1 65
23
Player2 58
18
Player3 75
24
2. Inheritable knowledge:
 In the inheritable knowledge approach, all data must be stored into a hierarchy of classes.
 All classes should be arranged in a generalized form or a hierarchal manner.
 In this approach, we apply inheritance property.
 Elements inherit values from other members of a class.
 This approach contains inheritable knowledge which shows a relation between instance and class, and
it is called instance relation.
 Every individual frame can represent the collection of attributes and its value.
 In this approach, objects and values are represented in Boxed nodes.
 We use Arrows which point from objects to their values.
 Example:
3. Inferential knowledge:
 Inferential knowledge approach represents knowledge in the form of formal logics.
 This approach can be used to derive more facts.
 It guaranteed correctness.
 Example: Let's suppose there are two statements:
1. Marcus is a man
2. All men are mortal
Then it can represent as;
man(Marcus)
∀x = man (x) ----------> mortal (x)s
4. Procedural knowledge:
 Procedural knowledge approach uses small programs and codes which describes how to do specific
things, and how to proceed.
 In this approach, one important rule is used which is If-Then rule.
 In this knowledge, we can use various coding languages such as LISP language and Prolog language.
 We can easily represent heuristic or domain-specific knowledge using this approach.
 But it is not necessary that we can represent all cases in this approach.
Requirements for knowledge Representation system:
A good knowledge representation system must possess the following properties.
1. Representational Accuracy: KR system should have the ability to represent all kind of required
knowledge.
2. Inferential Adequacy: KR system should have ability to manipulate the representational structures to
produce new knowledge corresponding to existing structure.
3. Inferential Efficiency: The ability to direct the inferential knowledge mechanism into the most
productive directions by storing appropriate guides.
4. Acquisitional efficiency- The ability to acquire the new knowledge easily using automatic methods.
Characteristics of Expert System
An expert system is usually designed to have the following general characteristics.
 High level Performance: The system must be capable of responding at a level of competency equal
to or better than an expert system in the field. The quality of the advice given by the system should be
in a high-level integrity and for which the performance ratio should be also very high.
 Domain Specificity: Expert systems are typically very domain specific. For ex., a diagnostic expert
system for troubleshooting computers must actually perform all the necessary data manipulation as a
human expert would. The developer of such a system must limit his or her scope of the system to just
what is needed to solve the target problem. Special tools or programming languages are often needed
to accomplish the specific objectives of the system.
 Good Reliability: The expert system must be as reliable as a human expert.
 Understandable: The system should be understandable i.e. be able to explain the steps of reasoning
while executing. The expert system should have an explanation capability similar to the reasoning
ability of human experts.
 Adequate Response time: The system should be designed in such a way that it is able to perform
within a small amount of time, comparable to or better than the time taken by a human expert to reach
at a decision point. An expert system that takes a year to reach a decision compared to a human expert’s
time of one hour would not be useful.
 Use symbolic representations: Expert system use symbolic representations for knowledge (rules,
networks or frames) and perform their inference through symbolic computations that closely resemble
manipulations of natural language.
 Linked with Metaknowledge: Expert systems often reason with metaknowledge i.e. they reason with
knowledge about themselves and their own knowledge limits and capabilities. The use of
metaknowledge is quite interactive and simple for various data representations.
 Expertise knowledge: Real experts not only produce good solutions but also find them quickly. So,
an expert system must be skilful in applying its knowledge to produce solutions both efficiently and
effectively by using the intelligence human experts.
 Justified Reasoning: This allows the users to ask the expert system to justify the solution or advice
provided by it. Normally, expert systems justify their answers or advice by explaining their reasoning.
If a system is a rule-based system, it provides to the user all the rules and facts it has used to achieve
its answer.
 Explaining capability: Expert systems are capable of explaining how a particular conclusion was
reached and why requested information is needed during a consultation. This is very important as it
gives the user a chance to access and understand the system’s reasoning ability, thereby improving the
user’s confidence in the system.
 Special Programming Languages: Expert systems are typically written in special programming
languages. The use of languages like LISP and PROLOG in the development of an expert system
simplifies the coding process. The major advantage of these languages, as compared to conventional
programming languages is the simplicity of the addition, elimination or substitution of new rules and
memory management capabilities.
Some of the distinguishing characteristics of programming languages needed for expert system work
are as follows:





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
Efficient mix of integer and real variables.
Good memory management procedures.
Extensive data manipulation routines.
Incremental compilation.
Tagged memory architecture.
Efficient search procedures.
Optimization of the systems environment.
Types of problems handled by Expert system
An ES is no substitute for a knowledge worker's overall performance of the problem-solving task. But these
systems can dramatically reduce the amount of work the individual must do to solve a problem, and they do
leave people with the creative and innovative aspects of problem solving. Below are some capabilities of an
Expert System:
 Advising: It is capable of advising the human being for the query of any domain from the particular
ES.
 Provide decision-making capabilities: It provides the capability of decision making in any domain,
such as for making any financial decision, decisions in medical science, etc.
 Demonstrate a device: It is capable of demonstrating any new products such as its features,
specifications, how to use that product, etc.
 Problem-solving: It has problem-solving capabilities.
 Explaining a problem: It is also capable of providing a detailed description of an input problem.
 Interpreting the input: It is capable of interpreting the input given by the user.
 Predicting results: It can be used for the prediction of a result.
 Diagnosis: An ES designed for the medical field is capable of diagnosing a disease without using
multiple components as it already contains various inbuilt medical tools.
 An Es can complete its part of the tasks much faster than a human expert.
 The error rate of successful systems is low, sometimes much lower than the human error rate for the
same task.
 ESs make consistent recommendations
 ESs are a convenient vehicle for bringing to the point of application difficult-to-use sources of
knowledge.
 ESs can capture the scarce expertise of a uniquely qualified expert.
 ESs can become a vehicle for building up organizational knowledge, as opposed to the knowledge of
individuals in the organization.
 When use as training vehicles, ESs result in a faster learning curve for novices.
 The company can operate an ES in environments hazardous for humans.
Expert System Tools
Techniques of Knowledge representation
There are mainly four ways of knowledge representation which are given as follows:
1. Logical Representation
2. Semantic Network Representation
3. Frame Representation
4. Production Rules
1. Logical Representation
Logical representation is a language with some concrete rules which deals with propositions and has no
ambiguity in representation. Logical representation means drawing a conclusion based on various conditions.
This representation lays down some important communication rules. It consists of precisely defined syntax
and semantics which supports the sound inference. Each sentence can be translated into logics using syntax
and semantics.
Syntax:
 Syntaxes are the rules which decide how we can construct legal sentences in the logic.
 It determines which symbol we can use in knowledge representation.
 How to write those symbols.
Semantics:
 Semantics are the rules by which we can interpret the sentence in the logic.
 Semantic also involves assigning a meaning to each sentence.
Logical representation can be categorised into mainly two logics:
1. Propositional Logics
2. Predicate logics
Advantages of logical representation:
1. Logical representation enables us to do logical reasoning.
2. Logical representation is the basis for the programming languages.
Disadvantages of logical Representation:
1. Logical representations have some restrictions and are challenging to work with.
2. Logical representation technique may not be very natural, and inference may not be so efficient.
2. Semantic Network Representation
Semantic networks are alternative of predicate logic for knowledge representation. In Semantic networks, we
can represent our knowledge in the form of graphical networks. This network consists of nodes representing
objects and arcs which describe the relationship between those objects. Semantic networks can categorize the
object in different forms and can also link those objects. Semantic networks are easy to understand and can be
easily extended.
This representation consist of mainly two types of relations:
1. IS-A relation (Inheritance)
2. Kind-of-relation
Example: Following are some statements which we need to represent in the form of nodes and arcs.
Statements:
1. Jerry is a cat.
2. Jerry is a mammal
3. Jerry is owned by Priya.
4. Jerry is brown coloured.
5. All Mammals are animal.
In the above diagram, we have represented the different type of knowledge in the form of nodes and arcs.
Each object is connected with another object by some relation.
Drawbacks in Semantic representation:
1. Semantic networks take more computational time at runtime as we need to traverse the complete
network tree to answer some questions. It might be possible in the worst case scenario that after
traversing the entire tree, we find that the solution does not exist in this network.
2. Semantic networks try to model human-like memory (Which has 1015 neurons and links) to store the
information, but in practice, it is not possible to build such a vast semantic network.
3. These types of representations are inadequate as they do not have any equivalent quantifier, e.g., for
all, for some, none, etc.
4. Semantic networks do not have any standard definition for the link names.
5. These networks are not intelligent and depend on the creator of the system.
Advantages of Semantic network:
1. Semantic networks are a natural representation of knowledge.
2. Semantic networks convey meaning in a transparent manner.
3. These networks are simple and easily understandable.
3. Frame Representation
A frame is a record like structure which consists of a collection of attributes and its values to describe an entity
in the world. Frames are the AI data structure which divides knowledge into substructures by representing
stereotypes situations. It consists of a collection of slots and slot values. These slots may be of any type and
sizes. Slots have names and values which are called facets.
Facets: The various aspects of a slot is known as Facets. Facets are features of frames which enable us to put
constraints on the frames. Example: IF-NEEDED facts are called when data of any particular slot is needed.
A frame may consist of any number of slots, and a slot may include any number of facets and facets may have
any number of values. A frame is also known as slot-filter knowledge representation in artificial
intelligence.
Frames are derived from semantic networks and later evolved into our modern-day classes and objects. A
single frame is not much useful. Frames system consist of a collection of frames which are connected. In the
frame, knowledge about an object or event can be stored together in the knowledge base. The frame is a type
of technology which is widely used in various applications including Natural language processing and
machine visions.
Example: 1
Let's take an example of a frame for a book
Slots
Title
Filters
Artificial Intelligence
Genre
Author
Edition
Year
Page
Computer Science
Peter Norvig
Third Edition
1996
1152
Advantages of frame representation:
1. The frame knowledge representation makes the programming easier by grouping the related data.
2. The frame representation is comparably flexible and used by many applications in AI.
3. It is very easy to add slots for new attribute and relations.
4. It is easy to include default data and to search for missing values.
5. Frame representation is easy to understand and visualize.
Disadvantages of frame representation:
1. In frame system inference mechanism is not be easily processed.
2. Inference mechanism cannot be smoothly proceeded by frame representation.
3. Frame representation has a much generalized approach.
4. Production Rules
Production rules system consist of (condition, action) pairs which mean, "If condition then action". It has
mainly three parts:
 The set of production rules
 Working Memory
 The recognize-act-cycle
In production rules agent checks for the condition and if the condition exists then production rule fires and
corresponding action is carried out. The condition part of the rule determines which rule may be applied to a
problem. And the action part carries out the associated problem-solving steps. This complete process is called
a recognize-act cycle.
The working memory contains the description of the current state of problems-solving and rule can write
knowledge to the working memory. This knowledge match and may fire other rules.
If there is a new situation (state) generates, then multiple production rules will be fired together, this is called
conflict set. In this situation, the agent needs to select a rule from these sets, and it is called a conflict resolution.
Example:
 IF (at bus stop AND bus arrives) THEN action (get into the bus)
 IF (on the bus AND paid AND empty seat) THEN action (sit down).
 IF (on bus AND unpaid) THEN action (pay charges).
 IF (bus arrives at destination) THEN action (get down from the bus).
Advantages of Production rule:
1. The production rules are expressed in natural language.
2. The production rules are highly modular, so we can easily remove, add or modify an individual rule.
Disadvantages of Production rule:
1. Production rule system does not exhibit any learning capabilities, as it does not store the result of the
problem for the future uses.
2. During the execution of the program, many rules may be active hence rule-based production systems
are inefficient.
Knowledge Engineering










Knowledge engineering is a field of artificial intelligence (AI) that tries to emulate the judgment and
behaviour of a human expert in a given field.
It looks at the metadata (information about a data object that describes characteristics such as content,
quality, and format), structure and processes that are the basis of how a decision is made or conclusion
reached.
Knowledge engineering is the technology behind the creation of expert systems to assist with issues
related to their programmed field of knowledge.
Expert systems involve a large and expandable knowledge base integrated with a rules engine that
specifies how to apply information in the knowledge base to each particular situation. The systems
may also incorporate machine learning so that they can learn from experience in the same way that
humans do.
Expert systems are used in various fields including healthcare, customer service, financial services,
manufacturing and the law.
Using algorithms to emulate the thought patterns of a subject matter expert, knowledge engineering
tries to take on questions and issues as a human expert would. Looking at the structure of a task or
decision, knowledge engineering studies how the conclusion is reached.
A library of problem-solving methods and a body of collateral knowledge are used to approach the
issue or question. The amount of collateral knowledge can be very large. Depending on the task and
the knowledge that is drawn on, the virtual expert may assist with troubleshooting, solving issues,
assisting a human or acting as a virtual agent.
Scientists originally attempted knowledge engineering by trying to emulate real experts. Using the
virtual expert was supposed to get you the same answer as you would get from a human expert. This
approach was called the transfer approach. However, the expertise that a specialist required to answer
questions or respond to issues posed to it needed too much collateral knowledge: information that is
not central to the given issue but still applied to make judgments.
A surprising amount of collateral knowledge is required to enable analogous reasoning and nonlinear
thought. Currently, a modelling approach is used where the same knowledge and process need not
necessarily be used to reach the same conclusion for a given question or issue. Eventually, it is
expected that knowledge engineering will produce a specialist that surpasses the abilities of its human
counterparts.
Knowledge engineering attempts to take on challenges and solve problems that would usually require
a high level of human expertise to solve. Figure 1 illustrates the knowledge engineering pipeline.
Knowledge Engineering Processes
The knowledge engineering process includes:
 Knowledge acquisition
 Knowledge representation
 Knowledge validation
 Inferencing
 Explanation and justification
The interaction between these stages and sources of knowledge is shown in Figure 2.
Figure 2: Knowledge engineering processes
The amount of collateral knowledge can be very large depending on the task. A number of advances in
technology and technology standards have assisted in integrating data and making it accessible. These include
the semantic web (an extension of the current web in which information is given a well-defined meaning),
cloud computing (enables access to large amounts of computational resources), and open datasets (freely
available datasets for anyone to use and republish). These advances are crucial to knowledge engineering as
they expedite data integration and evaluation.
System – building aids
The system-building aids consist of programs which help acquire and represent the domain expert’s
knowledge and programs which help design the expert system under construction. These programs address
very difficult tasks; many are research tools just beginning to evolve into practical and useful aids, although a
few are offered as full-blown commercial systems.
Compared with programming and knowledge engineering languages, relatively few system-building aids have
been developed. Those which exist fall into two major categories; design aids and knowledge acquisition aids.
The AGE system exemplifies design aids, while TEIRSIAS, MOLE and SALT exemplify knowledge
acquisition, TIMM system construction and SEEK knowledge refinement aids.
A few of these are discussed below:
a. AGE: This software tool helps the knowledge engineer design and build an expert system. AGE
provides the user with a set of components which, like building blocks, can be assembled to form
portions of an expert system. Each component, a collection of INTERLISP functions, supports an expert
system framework, such as forward chaining, backward chaining, or a blackboard architecture.
The term blackboard refers to a central data base used by systems, to coordinate and, control the
operation of independent groups of rules called knowledge sources. The knowledge sources
communicate by writing messages on the blackboard and reading messages from other knowledge
sources.
b. MOLE: This is a knowledge acquisition system for heuristic classification problem, such as diagnosing
diseases. In particular, it is used in conjunction with the cover-and- differentiate problem solving
method. The expert-system-produced by MOLE accepts input data, comes up with a set of candidate
explanations or classifications which cover the data, then uses differentiating knowledge to determine
which one is best. The process is iterative, since explanations must themselves be justified, until
ultimate causes are ascertained.
MOLE interacts with a domain expert to produce a knowledge base which a system, called MOLE-p
(for mole performance) uses to solve problems.
c. TEIRESIAS: TEIRESIAS was developed by Davis in the mid-1970s as a vehicle for exploring new
ideas in knowledge acquisition and data base maintenance rather than as a tool for building expert
systems, at the university of Stanford (USA).
TEIRESIAS acquires knowledge interactively from an expert. If a wrong diagnosis has been made by
MYCIN, then TEIRESIAS will lead the expert back through the chain of incorrect reasoning until the
expert states where the incorrect reasoning started. While going back through the reasoning chain,
TEIRESIAS will also interact with the expert to modify incorrect rules or acquire new rules.
Support facilities
It includes a number of tools for aiding the programming. These tools generally should be considered in the
knowledge engineering language. There are four major kinds of support facilities as follows:




I/O facilities
Explanation facilities
Knowledge-base creation
Debugging tools
Hardware support – refers to the computer on which the tools run. Naturally, the size of a tool plays major
role in choosing the appropriate machine. A very vital aspect of selecting the relevant machine is its cost. The
type of computers that a tool can be run on are for example: PC Workstations, Mainframes and others.
Capabilities of expert system building tools vary from one tool to another. Therefore, it is very job to select
an appropriate tool. There is no agreement about how one chooses a shell to use for a given application. The
successful use of expert systems building tool lies in choosing the right tool for a problem. The field also
needs further research. It requires a practical search of different tools in use and analysis of both tools
characteristics and problem domain features.
Stages in the development of Expert System
The following points highlight the five main stages to develop an expert system. The stages are:
1. Identification
2. Conceptualisation
3. Formalisation (Designing)
4. Implementation
5. Testing (Validation, Verification and Maintenance).
Stage # 1. Identification:
Before we can begin to develop an expert system, it is important to describe, with as much precision as
possible, the problem which the system is intended to solve. It is not enough simply to feel that an expert
system would be helpful in a certain situation; we must determine the exact nature of the problem and state
the precise goals which indicate exactly how the expert system is expected to contribute to the solution.
Stage # 2. Conceptualisation:
Once it has been identified for the problem an expert system is to solve, the next stage involves analysing the
problem further to ensure that its specifics, as well as generalities, are understood.
In the conceptualisation stage, the knowledge engineer frequently creates a diagram of the problem to depict
graphically the relationships between the objects and processes in the problem domain. It is often helpful at
this stage to divide the problem into a series of sub-problems and to diagram both the relationships among the
pieces of each sub-problem and the relationships among the various sub-problems.
Stage # 3. Formalisation (Designing):
In the preceding stages, no effort has been made to relate the domain problem to the artificial intelligence
technology which may solve it. During the identification and formalization stages, the focus is entirely on
understanding the problem. Now, during the formalization stage, the problem is connected to its proposed
solution, an expert system is supplied by analysing the relationships depicted in the conceptualization stage.
The knowledge engineer begins to select the techniques which are appropriate for developing this particular
expert system.
Stage # 4. Implementation:
During the implementation stage the formalised concepts are programmed into the computer which has been
chosen for system development, using the predetermined techniques and tools to implement a ‘first-pass’
(prototype) of the expert system.
Theoretically, if the methods of the previous stages have been followed with diligence and care, the
implementation of the prototype should proceed smoothly.
Stage # 5. Testing (Validation, Verification and Maintenance):
The chance of prototype expert system executing flawlessly the first time it is tested are so slim as to be
virtually non-existent. A knowledge engineer does not expect the testing process to verify that the system has
been constructed entirely correctly. Rather, testing provides an opportunity to identify the weaknesses in the
structure and implementation of the system and to make the appropriate corrections.
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