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Transcript
An Introduction to Artificial
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Introduction
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Getting machines to “think” .
Imitation game and the Turing test.
Chinese room test.
Key processes of AI:
– Search, e.g. breadth first search, depth first
search, heuristic searches.
– Knowledge representation, e.g. predicate
logic, rule-based systems, semantic
networks.
Areas of AI
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Game playing
Theorem proving
Expert systems
Natural language processing
Modeling human performance
Planning and Robotics
Neural-networks
Evolutionary algorithms and other
biologically inspired methods
• Agent-based technology
Game Playing
• Getting the computer to play certain
board games that require “intelligence”,
e.g. chess, checkers, 15-puzzle.
• A state space of the game is developed
and a search applied to the space to
look ahead.
• Example: Deep blue vs. Kasparov.
.
Theory Proving
• Automatic theorem proving.
• Generate proofs for simple
theorems.
• Mathematical logic forms the basis
of these systems.
• The “General Problem Solver” is one
of the first systems.
.
Expert Systems
• Performs the task of a human
expert, e.g. a doctor, a psychologist.
• Knowledge from an expert is stored
in a knowledge base.
• Examples: ELIZA, MYCIN, EMYCIN
• Suitable for specialized fields with a
clearly defined domain.
.
Natural Language Processing
• Develop systems that are able to
“understand” a natural language
such as English.
• Voice input systems, e.g. Dragon.
• Systems that “converse” in a
particular language.
• Examples: SHRDLU and ELIZA
.
Modeling Human Performance
• Systems that model some aspect
of problem solving.
• Examples: Intelligent tutoring
systems that provide
individualized instruction in a
specific domain.
.
Planning and Robotics
• Involves designing flexible and
responsive robots.
• Lists of actions to be performed
are generated.
• Aimed at high-level tasks, e.g.
moving a box across the room.
• Has led to agent-oriented
problem solving.
Neural Networks
• Aimed of low-level processing.
• Are essentially mathematical models of
the human brain.
• A neuron:
Dendrites
Cell Body
Axon
.
Synapse
Evolutionary Algorithms & Other
Nature-Inspired Algorithms
• Based on Darwin’s theory of evolution.
• An initial population of randomly created
individuals is iteratively refined until a
solution is found.
• Examples: genetic algorithms, genetic
programming, memetic algorithms
• Other methodologies: ant colonization,
swarm intelligence.
.
Uncertainty Reasoning
• Uncertain terms may need to be
presented.
• Example: representing terms such as
“big” or “small”.
• Methods for this purpose:
–Fuzzy logic
–Bayesian reasoning and networks
.
Agent-based Technology
• Intelligent agents, also called
“softbots”, are used to perform
mundane tasks or solve
problems.
• In a multi-agent system agents
communicate using an agent
communication language.
.
Artificial Intelligence Languages
• Programming paradigms
• Artificial intelligence languages – Prolog
and Lisp
• Prolog (Programming Logic) – declarative
– predicate logic
• Lisp (List Processing) – functional – code
takes the form of recursive functions.
• More recently AI systems have been
developed in a number of languages
including Smalltalk, C, C++ and Java.