CSC 480: Artificial Intelligence - An
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Transcript CSC 480: Artificial Intelligence - An
Introduction
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7 July 2015
ARTIFICIAL INTELLIGENCE
EXAMPLES OF DEFINITIONS OF AI
approaches
emphasis on the way systems work or “think”
Behavioral
approaches
only activities observed from the outside are taken into
account
Human-like
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Cognitive
systems
try to emulate human intelligence
Rational
systems
systems that do the “right thing”
idealized concept of intelligence
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SYSTEMS THAT THINK LIKE HUMANS
“[The automation of] activities that we associate
with human thinking, activities such as decisionmaking, problem solving, learning …”
[Bellman, 1978]
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“The art of creating machines that perform
functions that require intelligence when performed
by people”
[Kurzweil, 1990]
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SYSTEMS THAT THINK RATIONALLY
study of mental faculties through the
use of computational models”
[Charniak and McDermott, 1985]
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“The
“The
study of the computations that make
it possible to perceive, reason, and act”
[Winston, 1992]
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SYSTEMS THAT ACT RATIONALLY
field of study that seeks to explain and
emulate intelligent behavior in terms of
computational processes”
[Schalkhoff, 1990]
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“A
“The
branch of computer science that is
concerned with the automation of
intelligent behavior”
[Luger and Stubblefield, 1993]
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COGNITIVE MODELING
to construct theories of how the
human mind works
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Tries
Uses
computer models from AI and
experimental techniques from psychology
Most
AI approaches are not directly based
on cognitive models
often difficult to translate into computer programs
performance problems
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RATIONAL THINKING
on abstract “laws of thought”
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Based
usually with mathematical logic as tool
Problems
and knowledge must be
translated into formal descriptions
The
system uses an abstract reasoning
mechanism to derive a solution
Serious
real-world problems may be
substantially different from their abstract
counterparts
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RATIONAL AGENTS
agent that does “the right thing”
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An
it achieves its goals according to what it knows
perceives information from the environment
may utilize knowledge and reasoning to select actions
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BEHAVIORAL AGENTS
agent that exhibits some behavior
required to perform a certain task
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An
may simply map inputs onto actions
simple behaviors may be assembled into more
complex ones
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FOUNDATIONS OF ARTIFICIAL INTELLIGENCE
theories of language, reasoning, learning, the mind
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Philosophy
Mathematics
formalization of tasks and problems (logic, computation,
probability)
Linguistics
understanding and analysis of language
knowledge representation
Psychology
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FOUNDATIONS OF ARTIFICIAL INTELLIGENCE
CONT.
science
provides tools for testing theories
programmability
speed
storage
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Computer
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CONCEPTION (LATE 40S, EARLY 50S)
neurons (McCulloch and Pitts,
1943)
Learning
Chess
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Artificial
in neurons (Hebb, 1949)
programs (Shannon, 1950; Turing,
1953)
Neural
computer (Minsky and Edmonds,
1951)
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BABY STEPS (LATE 1950S)
of programs solving simple
problems that require some intelligence
Development
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Demonstration
of some basic concepts and
methods
Lisp (McCarthy, 1958)
formal methods for knowledge representation and
reasoning
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(EARLY 1960S)
Problem Solver (Newell and
Simon, 1961)
Shakey
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General
the robot (SRI)
Algebraic
problems (Bobrow, 1967)
Neural
networks (Widrow and Hoff, 1960;
Rosenblatt, 1962; Winograd and Cowan,
1963)
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(LATE 60S, EARLY 70S)
networks can learn, but not very
much (Minsky and Papert, 1969)
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Neural
Expert
systems are used in some real-life
domains
Knowledge
representation schemes
become useful
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AI GETS A JOB (EARLY 80S)
applications of AI systems
R1 expert system for configuration of DEC computer
systems (1981)
Expert
AI
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Commercial
system shells
machines and tools
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(LATE 80S)
all, neural networks can learn more
in multiple layers (Rumelhart and
McClelland, 1986)
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After
Hidden
Markov models help with speech
problems
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(90S)
AI
and speech recognition work
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Handwriting
is in the driver’s seat (Pomerleau, 1993)
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INTELLIGENT AGENTS APPEAR (MID-90S)
between hardware (robots) and
software (softbots)
Agent
architectures
Situated
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Distinction
agents
embedded in real environments with continuous
inputs
Web-based
agents
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CHAPTER SUMMARY
to important concepts and
terms
Relevance
Influence
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Introduction
of Artificial Intelligence
from other fields
Historical
development of the field of
Artificial Intelligence
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