artificial intelligence ai topics history and overview

Artificial Intelligence

AI TopicsHistory and OverviewMachine LearningGames and AIThe Turing testComputer Vision

AI PioneersAlan M. Turing

“Computing Machinery and Intelligence”Marvin Minksy

Constructed the first neural net machineHerbert Simon, Allen Newell, J.C. Shaw

Developed the first AI computer program

AI Terms Artificial Intelligence: The capability of a machine to

imitate intelligent human behavior Artificial Neural Network: A network of neurons with

connections of varying strength Fuzzy Logic: A superset of Boolean logic which includes

truth values between true and false Knowledge Base: A collection of knowledge expressed

using some formal knowledge representation language AI-complete: Describes a problem which presupposes a

solution to the “strong AI problem”

Famous AI Programs ELIZA (Joseph Weizenbaum)

Psychologist Deep Blue (IBM)

Chess program Cyc (MCC and Cycorp)

Multi-contextual knowledge base and inference engine

HAL (Arthur C. Clarke) Space explorer

Machine Learning

What Is Machine Learning?Enabling machines to process data in

such a way that it can be to make future decisions

ML been studied for many yearsML has many applications in a variety of

fields

Methods of LearningGenetic algorithm Inductive logicComputational learning

Dimensions of Study Representation of experience

Most learning is based on experience Storage values

Attribute values (length) Binary values (yes/no)

Relations (Difficult)

Representation of acquired knowledge Generalizations Logical/discrete vs. numeric/continuous

Dimensions of Study Supervised and unsupervised learning

Supervised Feedback given immediately after an action is taken Easy to give examples of correct vs. incorrect behavior

Unsupervised Machine learns on its own with no conditioning

Inductive learning vs. analytic learning Inductive – take all data, make generalizations Analytic – offer explanations for new data based

on previous data, then simplify

Dimensions of Study Incremental vs. Non-Incremental

Learning Incremental

Examine results one-by one Less information retained, but faster

Non-Incremental Examine all results at once More information retained, but slower

Tasks For MachinesPattern recognitionGrouping/classification

Create general descriptions for classes of instances

StrategizingGenerating heuristicsProblem solving

Problem Solving Take a similar problem with a known solution

and try to find the answer (analogies) Simplify the problem and find a solution that

can be used to solve the main problem Thresholds Decision trees Macro-operators (AND, OR)

Issues in Machine LearningComputational complexityEthicsCorrectness

Would the exact desired learning be constructed?

What if there is an error in learning?

Games AIMin-Max Trees

Builds a level of maximizing moves followed by a level of minimizing moves

Uses evaluate functions to analyze situation

Alpha Beta Trees Like Min-Max Trees Discards paths it knows to be useless

Chess AlgorithmsMost use Alpha-Beta trees to make

movesTrees helped by additional knowledge

Transposition Tables Endgame Database Human Literature

Deep Blue First championship caliber chess player

Other GamesOthello – Logistello

Deep search algorithm Can solve most endgames Large opening book

Checkers – Chinook Extremely deep search depth 8 piece endgame database

The Turing TestMotivated to identify intelligence in a

computer program.Proposed in 1950 by Alan Turing.Original Proposal:

Given a person X, a computer Y, and an interrogator C, C isolated from X and Y.

C must determine who is the person X is intelligent if it can fool C.

Problems with the Turing Test Intelligence may be considered as a

continuum. The Turing test only identifies one (very strong) type of intelligence, and thus offers no means to measure.

Does fooling C really imply intelligence?

Our ProposalMotivated to allow:

a measure of intelligence. more rigid definitions. more flexible admission of programs.

Our ProposalDefine D as the set of all problems.

This may be restricted for practical considerations.

P(D) is therefore the partially ordered set (under inclusion) of all subsets of problems.

Our ProposalLet R be the set of all responsesP(R) is therefore the partially ordered

set of subsets of R.Define the Turing Test T as a function

between P(D) and P(R).Those programs which mimic T on

some subset X (pre-image) of P(D) are said to pass T restricted to X.

Our ProposalAs P(D) is partially ordered, and by the

way D was defined, there are several maximal elements Mi in P(D).

A program that is said to pass T restricted to an Mi is said to be an expert in Mi.

In specific applications, one may identify an expert program as intelligent.

ExamplesConsider the set of Arithmetic Problems If a program can solve these problems,

it is said to pass T restricted to Arithmetic Problems.

In practice, one would need to restrict this set.

ExamplesThe set of all Math Problems is a

maximal element. If a program can solve these problems,

it is said to be an Expert in Math Problems.

Sources Encyclopedia of Artificial Intelligence 2nd ed. Ed. Stuart C. Shapiro. John Wiley & Sons,

Inc. New York City, NY, 1992. R. Miikkulainen and D. Moriarity. Discovering Complex Othello Strategies Through

Evolutionary Neural Networks. University of Texas, USA, 1995. B. Moreland. Basic Search Techniques.

http://www.seanet.com/~brucemo/topics/topics.htm. USA, 2001. J. Schaffer. The Games Computers (And People) Play. University of Alberta, Canada,

2000