Transcript Ch1 - shilepsky.net

CS 385 Fall 2006
Chapter 1
AI: Early History and Applications
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Where are We Going?
What is AI?
What is intelligence?
Our focus:
– representation of knowledge
• grades in a matrix
• a sentence in a parse tree
– exploitation of the representation (often via search)
• find the averages
• interpret what the sentence means
Method
– learn some important abstractions
– logic programming in PROLOG to implement the ideas
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Working Definition of AI
AI is the collection of problems and methodologies studied
by artificial intelligence researchers
• Recursive
• Actually, not a bad definition
• As such, a body of problems and techniques
• Best way to understand AI is to study them
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Part 1 Early History and Applications
1.1 From Eden to Eniac: Attitudes towards Intelligence,
Knowledge, and Human Artifice
– a chance to put this course in the context of what else you know
– recurring theme in our literature and mythology:
intellectual ambition → disaster
(Eve, Prometheus, Frankenstein)
– does this relate to popular views of AI?
1.2 Overview of AI Application Areas
– rest of the course: build the tools to attack some of these areas
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Back to 1.1
1.1.1 Historical Foundations.
A good, but dense, discussion of the development of
western thought.
(Who were Aristotle, Copernicus, and Descartes?)
1.1.2 AI and the Rationalist and Empiricist Traditions
• rationalism: the world can be described mathematically
– early rationalist bias in AI lead to sterile, mechanical systems
that couldn't "think."
• empiricism: we only know what we see
– how to represent knowledge here: association of related ideas
synthesis of the two: stochastic modeling and associative
theories
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1.1 (cont.)
1.1.3 The Development of Formal Logic
• 20th century: computers meant that formal reasoning as
in predicate calculus could be mechanized on a
computer and AI could develop
1.1.4 The Turing Test
1.1.5 Biological and Social Models
• Criticisms of the rational/logical approach to AI (GOFAI)
• New models of intelligence
– biological inspired by genetic evolution (how we adapt and
grow)
– social inspired by social organizations (e.g. # of loaves of bread
in NYC)
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The Turing Test
Eliza
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1.2: AI Application Areas
1.2.1 Game Playing
1.2.2 Automated Reasoning and Theorem Proving
1.2.3 Expert Systems
1.2.4 Natural Language Understanding and Semantic
Modeling
1.2.5 Modeling Human Performance
1.2.6 Planning and Robotics
1.2.7 Languages and Environments for AI
1.2.8 Machine Learning
1.2.9 Alternative Representations: Neural Nets and
Genetic Algorithms (very confusing section)
1.2.10 AI and Philosophy
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Important Features of Artificial Intelligence:
1. The use of computers to do reasoning, pattern recognition,
learning, or some other form of inference.
2. A focus on problems that do not respond to algorithmic solutions.
This underlies the reliance on heuristic search as an AI problemsolving technique.
3. A concern with problem solving using inexact, missing, or poorly
defined information and the use of representational formalisms
that enable the programmer to compensate for these problems.
4. Reasoning about the significant qualitative features of a situation.
5. An attempt to deal with issues of semantic meaning as well as
syntactic form.
6. Answers that are neither exact nor optimal, but are in some sense
“sufficient.” This is a result of the essential reliance on heuristic
problem-solving methods in situations where optimal or exact
results are either too expensive or not possible.
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Important features of Artificial Intelligence:
7. The use of large amounts of domain-specific knowledge in solving
problems. This is the basis of expert systems.
8. The use of meta-level knowledge to effect more sophisticated
control of problem solving strategies. Although this is a very
difficult problem, addressed in relatively few current systems, it is
emerging as an essential area of research.
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Last Parag in 1.3:
Some of the discussion suggests that straight rationalism
is insufficient for AI and that
• objects take on a meaning through their relationships
with other objects.
• This is equally true of the facts, theories, and techniques
that constitute a field of scientific study.
The facts/methods we will learn in this course will help us
develop an understanding of the overall substance and
directions of the field.
Text in italics taken from Luger, p. 31.
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What You should Get from this Course
Important models for abstraction
– Many of these are creeping into "real" CS
– AI identity crisis: once something can be done, it is not
considered AI
Introduction to the logic language PROLOG
A sense of what the field is
Interesting musings about intelligence and thinking
How does this fit with the rest of your CS education?
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