Intorduction to Artificial Intelligence

Prof. Dechter

ICS 270A

Winter 2003

270a- winter 2003

Course Outline

Classoom: ICS2-144 Days: Tuesday & Thursday Time: 09:30 a.m. - 10:50 a.m. Instructor: Rina Dechter Textbooks

Nils Nilsson, "Artificial Intelligence: A New Synthesis", Morgan Kauffmann, 1998

S. Russell and P. Norvig, "Artificial Intelligence: A Modern Approach" (Second Edition), Prentice Hall, 1995

J. Pearl, "Heuristics: Intelligent Search Stratagies", Addison-Wesley, 1984.

270a- winter 2003

Assignments: There will be weekly homework-assignments, a project, a

midterm and/or a final.

Course-Grade: Homeworks plus project will account for 50% of the grade,

midterm and/or final 50% of the grade.

Course Overview Topics covered Include: Heuristic search, Adverserial

search, Constraint Satisfaction Problems, knowledge representation, propositional and first order logic, inference with logic, Planning, learning and probabilistic reasoning.

Course Outline

270a- winter 2003

Week Topic Date  

Week 1

Introduction and overview: What is AI? History 7-Jan

Nillson Ch.1 (1.1-1.5), RN: chapters 1,2.

Problem solving: Statement of Search problems: state space graph, problem types, examples (puzzle problem, n-queen, the road map, travelling sales-man.)

Nillson Ch 7. RN: chapter 3, Pearl: ch.1

Week 2

Uninformed search: Greedy search, breadth-first, depth-first, iterative deepening, bidirectional search.

14-Jan

Nillson Ch. 8, RN: Ch. 3, Pearl: 2.1, 2.2

Informed heuristic search: Best-First, Uniform cost, A*, Branch and bound.

Nillson Ch. 9, RN: Ch. 4 , Pearl, 2.3.1

Week 3

Properties of A*, iterative deepening A*, generating heuristics automatically. Learning heuristic functions.

28-Jan

Nillson Ch. 9, 10.3, RN: chapter 4, Pearl: 3.1, 3.2.1, 4.1, 4.2

Game playing: minimax search, alpha-Beta pruning.

Nillson Ch. 12, RN: Ch. 6.

Course Outline

270a- winter 2003

Week 4 Constraint satisfaction problems 21-Jan

Definitions, examples, constraint-graph, constraint propagation (arc-consistency, path-consistency), the minimal network.

Reading: RN: Ch. 5, class notes.

Backtracking and variable-elimination

advanced search: forward-checking, Dynamic variable orderings, backjumping, solving trees, adaptive-consistency.

Reading: RN: Ch. 5, class notes.

Week 5 Knowledge and Reasoning: Propositional logic, syntax, semantics, inference rules.

4-Feb

Nillson Ch. 13, RN: Ch 7.

Propositional logic. Inference, First order logic

Nillson Ch. 14, RN: Ch. 7

Week 6 Knowledge representation: 11-Feb

First-order (predicate) Logic.

Nillson Ch. 15, RN: Ch. 9.

Course Outline

270a- winter 2003

Week 7 Inference in First Order logic 18-Feb

Nillson Ch. 16, RN: Ch. 9

Planning:

Logic-based planning, the situation calculus, the frame problem.

Nillson Ch. 21, RN: Ch. 11.

Week 8 Planning: Planning systems, STRIP, regression planning, current trends in planning: search-based, and propositional-based.

25-Feb

Nillson Ch. 22, RN: Ch. 11.

Week 9 Reasoning and planning under uncertainty 4-Mar

Nillson Ch. 19, RN: chapter 14.

Week 10 Assorted topics 11-Mar

Course Outline

270a- winter 2003

Course Outline

Resources on the Internet AI on the Web: A very comprehensive list of Web

resources about AI from the Russell and Norvig textbook.

Essays and Papers What is AI, John McCarthy Rethinking Artificial Intelligence, Patrick H. Winston International Summer School on AI Planning An overview of recent algorithms for AI planning, Jussi

Rintanen

270a- winter 2003

Today’s class

What is Artificial Intelligence? Engineering versus cognitive approaches Intelligent agents History of AI Real-World Applications of AI

many products, systems, have AI components

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What is Artificial Intelligence? Thought processes vs behavior Human-like vs rational-like RN figure: “How to simulate humans intellect and

behavior on by a machine. Mathematical problems (puzzles, games,

theorems) Common-sense reasoning Expert knowledge: lawyers, medicine,

diagnosis Social behavior

270a- winter 2003

What is Artificial Intelligence

Thought processes “The exciting new effort to make computers

think .. Machines with minds, in the full and literal sense” (Haugeland, 1985)

Behavior “The study of how to make computers do

things at which, at the moment, people are better.” (Rich, and Knight, 1991)

The automation of activities that we associate with human thinking, activities such as decision-making, problem solving, learning… (Bellman)

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270a- winter 2003

The Turing Test

RequiresNatural languageKnowledge representationAutomated reasoningMachine learning (vision, robotics)

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Acting humanly Turing test (1950) Requires:

Natural language Knowledge representation automated reasoning machine learning (vision, robotics.) for full test

Thinking humanly: Introspection, the general problem solver (Newell and

Simon 1961) Cognitive sciences

Thinking rationally: Logic Problems: how to represent and reason in a domain

Acting rationally: Agents: Perceive and act

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AI examplesCommon sense reasoning Tweety Yale Shooting problem

Update vs revise knowledge The OR gate example: A or B - C Observe C=0, vs Do C=0Chaining theories of actions

Looks-like(P) is(P)Make-looks-like(P) Looks-like(P)----------------------------------------Makes-looks-like(P) ---is(P) ???

Garage-door example: garage door not included. Planning benchmarks 8-puzzle, 8-queen, block world, grid-space world (Nillson Fig 1.2)

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History of AI McCulloch and Pitts (1943)

Neural networks that learn Minsky (1951)

Built a neural net computer Darmouth conference (1956):

McCarthy, Minsky, Newell, Simon met, Logic theorist (LT)- proves a theorem in Principia Mathematica-Russel. The name “Artficial Intelligence” was coined.

1952-1969 GPS- Newell and Simon Geometry theorem prover - Gelernter (1959) Samuel Checkers that learns (1952) McCarthy - Lisp (1958), Advice Taker, Robinson’s resolution Microworlds: Integration, block-worlds. 1962- the perceptron convergence (Rosenblatt)

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History, continued

1966-1974 a dose of reality Problems with computation

1969-1979 Knowledge-based systems Weak vs. strong methods Expert systems:

• Dendral:Inferring molecular structures• Mycin: diagnosing blood infections• Prospector: recomending exploratory drilling (Duda).

Roger Shank: no syntax only semantics 1980-1988: AI becomes am industry

R1: Mcdermott, 1982, order configurations of computer systems

1981: Fifth generation 1986-present: return to neural networks Recent event:

Hidden markov models, planning, belief network

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What’s involved in Intelligence?Intelligent agents

Ability to interact with the real world to perceive, understand, and act e.g., speech recognition and understanding and synthesis e.g., image understanding e.g., ability to take actions, have an effect

Knowledge Representation, Reasoning and Planning modeling the external world, given input solving new problems, planning and making decisions ability to deal with unexpected problems, uncertainties

Learning and Adaptation we are continuously learning and adapting our internal models are always being “updated”

• e.g. a baby learning to categorize and recogniz animals

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Implementing an Agent

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Implementing agents Table look-ups Autonomy

All actions are completely specified no need in sensing, no autonomy example: Monkey and the banana

Structure of an agent agent = architecture + program Agent types

• medical diagnosis• Satellite image analysis system• part-picking robot• Interactive English tutor• cooking agent• taxi driver

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Agent types Example: Taxi driver Simple reflex

If car-in-front-is-breaking then initiate-breaking Agents that keep track of the world

If car-in-front-is-breaking and on fwy then initiate-breaking needs internal state

goal-based If car-in-front-is-breaking and needs to get to hospital then go to

adjacent lane and plan search and planning

utility-based If car-in-front-is-breaking and on fwy and needs to get to hospital

alive then search of a way to get to the hospital that will make your passengers happy.

Needs utility function that map a state to a real function (am I happy?)

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AI Application: Reasoning Scheduling:

Nasa Space telescope factory scheduling class scheduling

Puzzle solving Chess Checkers Backgamon

Speech recognition Vision Diagnosis

Medical Circuit diagnosis Health care consulting Decision support systems

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Summary of State of AI Systems , Practice

Speech synthesis, recognition and understanding very useful for limited vocabulary applications unconstrained speech understanding is still too hard

Computer vision works for constrained problems (hand-written zip-codes) understanding real-world, natural scenes is still too hard

Learning adaptive systems are used in many applications: have their limits

Planning and Reasoning only works for constrained problems: e.g., chess real-world is too complex for general systems

Overall: many components of intelligent systems are “doable” there are many interesting research problems remaining

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Summary What is Artificial Intelligence?

modeling humans thinking, acting, should think, should act.

History of AI Intelligent agents

We want to build agents that act rationally

Real-World Applications of AI AI is alive and well in various “every day” applications

• many products, systems, have AI components Assigned Reading

Chapter 1, Nillson Chapters 1 and 2 in the text R&N