2015-8-26 EIE426-AICV 1 Introduction to Artificial Intelligence Filename: eie426-intro-0809.ppt.

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Introduction to Artificial Intelligence

Filename: eie426-intro-0809.ppt

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Contents

A typical definition of Artificial Intelligence (AI)

The Turing test

AI applications

Topics Covered by AI

Basic knowledge representation schemes

Basic problem solving paradigms

Brief history of AI

The State of the Art

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What is AI?

The development of theories and techniques required to provide a computational engine the abilities to perceive, think and act, in an intelligent matter, in a complex environment.

Systems that think like humans Systems that think rationally

Systems that act like humans Systems that act rationally

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Engineering Goal: to solve real-world problems using AI ideas about representing knowledge, using knowledge, and assembling systems.

Scientific Goal: to determine which ideas about representing knowledge, using knowledge, and assembling systems explains various sorts of intelligence.

Artificialintelligence

Computer science and engineering

psychologypsychology

What is AI? (cont.)

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The Turing Test

Computing machinery and intelligence, Turing 1950 “Can machines think?” “Can machines behave

intelligently?” Operational test for intelligent behavior: the Imitation Game

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The Turing Test (cont.)

The Turing test measures the performance of an allegedly intelligent machine against that of a human being.

Three important features of the test:1. An objective notion of intelligence2. Preventing us from being sidetracked by confusing and

currently unanswerable questions3. Eliminating any bias in favor of living organisms

The problem: Turing test is not reproducible, constructive, or amenable to mathematical analysis.

Some Turing Test Questions “What is the meaning of life?” “Why is the sky blue?” “How are you?” “Is OJ guilty?” “Which came first, the chicken or the egg?” “What word rhymes with Orange?” “What is love?” and “How does love feel?” “Boxers or briefs?” “Who is your best friend?” “Do you prefer open minds or only open preferred minds?” “Please respond only when I say 'Simon Says'.” “Do you think that I should undergo an operation to remove my brain and

install a computer in my head?” “Tell me about your childhood.” “How are you feeling today?” “When were you born?”

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AI Applications

Perception Vision Speech

Natural language Understanding Generation Translation

Commonsense reasoning Robot control (http://www.cs.rochester.edu/u/jag/demos/demos.html)

Mundane Tasks

demo

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Games Chess Backgammon Checkers Go

Mathematics Geometry Logic Integral calculus Proving properties of programs

Formal Tasks

AI Applications (cont.)

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Engineering Design Fault finding Manufacturing planning

Scientific analysis Medical diagnosis Financial analysis

Expert Tasks

AI Applications (cont.)

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Example: Airport Resource Information System

Arrange gates for flights considering the seat capacity, time to take off, connected flights, weather condition, emergent factors, etc.

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Topics Covered by AI search and game-playing (√) logical systems knowledge (expert) systems (√) planning systems (√) uncertainty - probability and decision theory machine learning (√) computational intelligence

- artificial neural networks (ANN’s)

- fuzzy systems (FS’s), and

- evolutionary algorithms (EA’s) (√) natural language processing (NLP) (√) perception (√) robotics (√) philosophical issues (√)

√: to be taught

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Relationships among various topics

Search

Vision

PlanningMachine Learning

Knowledge RepresentationLogic

Expert SystemsRoboticsNLP

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Semantics nets (a subset: frames) √ Predicate logic Production rules √

A representation is a set of conventions about how to describe a class of things. A description makes use of the conventions of a representation to describe some particular thing.

Representation Techniques:

Knowledge Representation

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Example: The Farmer, Fox, Goose, and Grain

A farmer wants to move himself, a silver fox, a fat goose, and some tasty grain across a river. Unfortunately, his boat is so tiny he can take only one of his possessions across on any trip. Worse yet, an unattended fox will eat a goose, and an unattended goose will eat grain, so the farmer must not leave the fox alone with the goose or the goose alone with the grain. How can he do?

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Example: The Farmer, Fox, Goose, and Grain (cont.)

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Powerful Idea:

A lexical part - vocabulary A structural part - constraints A procedural part - access procedures A semantic part - meaning

Once a problem is described using an appropriate representation, the problem is almost solved.

A Representation has Four Fundamental Parts:

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Semantic Nets

A semantic net is a representation

In which Lexically, there are nodes, links, and application-specific link labels. Structurally, each link connects a tail node to a head node. Semantically, the nodes and links denote application-specific entities.

With constructors that Construct a node Construct a link, given a link label and two nodes to be connected

With readers that Produce a list of all links departing from a given node Produce a list of all links arriving at a given node Produce a tail node, given a link Produce a head node, given a link Produce a link label, given a link

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Semantic Nets – Different Froms

To be taught

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Problem Solving Paradigms

Describe and match √ Generate and test √ Means-ends analysis √ Problem-reduction √ Search √ Rule-based systems √ Predicate calculus

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To identify an object using describe and match, Describe the object using a suitable representation. Match the object description against library descriptions until there

is a satisfactory match or there are no more library descriptions. If you find a satisfactory match, announce it; otherwise, announce

failure.

The Describe-And-Match Method

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Feature-Based Object Identification

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Generate-And-Test

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Generate-And-Test (cont.)

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The Generate-And-Test Method

To perform generate and test, Until a satisfactory solution is found or no more candidate

solutions can be generated, Generate a candidate solution. Test the candidate solution.

If an acceptable solution is found, announce it; otherwise, announce failure.

Three properties for a good generator: complete, nonredundant, informed.

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The Means-Ends Analysis Method

A state space is a representation

That is a semantic net

in which The nodes denote states. The links denote transitions between states.

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Current state

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Algorithm: Means-Ends Analysis(Simple version)

To perform means-ends analysis, Until the goal is reached or no more procedures are available,

Describe the current state, the goal state, and the difference between the two.

Use the difference between the current state and goal state, possibly with the description of the current state or goal state, to select a promising procedure.

Use the promising procedure and update the current state. If the goal is reached, announce success; otherwise, announce failure.

Key idea is to reduce difference between the current state and the goal state.

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The Task:

A robot moves a desk with two things on it from one room to another.

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A Robot’s Operators

Operator Preconditions Results

PUSH(obj, loc) at(robot,obj)^ large(obj)^ clear(obj)^ armempty

at(obj, loc)^ at(robot, loc)

CARRY(obj, loc) at(robot,obj) ^ at(obj, loc)^ at(robot, loc)

WALK(loc)

none

at(robot, loc)

PICKUP(obj) at(robot, obj) holding(obj)

PUTDOWN(obj) holding(obj) holding(obj)

PLACE(obj1, obj2) at(robot, obj2)^ holding(obj1)

on(obj1, obj2)

small(obj)

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A Difference Table

differences

Operators

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The Progress of the Means-Ends Analysis Method

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Algorithm: Means-Ends Analysis(Advanced Version)

1 Compare CURRENT to GOAL. If there are no differences between them then return.

2 Otherwise, select the most important difference and reduce it by doing the following until success or failure is signaled:(a) Select an as yet untried operator O that is applicable to the

current difference. If there are no such operators, then signal failure.

(b)Attempt to apply O to CURRENT. Generate descriptions of two states: O-START, a state which O’s preconditions are satisfied and O-RESULT, the state that would result if O were applied in O-START.

(c)If (FIRST-PART MEA(CURRENT, O-START)) and(LAST-PART MEA(O-RESULT, GOAL))are successful, then signal success and return the result of concatenatingFIRST-PART, O, and LAST-PART.

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The Problem-Reduction (Goal Reduction) Method

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A Decomposable Problem

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Problem-Solving Methods Often Work Together

PolyU HK Airport

HK Airport Bejing Airport

Bejing UBejing Airport

Problem reduction

Walk Taxi Train

Means-ends analysis

PolyU Bejing U

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AI Prehistory

Philosophy logic, methods of reasoning, mind as physical system, foundations of learning, language, rationality

Mathematics formal representation and proof algorithms,computation, (un)decidability, (in)tractability, probability

Psychology adaptation, phenomena of perception and motor control, experimental techniques

Linguistics knowledge representation, grammarNeuroscience physical substrate for mental activityControl theory stability, simple optimal agent designs

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History of AI

Timeline of major AI events

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The State of the Art

Autonomous planning and scheduling: NASA’s Remote Agent Game playing: IBM’s Deep Blue Autonomous control: the ALVINN computer vision system, to

steer a car to keep it following a lane (2850 miles, 98% of the time) Medical diagnosis Logistics planning: DRAT (Dynamic Analysis and Replanning

Tool) Robotics: HipNav (a system that uses computer vision techniques

to create a 3-D model of a patient’s internal anatomy and then uses robotic control to guide the insertion of a hip replacement prosthesis.

Language understanding and problem solving: PROVERB (a computer program that solves crossword puzzles better than most humans)