1. intro class-object oriented analysis and design

8/7/2019 1. Intro Class-OBJECT ORIENTED ANALYSIS AND DESIGN

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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


A brief History



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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


A brief history



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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)








1952-1969







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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)








1952-1969







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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


A brief History



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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


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

1. intro class-object oriented analysis and design

Documents