Download G52HPA: History and Philosophy of Artificial Intelligence Lecture 1

Outline of this lecture
G52HPA:
History and Philosophy of Artificial
Intelligence
• some questions
• scope & content of the module
• structure of the module
Lecture 1: Module Overview
• assessment
Tony Pridmore & Natasha Alechina
School of Computer Science
• supplementary reading
tpp,[email protected]
• outline syllabus
© Brian Logan 2008
What is Artificial Intelligence?
G52HPA Lecture 1: Module Overview
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What is Artificial Intelligence?
• “The study of how to make computers do things at which, at the
moment, people are better” — (Rich and Knight, 1991)
• “The branch of computer science that is concerned with the
automation of intelligent behaviour” — (Luger and Stubblefield,
1993)
• “The study of mental faculties through the use of computational
models” — (Charniak and McDermott, 1985)
• “The exciting new effort to make computers think … machines with
minds, in the full and literal sense” — (Haugeland, 1985)
© Brian Logan 2008
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What is Artificial Intelligence?
© Brian Logan 2008
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AI as engineering
• many different definitions of AI
• views AI as part of Computer Science
• AI as engineering: a set of techniques which allow computer programs to do things
that would require intelligence if done by a human
• set of techniques for solving problems for which no algorithmic
solution is (currently) known
• AI as science
• how such programs relate to human intelligence is not of interest
– “Weak AI”: whether it is possible for a machine to simulate intelligence
– “Strong AI”: whether it is possible for a machine to actually be intelligent
– c.f. AI as engineering, where this sort of definitional question is of peripheral
interest
• we shall focus on Strong AI
© Brian Logan 2008
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© Brian Logan 2008
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AI as science
Weak AI
• Weak AI and Strong AI can interpreted as making claims about either:
• the Weak AI position is that even if we build a system that behaves as
though it is intelligent, it wouldn’t necessarily be intelligent
– specifically human intelligence; or
– possible for a machine to simulate human intelligence (including
the kinds of errors, mistakes etc. humans make) – c.f. Cognitive
Science
– “intelligence in the abstract” which can be applied to humans,
animals Martians – anything that could be intelligent (including
machines)
– more generally, any realisation of intelligent behaviour by a
machine is only a simulation of intelligence
© Brian Logan 2008
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Strong AI
© Brian Logan 2008
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Aims & scope of the module
• what is artificial intelligence?
• the Strong AI position is that intelligence is essentially a kind of
computation or information processing
• what is its relevance to philosophy (and what can philosophy tell us
about AI)?
– possible for a machine to actually be intelligent in more or less in
the same way as a person
• current state and future prospects for AI
– more generally, any system which does the right kind of
computation/information processing necessarily is intelligent
– some theoretical results (everything is “AI-complete”)
– clues from the history of AI—what has turned out to be easy and
what has turned out to be (surprisingly) hard
• may be many different ways of being intelligent (one of which is the
human way of being intelligent)
• focus on core topics from “classical” AI (GOFAI = symbol
manipulation)
© Brian Logan 2008
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History of AI
© Brian Logan 2008
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Knowledge representation
We shall focus on four key topics in artificial intelligence:
• representation of information in a computer program (or in hardware)
• knowledge representation
• often information about the external environment, but may be about
abstract things (mathematics, law etc.)
• reasoning
• representations are declarative—it’s possible to give a precise account
of what they mean which is independent of the operations performed
on them
• planning
• vision
while this list is not exhaustive, it is sufficient to give a good grounding in
“classical” AI
• allows the same information to be used in many different ways
• cf. datastructures in CS
© Brian Logan 2008
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© Brian Logan 2008
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Reasoning
Planning
• application of general rules to information to derive new information, e.g.,
“All men are mortal”, “Socrates is a man” therefore “Socrates is mortal”
• planning involves choosing a sequence of actions which, if executed,
will achieve some goal
• often considered together with knowledge representation (e.g., G53KRR)
• similar to reasoning, except that the results usually refer to possible
(rather than necessary) states of the world:
• however the same knowledge representation may support more than one kind
of reasoning, e.g., classical inference, default reasoning, reasoning by analogy
etc.
– if Fido is a dog, then Fido is necessarily a mammal
• reasoning procedure should be “truth preserving” (under some definition of
‘truth’)
– however catching a train is only one of several possible ways of
getting to the airport
• relies on explicit knowledge representations and may use reasoning to
infer the consequences of possible actions
• cf. algorithms in CS
© Brian Logan 2008
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Vision
© Brian Logan 2008
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Philosophy of AI
• Goal is to extract and represent information about the viewed scene
given (sets of) images of that scene - “To know what is where by
looking” (Aristotle). Vision may use but is not equal to image
processing.
• intentionality and representation
• reasoning and the frame problem
• planning and (responsible) agency
• Image intensity is a function of illumination, surface reflectance,
surface shape, viewpoint. Vision seeks to invert this function.
• vision and reality
• An infinite number of logically possible but improbable scenes may
generate a given image. Vision needs prior knowledge/assumptions.
• and many others …
• Multiple cues exist and can be exploited: stereo, motion, texture,
shading, colour…….
© Brian Logan 2008
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Structure of the module
© Brian Logan 2008
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Assessment
Assessment is by means of a report based on lecture material, directed
reading and research:
• lectures covering issues in the philosophy of AI
• directed reading: 3 key papers for each topic in the history of AI,
plus supplementary reading—each student is assigned a topic and
must read at least the key papers for that topic
• each student will be assigned a topic area and will be expected to read
the required papers for that topic (i.e., at least the three key papers)
and attend the tutorials
• group tutorials: discussion of key papers for each topic to understand
the relationship between topics (required)
• marks will be awarded for demonstrating a clear understanding of the
topic and clearly and accurately summarising the assigned papers
• individual tutorials: discussion of matters related to your particular
topic, choice of supplementary reading etc. (optional)
• extra credit will be given for drawing on additional (relevant) papers,
demonstrating an understanding of the relationships between topics
and/or developing the relationship to philosophical questions in more
detail
© Brian Logan 2008
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© Brian Logan 2008
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The report
Supplementary reading
The report should:
For an overview of the various AI sub-fields and philosophical questions
covered in module see:
• introduce the topic, explaining how it relates to AI in general and to
issues in the philosophy of AI
• Russell & Norvig (1995), Artificial Intelligence: A Modern Approach,
Prentice Hall, (or the 2002 2nd edition)
• summarise each of the assigned papers for the topic
• Dennett (1996), Kinds of Minds: Towards an Understanding of
Consciousness, Weidenfeld & Nicolson
• give an overview of the topic which includes progress to date, open
problems and likely future progress
Module web pages are at:
http://www.cs.nott.ac.uk/~nza/G52HPA/
• a full list of references cited in the report
• approximately 7,500 words (15 pages)
© Brian Logan 2008
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Outline syllabus
Week
Thursday 10-11
20
The next lecture
Lecture: Module overview
2 no tutorial
Lecture: Introduction to AI
3 Individual tutorials
Lecture: Representation &
Intentionality
4 Individual tutorials
Lecture: Reasoning & the Frame
Problem
5
Group tutorial on Paper I
6 Individual tutorials
Lecture: Planning and responsible
agency
7
Group tutorial on Paper II
8 Individual tutorials
Lecture: Vision and Reality
9
Group tutorial on Paper III
© Brian Logan 2008
G52HPA Lecture 1: Module Overview
Thursday 11-12 B11 Amenities
1 no tutorial
10
© Brian Logan 2008
Introduction to AI
Suggested reading:
• Russell & Norvig (2003), chapters 1, and 26
Note that the next lecture is at 11.00 on Thursday the 1st of October at
11am in room JC-AMEN-B11
Group tutorial on Papers I, II & III
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© Brian Logan 2008
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