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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
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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
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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
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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
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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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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