1. intro class-object oriented analysis and design
TRANSCRIPT
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Intorduction to
Artificial Intelligence
Prof. Dechter
ICS 271Fall 2008
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Course Outline
http://www.ics.uci.edu/~decht
er/courses/ics-271/fall-08/
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Course Outline
Classoom: ICS-243
Days: Tuesday & Thursday
Time: 11:00 a.m. 12:20 a.m. Instructor: Rina Dechter
Textbooks S. Russell and P. Norvig, "Artificial Intelligence: A
Modern Approach"(Second Edition), Prentice Hall,
1995 Nils Nilsson, "Artificial Intelligence: A New Synthesis",
Morgan Kauffmann, 1998
J. Pearl, "Heuristics: Intelligent Search Stratagies",Addison-Wesley, 1984.
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Assignments:
There will be weekly homework-assignments, a project, amidterm or a final.
Course-Grade: Homeworks plus project will account for 50% of the grade,
midterm or final 50% of the grade.
Course Overview
Topics covered Include: Heuristic search, Adverserial
search, Constraint Satisfaction Problems, knowledgerepresentation, propositional and first order logic, inferencewith logic, Planning, learning and probabilistic reasoning.
Course Outline
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Week Topic Date
Week
1
Introduction andoverview: What is AI? History 26-
SeptNillson 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.
05-oct
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.
12-oct
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 19-oct
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.
26-oct
Propositional logic. Inference, First order logic
RN: Ch. 7
Week 6 Knowledge representation: 02-Nov
First-order Logic.
RN: Ch. 9.
Course Outline
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Week 7 Inference in First Orderlogic 09-Nov
RN: Ch. 9
Planning
Week 8 Planning: Logic-based planning, the situation calculus,
the frame problem. Planning systems, STRIP, regression
planning, current trends in planning: search-based, and
propositional-based.
16-Nov
RN: Ch. 11.
Week 9 Reasoning underuncertainty 23-Nov
RN: chapter 14.
Thanksgiving
Week 10 Probabilistic reasoningover time
RN: Chapters 14,15Assorted topics
30-Nov
Course Outline
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Course Outline
Resources on the Internet
AI on the Web: A very comprehensive list ofWeb resources about AI from the Russell and
Norvig textbook.
Essays and Papers
What is AI, John McCarthy
Computing Machinery and Intelligence, A.M.Turing
Rethinking Artificial Intelligence, PatrickH.Winston
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Todays class
What is Artificial Intelligence?
A brief History
Intelligent agents State of the art
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Todays class
What is Artificial Intelligence?
A brief History
Intelligent agents State of the art
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What is Artificial
Intelligence? Thought processes vs behavior
Human-like vs rational-like
How to simulate humans intellect andbehavior by a machine.
Mathematical problems (puzzles, games,theorems)
Common-sense reasoning
Expert knowledge: lawyers, medicine,diagnosis
Social behavior
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What is AI?
Views of AI fall into four categories:
Thinking humanly Thinking rationally
Acting humanly Acting rationally
The textbook advocates "acting rationally
List of AI-topics
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What is Artificial Intelligence
(John McCarthy , Basic Questions)
What is artificial intelligence?
It is the science and engineering of making intelligent machines, especiallyintelligent computer programs. It is related to the similar task of usingcomputers to understand human intelligence, but AI does not have to confineitself to methods that are biologically observable.
Yes, but what is intelligence?
Intelligence is the computational part of the ability to achieve goals in theworld. Varying kinds and degrees of intelligence occur in people, manyanimals and some machines.
Isn't there a soliddefinition of intelligence that doesn't dependonrelating it to human intelligence?
Not yet. The problem is that we cannot yet characterize in general what kindsof computational procedures we want to call intelligent. We understand someof the mechanisms of intelligence and not others.
More in: http://www-formal.stanford.edu/jmc/whatisai/node1.html
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What is Artificial Intelligence
Thought processes
The exciting new effort to make computers
think .. Machines with minds, in the full andliteral 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)
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The Turing Test(Can Machine think? A. M. Turing, 1950)
Requires:
Natural language
Knowledge representation Automated reasoning
Machine learning
(vision, robotics) for full test
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Acting/Thinking
Humanly/Rationally Turing test (1950)
Requires:
Natural language
Knowledge representation
automated reasoning
machine learning
(vision, robotics.) for full test
Methods for Thinking Humanly:
Introspection, the general problem solver(Newell andSimon 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 (1980-1990)
Tweety Yale Shooting problem
Update vs revise knowledge
The OR gate example: A or B C
Observe C=0, vs Do C=0
Chaining theories ofactionsLooks-like(P) is(P)
Make-looks-like(P) Looks-like(P)
----------------------------------------
Makes-looks-like(P) ---is(P) ???
Garage-doorexample: garage door not included. Planning benchmarks
8-puzzle, 8-queen, block world, grid-space world
Cambridge parking example
Smoked fish example
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Todays class
What is Artificial Intelligence?
A brief history
Intelligent agents State of the art
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History of AI McCulloch and Pitts (1943)
Neural networks that learn
Minsky and Edmonds (1951)
Built a neural net computer
Darmouth conference (1956):
McCarthy, Minsky, Newell, Simon met,
Logic theorist (LT)- Of Newell and Simon proves atheorem 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, Robinsonsresolution
Microworlds: Integration, block-worlds.
1962- the perceptron convergence (Rosenblatt)
McCulloch and Pitts (1943)
Neural networks that learn
Minsky and Edmonds (1951)
Built a neural net computer
Darmouth conference (1956):
McCarthy, Minsky, Newell, Simon met,
Logic theorist (LT)- Of Newell and Simon proves atheorem 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, Robinsonsresolution
Microworlds: Integration, block-worlds.
1962- the perceptron convergence (Rosenblatt)
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The Birthplace of
Artificial Intelligence, 1956 Darmouth workshop, 1956: historical meeting of the precieved
founders of AI met: John McCarthy, Marvin Minsky, Alan Newell,and Herbert Simon.
A Proposal for the Dartmouth Summer Research Project on ArtificialIntelligence. J. McCarthy, M. L. Minsky, N. Rochester, and C.E.Shannon. August 31, 1955. "We propose that a 2 month, 10 man studyof artificial intelligence be carried out during the summer of 1956 atDartmouth College in Hanover, New Hampshire. The study is toproceed on the basis of the conjecture that every aspect of learning orany other feature of intelligence can in principle be so preciselydescribed that a machine can be made to simulate it." And this marksthe debut of the term "artificial intelligence.
50 anniversery of Darmouth workshop List of AI-topics
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History of AI- continued McCulloch and Pitts (1943)
Neural networks that learn
Minsky and Edmonds (1951)
Built a neural net computer
Darmouth conference (1956):
McCarthy, Minsky, Newell, Simon met,
Logic theorist (LT)- Of Newell and Simon proves atheorem 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, Robinsonsresolution
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 an industry
R1: Mcdermott, 1982, order configurations of computersystems
1981: Fifth generation
1986-present: return to neural networks
Recent event:
AI becomes a science: HMMs, planning, belief network
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State of the art
Deep Blue defeated the reigning world chesschampion Garry Kasparov in 1997
Proveda mathematical conjecture (Robbinsconjecture) 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 AIlogistics planning and scheduling program thatinvolved up to 50,000 vehicles, cargo, and people
NASA's on-board autonomous planning programcontrolled the scheduling of operations for a spacecraft
Proverb solves crossword puzzles better than mosthumans
DARPA grand challenge 2003-2005, Robocup
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Robotic links
Robocup Video
Soccer Robocupf
Darpa Challenge
Darpas-challenge-video
http://www.darpa.mil/grandchallenge05/TechPapers/Stanford.pdf
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Todays class
What is Artificial Intelligence?
A brief History
Intelligent agents State of the art
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Agents (chapter 2)
Agents and environments
Rationality
PEAS (Performance measure,
Environment, Actuators, Sensors)
Environment types
Agent types
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Agents
An agent is anything that can be viewed asperceiving its environment through sensorsand acting upon that environment through
actuators
Human agent: eyes, ears, and other organsfor sensors; hands,
legs, mouth, and other body parts foractuators
Robotic agent: cameras and infrared range
finders for sensors;
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Agents and environments
The agent function maps from percept
histories to actions:
[f: P* A]
The agent program runs on the physical
architecture to produce f
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Whats involved in Intelligence?
Intelligent agents
Ability to interact with the real world to perceive, understand, and act
e.g., speech recognition and understanding andsynthesis
e.g., image understanding
e.g., ability to take actions, have an effect
Knowledge Representation, Reasoning andPlanning
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 recognizeanimals
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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 ofan agent
agent = architecture + program
Agent types
medical diagnosis
Satellite image analysis system
part-picking robot Interactive English tutor
cooking agent
taxi driver
Graduate student
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Grad student
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Agent types
Example: Taxi driver Simple reflex
Ifcar-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 hospitalthen go to adjacent lane and plan
search and planning utility-based
If car-in-front-is-breaking andon fwy and needs toget to hospital alive then search of a way to get to thehospital that will make your passengers happy.
Needs utility function that map a state to a realfunction (am I happy?)
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Summary
What is Artificial Intelligence?
modeling humans thinking, acting, should think,should act.
Historyof 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
Chapters 1 and 2 in the text R&N