B. Ravikumar
Computer Science Department
116 I Darwin Hall
Class meets:
Fridays 9 to 12

Catalog Description:
A survey of techniques that simulate human intelligence.
Topics may include: pattern recognition, general problem solving, adversarial game-tree search, decision-making, expert systems, neural networks, fuzzy logic, and genetic algorithms.
Prerequisite: CS 315 or consent of instructor.
Background Expected:
•Programming and data structures (CS 315)
• Discrete mathematics (CS 242)
•Linear algebra
Some background in logic and probability will be helpful, but not necessary.

Course Goals:
AI covers wide range of topics:
• understanding language
• vision and speech processing
• problem solving, planning
• common sense reasoning.
AI techniques:
• combinatorial (searching, A* algorithm etc.)
• logical (prove assertion in formal framework)
• probabilistic (decision tree, Bayesian network)
• machine learning (neural network, evolutionary technique)

Course details
Other references:
• N. Nillsson, AI: A new synthesis.
• Winston, Artificial Intelligence.

Course details

Course details
Short Quizzes (5 – 10%)
Two Mid-Term tests (20%) – Both tests will be in class and will be about 75 miutes long. The tests will be open book/open notes.
Home Work and Projects (40 - 50%) – There will be some common programming projects and a final project.
The final project will be done individually. You can choose a problem from a list that will be provided early in the semester. The project is due the last week of the semester. You are to write a report summarizing your contributions to the chosen problem. Some selected project work will be presented in the department colloquium.
Final Examination (25 - 30%) – The final examination will be comprehensive and will take place at the scheduled time posted in the web page http://www.sonoma.edu/university /classsched/ finals_sched.pdf
(not updated for Fall 09 as of August 15, 2009.)

• Course overview
• What is AI?
• A brief history
• The state of the art
Slides adapted from Russell and Norvig, AIAMA

• Introduction (chapters 1,2)
Techniques
• Combinatorial (search) approach to AI (chapters
3,4,5,6)
• Symbolic (logical) approach to AI (chapters 7,8,9)
• Probabilistic approach to AI (chapters 13,14)
• Learning approach to AI (chapters 18,20)
Applications
• Natural Language Processing (chapter 22,23)
• Computer vision (Chapter 24)

Before attempting a definition, we will state some major contemporary applications of AI:
• business: advertising, financial decision making
• web: identifying objects in images, social network models etc.
• medical: image classification (belign vs. malignant tumor), image analysis using functional MRI
• multiple field: language translation, semantic analysis, speech synthesis, speech to text conversion.
• industrial: vision, robotics

• Turing (1950) "Computing machinery and intelligence":
• "Can machines think?"  "Can machines behave intelligently?"
• Operational test for intelligent behavior: the Imitation Game
• Predicted that by 2000, a machine might have a 30% chance of fooling a lay person for 5 minutes
• Anticipated all major arguments against AI in following 50 years
• Suggested major components of AI: knowledge, reasoning, language understanding, learning
•

• 1960s "cognitive revolution": informationprocessing psychology
•
• Requires scientific theories of internal activities of the brain
•
• How to validate? Requires
1) Predicting and testing behavior of human subjects (topdown) or
2) Direct identification from neurological data (bottomup)
• Both approaches (roughly, Cognitive Science and
Cognitive Neuroscience)

• What are correct arguments/thought processes?
• Several Greek schools developed various forms of logic:
notation and rules of derivation for thoughts; may or may not have proceeded to the idea of mechanization
• Direct line through mathematics and philosophy to modern AI
• Problems:
1.
Not all intelligent behavior is mediated by logical deliberation
2.
What is the purpose of thinking? What thoughts should
I have?

Acting rationally: rational agent
• Rational behavior: doing the right thing
•
• The right thing: that which is expected to maximize goal achievement, given the available information
•
• Doesn't necessarily involve thinking – e.g., blinking reflex – but thinking should be in the service of rational action
•

• Combinatorial search problems
– state space (over which search is performed)
– finite state space (discrete)
– how to move from one state to another (transition rules)
• Applications
– Games (one player or two players)
– Navigation (robotics)
• Solution
– Search tree exploration

techniques
• Combinatorial search approach
Sliding piece puzzle:
Start:
1 2 3
4 6
7 5 8 goal:
1 2 3
4 5 6
7 8
Legal moves: slide a piece next to empty slot.
Many AI problems can be modeled as search problems.

A portion of a search tree for the 8-puzzle.

Combinatorial search
•Uninformed search
• depth-first
• breadth-first
• iterative deepening
• breadth-depth
• informed search
• best-first

Combinatorial search
•Depth-first search
What are the ways to speed-up DFS?

Breadth-first search

Combinatorial search
heuristic search
• for each node, a heuristic provides an estimate of its distance from the goal.
• for sliding-piece puzzle, Manhattan distance is one such estimate.
• estimate for other search problems? (e.g. queen placement )

Combinatorial search
Consider placement in the
5 th row.

• Symbolic (logical) approach to AI intelligent problem solving requires reasoning and deduction.
Knowledge is represented as a set of logical assertions A
1
, …, A n
, and a conclusion to be drawn is also expressed as an assertion.
Can we deduce F from A
1
, …, A n
?

• Knowledge base = set of sentences in a formal language
•
• Declarative approach to building an agent (or other system):
– Tell it what it needs to know
–
• Then it can Ask itself what to do - answers should follow from the KB
•
• Agents can be viewed at the knowledge level i.e., what they know, regardless of how implemented

• If Alex goes, then Beki goes: A

B
• If Chris goes, then Alex goes: C

A
• Beki does not go: not B
• Chris goes: C
Query: Is it possible to satisfy all these conditions?
This is called satisfiability problem.

• Programming languages:
– Formal languages, not ambiguous, but cannot express partial information. Not expressive enough.
• Natural languages:
– Very expressive but ambiguous: ex: small dogs and cats.
• Good representation language:
– Both formal and can express partial information, can accommodate inference
• Main approach used in AI: Logic-based languages.
• Predicate-logic with Horn clauses

Example:
Given P  R, and Q  ~R
Can we deduce ~(P & Q)?
Resolution strategy
Applications
• expert systems (Mycin, dendral are early examples)
• logic programming
• automatic theorem proving
(software validation)

Example:

X (

Y ((mother(X)
 child_of(Y,X))
 loves(X,Y))) mother(mary) child_of(tom,mary)
Can we deduce?
loves(tom, mary)

• Probabilistic approach to AI
Knowledge representation models uncertainties.
Example:
• H = “Have a headache”
• F = “Coming down with Flu”
• P(H) = 1/10
• P(F) = 1/40
• P(H|F) = ½
Given that you have a headache, what is the probability that you have flu?
This kind of modeling is widely used in various prediction problems, e.g., in determining the insurance premium for car etc.

Some games are inherently probabilistic.
•Financial markets
• backgammon

techniques
Training set
New applicant: (young, has job, does not own house, good credit).
Will (s)he default? We can build a probabilistic model to answer.

techniques
Machine learning approach to AI:
• self-improving algorithms
• solution obtained without explicit programming
• Closer to modeling human intelligence or natural intelligence (we learn many things by observing even if step by step procedure absent)
Prominent examples:
• Neural networks
• Genetic algorithms, evolutionary method

techniques
Neuron (very roughly modeled by neurons in human brains.

An algorithm called back propagation algorithm is used to adjust the weights of neurons based on the discrepancy between correct output and computed output.

Evolutionary algorithms:
• encoding of the collection of solutions as strings.
• goal is to evolve the “best” solution.
• use cross-over and mutation and iterate.
Example of cross-over and mutation

• Philosophy
• Mathematics
• Economics
• Neuroscience
• Psychology
• Computer engineering
• Control theory
• Linguistics
Logic, methods of reasoning, mind as physical system foundations of learning, language, rationality
Formal representation and proof algorithms, computation, (un)decidability, (in)tractability, probability utility, decision theory physical substrate for mental activity phenomena of perception and motor control, experimental techniques building fast computers design systems that maximize an objective function over time knowledge representation, grammar

• 1943
• 1950
• 1956
• 1950s
McCulloch & Pitts: Boolean circuit model of brain
Turing's "Computing Machinery and Intelligence"
Dartmouth meeting: "Artificial Intelligence" adopted
Early AI programs, including Samuel's checkers program, Newell & Simon's Logic Theorist,
Gelernter's Geometry Engine
• 1965 Robinson's complete algorithm for logical reasoning
• 1966—73 AI discovers computational complexity
Neural network research almost disappears
• 1969—79 Early development of knowledge-based systems
• 1980--
• 1986--
• 1987--
• 1995--
AI becomes an industry
Neural networks return to popularity
AI becomes a science, probabilistic techniques dominate
The emergence of intelligent agents

• Deep Blue defeated the reigning world chess champion
Garry Kasparov in 1997
• Proved a mathematical conjecture (Robbins conjecture) unsolved for decades
• No hands across America (driving autonomously 98% of the time from Pittsburgh to San Diego)
• During the 1991 Gulf War, US forces deployed an AI logistics planning and scheduling program that involved up to
50,000 vehicles, cargo, and people
• NASA's on-board autonomous planning program controlled the scheduling of operations for a spacecraft
• Proverb solves crossword puzzles better than most humans