Knowledge-based
systems
Rozália Lakner
University of Veszprém
Department of Computer Science
An overview
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Knowledge-based systems, expert systems
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structure, characteristics
main components
advantages, disadvantages
Base techniques of knowledge-based systems
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rule-based techniques
inductive techniques
hybrid techniques
symbol-manipulation techniques
case-based techniques
(qualitative techniques, model-based techniques, temporal
reasoning techniques, neural networks)
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Knowledge-based systems
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Structure and characteristics 1
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KBSs are computer systems
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KBSs are AI programs with program structure of new type
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contain stored knowledge
solve problems like humans would
knowledge-base (rules, facts, meta-knowledge)
inference engine (reasoning and search strategy for solution, other
services)
characteristics of KBSs:
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intelligent information processing systems
representation of domain of interest  symbolic representation
problem solving  by symbol-manipulation
 symbolic programs
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Structure and characteristics 2
Explanation
subsystem
User
Case specific
database
User
interface
Inference
engine
Knowledge
base
Knowledge
engineer
Developer's
interface
Knowledge
acquisition
subsystem
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Main components 1
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knowledge-base (KB)
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inference engine
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knowledge about the field of interest (in natural language-like formalism)
symbolically described system-specification
KNOWLEDGE-REPRESENTATION METHOD!
„engine” of problem solving (general problem solving knowledge)
supporting the operation of the other components
PROBLEM SOLVING METHOD!
case-specific database
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auxiliary component
specific information (information from outside, initial data of the concrete
problem)
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information obtained during reasoning
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Main components 2
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explanation subsystem
explanation of system’ actions in case of user’ request
typical explanation facilities:
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explanation during problem solving:
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WHY... (explanative reasoning, intelligent help, tracing information about
the actual reasoning steps)
WHAT IF... (hypothetical reasoning, conditional assignment and its
consequences, can be withdrawn)
WHAT IS ... (gleaning in knowledge-base and case-specific database)
explanation after problem solving:
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HOW ... (explanative reasoning, information about the way the result has
been found)
WHY NOT ... (explanative reasoning, finding counter-examples)
WHAT IS ... (gleaning in knowledge-base and case-specific database)
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Main components 3
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knowledge acquisition subsystem
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main tasks:
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user interface ( user)
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dialogue on natural language (consultation/ suggestion)
specially intefaces
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checking the syntax of knowledge elements
checking the consistency of KB (verification, validation)
knowledge extraction, building KB
automatic logging and book-keeping of the changes of KB
tracing facilities (handling breakpoints, automatic monitoring and reporting
the values of knowledge elements)
database and other connections
developer interface ( knowledge engineer, human expert)
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Main components 4
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the main tasks of the knowledge engineer:
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knowledge acquisition and design of KBS: determination,
classification, refinement and formalization of methods, thumb-rules
and procedures
selection of knowledge representation method and reasoning
strategy
implementation of knowledge-based system
verification and validation of KB
KB maintenance
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Expert Systems
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Structure and characteristics 1
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expert systems  knowledge-based systems
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employ expert’ knowledge
applied in a narrow specific field
solve difficult problems (must be demand on special knowledge)
specialized human experts are needed
experts must be agreed on the fundamental questions of
professional field
learning examples and raw data are needed
expectations from an ES (like a human expert):
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make intelligent decision: offer intelligent advice and explanations
question/ answer (“treated as an equal conversation partner”)
explanation of questions
acceptable advice even in case of uncertain situation
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Structure and characteristics 2
AI programs
Knowledge-based systems
Expert systems
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AI programs:
intelligent problem solving tools
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KBSs
AI programs with special program
structure separated knowledge base
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ESs
KBSs applied in a specific narrow field
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Expert system shells 1
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„empty” ESs, contain all the active elements of an ES
empty KB, powerful knowledge acquicition subsystem
contain services for construction and operation of ES
independently of the field of interest
support the development of rapid prototype and the
incremental construction
examples: CLIPS, GoldWorks, G2, Level5
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Expert system shells 2
Explanation
subsystem
User
Case specific
database
User
interface
Inference
engine
Knowledge
base
Knowledge
engineer
Developer's
interface
Knowledge
acquisition
subsystem
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Advantages of KBSs and ESs
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make up for shortage of experts, spread expert’
knowledge on available price (TROPICAID)
field of interest’ changes are well-tracked (R1)
increase expert’ ability and efficiency
preserve know-how
can be developed systems unrealizabled with tradicional
technology (Buck Rogers)
self-consistents in advising, equable in performance
are available permanently
able to work even with partial, non-complete data
able to give expanation
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Disadvantages of KBSs and ESs
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their knowledge is from a narrow field, don’t know the
limits
the answers are not always correct (advices have to be
analysed!)
don’t have common sence (greatest restriction)  all of
the self-evident checking have to be defined
(many exceptions  increase the size of KB and the
running time)
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Base techniques of KBSs
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Techniques of KBSs
based on the knowledge-representation methods and
reasoning strategies applied in the implementation
 rule-based techniques
 inductive techniques
 hybrid techniques
 symbol-manipulation techniques
 case-based techniques
 (qualitative techniques, model-based techniques, temporal
reasoning techniques, neural networks)
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Rule-based techniques
(a short review)
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Reasoning with rules 1
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knowledge-representation form: rule
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rule-base can be according to the structure of KB
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simple/unstructured
structured (contexts)
reasoning strategies:
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according to the control direction
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data-driven/forward chaining
goal-driven/backward chaining
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Reasoning with rules 2
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aim: proving a goal statement or achieving a goal state
the reasoning algorithm:
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pattern matching
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conflict resolution
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selecting the most appropriate rule from conflict set
conflict resolution strategies
firing
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finding applicable rules (watching condition/conclusion part of rules)
fireable rules  conflict set (match condition/conclusion part of rules)
executing the selected rule  new knowledge (new facts or new
subgoals to be proved)
watching termination conditions
restart of the cycle
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Inductive techniques
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Inductive reasoning
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a type of machine learning technics
inferring from individual cases to general information
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given a collection of training examples (x, f(x))
return a function h that approximates f
h is called hypothese
f(x)
x
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h y p o t h e s e s
aim: finding the hypothese fits well on the training examples
h is used for prediction the values of the unseen examples
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Decision tree 1
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one of the most known methods of inductive learning:
learning decision trees
decision tree: simple representation for classifying
examples
elements of the decision tree:
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nonleaf (internal) nodes are labelled with attributes (A)
arcs out of a node are labelled with possible attribute values of A
leaf nodes are labelled with classifications (Boolean values –
yes/no - in the simplest case)
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Decision tree 2
Country
Age Engine Colour Easy to sell
1. Germany 3-6
2. Japan
3. Japan
diesel
6-10 diesel
3-6 diesel
white
yes
red
blue
yes
We want to classify new examples on
property Easy to sell based on the
examples’ Country, Age, Engine and
Colour.
no
Country
Germany
Japan
yes
Colour
red
yes
blue
no
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Decision tree 3
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a decision tree under construction contains:
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nodes labelled with attributes
nodes labelled with classifications (yes/no values)
unlabelled nodes
arcs labelled with attribute values outlet only form nodes labelled
with attributes
every unlabelled nodes possess:
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a subset of training examples
eligible attributes
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Decision tree 4
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some questions about decision tree:
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Given some data (set of training examples and attributes), which
decision tree should be generated?
A decision tree can represent any discrete function of the inputs.
Which trees are the best predictors of unseen data?
You need a bias (preference for one hypothesis over another).
Example, prefer the smallest tree.
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Least depth?
Fewest nodes?
How should you go about building a decision tree? The space of
decision trees is too big for systematic search for the smallest
decision tree.
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Learning decision trees 1
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learning decision tree  ID3 algorithm:
1. initially decision tree contains an unlabelled node with all of the
training examples and attributes
2. selecting an unlabelled node (n) with non-empty set of training
examples (T) and non-empty set of attributes (A)
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if T is homogen class  n leaf node, label with the classification
otherwise
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choosing the „best” attribute (B) from A
extension of the tree with all of the possible attribute values of B (devide into
subclasses)
classification of T to the children nodes according to the attribute values
(assign the elements of T to subclasses)
continue with step 2.
building the tree top-down
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Learning decision trees 2
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how to choose the „best” attribute?
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attribute divides the examples into homogen classes
otherwise attribute makes the most progress towards this
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hill-climbing search on the space of decision trees
searching for the smallest tree  heuristics (maximum information gain)
information gain of an attribute test
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measures the difference between the original information
requirement and the new requirement (after the attribute test)
information gain (G) it is based on information contents (entropy, E)
n
G ( S , A)  E ( S )  
i 1
where:
Si
S
E ( Si )
S: set of classified examples, A: attribute
S1, … , Sn: subsets of S according to A
S
S S
S
E: entropy
E (S )  
log 2

log 2
S
S
S
S
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Learning decision trees 3
Author
Thread
Length Reads
1
known
new
short
true
2
unknown
new
long
true
3
unknown
old
short
false
4
known
old
short
true
5
known
new
long
true
6
known
old
long
true
7
unknown
old
long
false
8
unknown
new
long
true
9
known
new
short
true
10 unknown
old
long
false
11
new
short
true
12 known
old
long
true
13 known
new
long
true
known
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Using decision trees 1
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major problem with using decision tree: overfitting
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occurs when there is a distinction in the tree that appears in the
training examples, but it doesn’t appear in the unseen examples
handling overfitting:
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restricting the splitting, so that you split only when the split is
useful
allowing unrestricted splitting and pruning the resulting tree
where it makes unwarranted distinctions:
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examples are devided into two sets: training set and test set
constructing a decision tree with the training set
examining all of the nodes with the test set: whether the subtree
under the node is replaceable with a leaf node
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Using decision trees 2
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supporting knowledge acquisition/ fast prototype-making
(rule-based/ hybrid systems with inductive services)
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each one row in the matrix of training examples is a rule
Author
Thread
Length Reads
1
known
new
short
true
2
unknown new
long
true
…
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IF (Author = known) and (Thread = new) and (Length = short)
THEN (Reads = true)
IF (Author = unknown) and (Thread = new) and (Length = long)
THEN (Reads = true)
…
better: each one path (root  leaf) on the decision tree is a rule
IF (Author = known)
THEN (Reads = true)
IF (Author = unknown) and (Thread = new)
THEN (Reads = true)
IF (Author = unknown) and (Thread = old)
THEN (Reads = false)
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Main components of inductive
systems
Knowledge representation:
The matrix of training examples:
attributes, values
Reasoning and control:
Algorithm, which constructs a
decision tree using the matrix of
training examples and operates
the generated system.
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Main steps of inductive systems
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problem definition (knowledge representation):
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reasoning (generating a hypothese)
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attributes (head of the matrix, generate coloumns, define object
classes)
training examples (fill the raws of the matrix, define instances)
checking the contradiction freeness of the training examples
learning optimal decision tree (DT)  knowledge base
control (operating the system)
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classification of user’ (unknown) examples (traversing DT)
analysis of user’ examples (with the help of DT)
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Hybrid techniques
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Characteristics of hybrid systems
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supporting various programming techniques:
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frame-based techniques
rule-based techniques
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data-driven reasoning
goal-driven reasoning
inductive techniques
realization:
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using of object-oriented tools
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Frames
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knowledge-representation unit developed on
epistemology foundations
formal tool using for description of structured objects or
events or notions
characteristics of frames:
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a frame contains:
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the name of the object/event
its important properties (attributes)  stored in slots (slot identifier,
type, value – it can be another frame)
classes, subclasses, instances
hierarchical structure (is_a, instance_of relations)
inheritance (classes - subclasses, classes - instances)
procedures controlled by events: daemons
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Formalization of frames 1
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directed graph
Person
is_a
status
subjects
f_name
l_name
Subject
is_a
name
preconditions
instance_of
name
Teacher
Student
subjects
ES
Expert_
systems
preconditions
AI
instance_of
f_name
l_name
status
subjects
Rozália
instance_of
Peter
Peter
f_name
subjects
l_name
Kis
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Formalization of frames 2
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description in frame-based environment
frame
person
is_a class
f_name:
l_name:
end
frame
is_a
subjects:
end
student
person
collection_of subject
frame
Peter
instance_of student
f_name: Peter
l_name: Kis
subjects: ES
end
frame
ES
isnstance_of subject
name:
Expert_systems
precond: AI
end
frame
subject
is_a
class
name:
precond: collection_of
subject
end
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Formalization of frames 3
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object-attribute-value triplets
<Peter, f_name, Peter>
<Peter, l_name, Kis>
<Peter, subjects, [ES]>
<ES, name, Expert_systems>
<ES, preconditions, [AI]>
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Daemons 1
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active elements of a frame system
standard built-in procedures
assigned to the attributes of the classes and instances
automatically invoked in case of predefined changing in
the value of the slot
usual daemons are as follows:
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when-needed: describes the steps to be performed when the
value of slot is read
when-changed: is invoked when the value of the slot is changed
when-added: contains the actions to be performed when the slot
gets its first value
when deleted: is executed when the value of the slot is deleted
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Daemons 2
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the executable part of the daemons is determined by the
user or it may even be empty
execution is controlled by events
daemons can invoke (call) each other via changing slot
values  spread over and over
the operation of a frame system is described in an
indirect way (embedded in the daemons)
daemons can be used for restricted data-driven
reasoning
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Daemons versus rules
Daemons
Rules
Faster and more independent than rules.
„Reason/action” is connected to the changes in
values and the system’ responses.
They act in autonomous way.
A rule is invoked by another rule or in case of
presence of a certain data.
The execution depends on the situation and
cannot be seen in advance.
Less readable than rules.
(daemons are defined on the implementation
language of the given tool)
Easy to read.
(symbolic formalism, natural-language like)
They handle the pre-defined changes of the
given attribute-values.
The built-in knowledge of the rules steams freely
to all of the rules.
The range of a deamon is bounded statically in
advance. (more or less flexible)
The range of a rule is stand out dynamically in
run-time. (flexible, creative problem solving)
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Hybrid techniques
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rules: used for description of heuristic knowledge
frames: contains both descriptive and procedural
knowledge of the given objects/ events/ notions
(altogether in one place!  easy to read and modify, the
effects of modifications can be held easily)
inference engine of hybrid techniques can contain:
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mechanisms insuring inheritance and handling of daemons
mechanisms insuring message changing (object-oriented)
data-driven and/or goal-driven reasoning mechanism
can support the organization of rules and/or frames into
hierarchical modules
can support making and using of meta-rules
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Symbol-manipulation
techniques
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Programming languages of AI
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high-level symbol-manipulation languages are used to
support the implementation of AI methods
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LISP (LISt Processing)
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based on the notion and operations of lists
all of the problems can be described in the form of function calls
PROLOG (PROgramming in LOGic)
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high-level declarative language
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define relationships between various entities with the help of logic
special type of clause (A  B1 …  Bn): fact, rule, question
reasoning environment with a built-in inference engine
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answer to a question with the help of logical reasoning
goal-driven (backward) reasoning
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Comparison of symbol-manipulation
and traditional techniques
Traditional programming languages
LISP
PROLOG
numeric calculus
symbol-manipulation
symbol-manipulation
Neumann-principle languages
consist of sequence of commands
executed in a predefined order
functional approach
sequence of evaluation of functionexpressions
(-calculus)
relation approach
based on mathematical logic
(predicate-calculus)
main elements: commands
main elements: functions (procedures)
main elements: predicates (relations
among objects)
procedural (executing in a predefined
order)
procedural
declarative (defining only the description
of the problem)
executing mechanism have to be defined
by the programmer
executing mechanism have to be defined
by the programmer
built-in executing mechanism
(goal-driven reasoning with backtracking
search strategy)
the structure of program and data is
different
the sructure of program and data is the
same (can produce, execute other
programs, can modify themselves)
the sructure of program and data is the
same (can produce, execute other
programs, can modify themselves)
readability: LISP-like
hard to read
easy to read
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Case-based techniques
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Case-based reasoning (CBR) 1
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basic assumption: like was the past like will be the future
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the „really” observation can be describe hard with the help of
classical rules
it consists of interconnected relationships of more or less
generalized events
idea:
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solving problems based on solutions for similar problems solved
in the past
requires storing, retrieving and adapting past solutions to similar
problems
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Case-based reasoning 2
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solve a new problem by making an analogy to an old one
and adapting its solution to the current situation
retrieving a case starts with a problem description and
ends when a best matching case has been found
all case-based reasoning methods have in common the
following process:
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identifying a set of relevant problem descriptors
retrieve the most similar case (or cases) comparing the case to
the library of past cases
reuse the retrieved case to try to solve the current problem
revise and adapt the proposed solution if necessary
retain the final solution as part of a new case
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Case
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a case represents specific knowledge in a particular context
there are three major parts in any case:
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a description of the problem/situation
the state of the world when the case is available
solution
the chain of operators that were used to solve the problem (solving
path)
outcome/consequence
the state of the world after the supervention of the case (description
of the effect on the world)
in addition to specific cases, one also has to consider the
case memory organisation
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Case - indexing
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the most important problem in CBR
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how do we remember when to retrieve what?
essentially, the indexing problem requires assigning
labels to cases to designate the situations in which they
are likely to be useful
indexing of cases - issues
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indexing should anticipate the vocabulary a retriever might use
indexing has to be by concepts normally used to describe the
items being indexed
indexing has to anticipate the circumstances in which a retriever
is likely to want to retrieve something
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Main components of case-based
systems 1
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case-base (library of cases)
tools for determining of key-elements of actual case and
for retrieving of most-similar cases
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for speeding of data-retrieval  indexing
for finding suitable cases  pattern, similarity-estimation
tools for the solution’ adaptation according to the
specialities of the new case
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finding the deviations, implementation of alterations in the
suggested solution (ex. null-adaptation, parameter adjustment)
supervision (solution after the adaptation is suitable or not)
learning (finding the reason of failure or enclosing the case to the
case-base)
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Main components of case-based
systems 2
new problem
indexing
retrieving
(case-matching)
similar cases
case-base
selecting
proposed solution
adapting
learning
checking
solution
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Advantages and disadvantages
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advantages:
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case-base is more objective and formal than the expert’s
interpretation (knowledge of expert’s)
knowledge are represented in an explicit way
case can be defined for incomplete or badly-defined notions
CBR is suitable for domains for which a proper, theoretical
foundations do not exist
CBR is applicable in default of algorithmic method
easy knowledge acquisition (get well during usage)
disadvantages:
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CBR solves only the problems covered by cases
CBR might use a past case blindly without validating it in the new
situation
solution is time-demanding (also in case of proper indexing)
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Rule-based systems versus casebased systems
Rule-based systems
Case-based systems
Rule: symbolic pattern
Case: collection of data, constants
Rule: individual unit, independent of the other rules,
consistent piece of field of interest
Case: depends on the other cases (often overlap each
other), individual unit of the field of interest
Retrieving rule: exact matching
Retrieving case: partial matching
Using of rules: general iterativ cycle
Using of cases: several steps (approximate retrieval,
adaptation, refinement)
The model of the problem have to be developed
(sometimes it is hard or impossible)
The model of the problem needn’t be developed
The knowledge-acquisition of field of interst is hard and
time-demanding
The knowledge-acquisition of field of interst is limited to
collecting and analysing the past cases
Development time is long
Development time is short
Slow, handling of many data is difficult
Many data is treatable with the useing of database –
handling techniques
Enlargement is hard (the validation have to be repeated
after enlargement)
Enlargement and development is easy.
Learning is not supported
It is able to learn (preserving new cases)
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Summary
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Knowledge-based systems, expert systems
Base techniques of knowledge-based systems
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rule-based techniques
inductive techniques
hybrid techniques
symbol-manipulation techniques
case-based techniques
References
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K. M. Hangos, R. Lakner and M. Gerzson: Intelligent Control Systems. An
Introduction with Examples. Kluwer Academic Publishers, 2001. Chapter 5.
D. Poole, A. Mackworth, R. Goebel: Computational Intelligence. A logical
Approach. Oxford University Press, 1998. Chapter 6.
I. Futó (Ed.): Mesterséges intelligencia. Aula Kiadó, 1999. Chapter 12. (in
hungarian)
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