artificial intelligence ai topics history and overview
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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