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artifi[e86,jmc]	Artificial Intelligence for Hopcroft study

	Artificial intelligence (AI) is the name of a branch of science
and also the corresponding technology.  Its main scientific problem is the
characterization of intelligent behavior as a computational process.  This
also involves the creation of computer programs that exhibit intelligent
behavior or, in some approaches, the creation of hardware that exhibits
intelligent behavior.

	Sustained scientific work on AI dates from the 1950s with the
Dartmouth Summer Project on AI and the formation of research groups at
Carnegie-Mellon University and M.I.T.  In the 1960s these were joined by
groups at Stanford University, SRI International and the University of
Edinburgh.  More recently AI research has spread throughout the U.S.  and
the world.  In the 1970s the basic research work was joined by efforts
primarily motivated by applications --- the development of {\it expert
systems}.

	Several approaches to AI have been pursued.

	1. It is possible to begin with human psychology --- making experiments
on human problem solving and attempting to characterize the
intellectual processes used by the subjects.  Newell and Simon
pioneered here.

	2. It is possible to begin with physiology and to work with
networks of neurons.

	3. It is possible to begin with an objective study of the
common sense world and the problems it presents for a being with
given kinds of goals and given observational and computational resources.
This approach subdivides according to whether logic as a means of describing
what is known about the world is emphasized or whether one
regards knowledge as essentially procedural to be described directly
in some kind of programming language.

	In this connection it should be noted that AI research has
pioneered flexible programming languages beginning with the Newell
and Simon IPLs, and continuing with McCarthy's LISP and Colmerauer's
Prolog.

	4. Some AI researchers have based their work on beginning
with an initially unintelligent machine or program that learns to
be more intelligent by experience and training.

	The most steadily active approaches have been the psychological
approach and the objective problem-solving approach.  While the other
approaches have met difficulties which have discouraged some researchers,
no-one have proved them necessarily unfruitful, and they are revived
from time to time by people with new ideas for implementing them.

*****
lead in to logic and vision
jeh@gvax.cs.cornell.edu,nilsson@score/cc
section for report
Here it is finally.
Artificial Intelligence as a branch of computer science

	Artificial intelligence is concerned with making machines,
especially computers, behave intelligently.  As a branch of computer
science, it suffers from the fact that what constitutes intelligent
behavior is still not fully defined.

	Indeed the name ``artificial intelligence'' arose in 1955 in
connection with a proposal for a summer study that was held
at Dartmouth College in the summer of 1956.  The reason for choosing
such an ambitious name was that a previous effort, a call for
papers which it was hoped would elicit papers in the area had
been called more modestly {\it Automata Studies} had elicited
papers defining automata theory --- a branch of mathematics.

	Even today, people who start with the idea of doing artificial
intelligence find the lack of definition frustrating and tend
to veer off either into some related topic admitting a more
mathematical treatment at the present state of science or
into purely empirical experimental programming.

	However, if computer programs with human level intelligence
are to be achieved, it is necessary to study what intelligence is
even though the problems are ill-defined.

	The problems have been attacked on several levels.

	1. The human and animal physiological level.  Starting with
studies of the neuron, people have asked what the assemblies of
neurons in the nervous system do.  In parallel, others ask what
assemblies of mathematically defined ``neurons'' can be constructed
to do.  Pattern recognition has been the goal of most of this study.
Hardware has been built or simulated by computer programs.

	2. Others have taken a psychological approach.  The behavior
of humans and animals in problem-solving situations is studied.
Hypothesized mechanisms are simulated in computers and whether
they exhibit the behavior and solve the problems is studied.  From
the beginning, the simulation programs have required large amounts
of internal state, and this is a major cause of the demise of
behaviorism which demanded theories that directly related stimulus
and response.

	3. Direct study of the relation between problems and their
solution methods.  This has been the most popular and successful
AI methodology.
The idea is that intelligent goal-achieving behavior can be studied
apart from the specifics of the way animals or humans do it.  From
this point of view AI is a branch of engineering or applied mathematics
akin to operations research or ``mathematical programming''.  It differs
fundamentally from those disciplines in that is concerned with the
treatment of problems before a delimited mathematical model has been
found for them.  AI programs may either establish a model and use
it or may treat the problem without ever establishing a delimited
model, i.e. without ever establishing what phenomena are to be
taken into account.  Post facto, i.e. after the system has decided
what to do, it can be asked what facts it did take into account.

	The direct treatments of problems may be regarded as
distributed along an axis.  One pole of the axis involves formulating
the facts as formulas in languages of mathematical logic and deciding
what to do by reasoning with these formulas.  Recently it has been
discovered that logical deduction is not the only reasoning method
required; that it must be supplemented by some form of non-monotonic
reasoning.  Various approaches to formalizing non-monotonic reasoning
are being explored.

	Towards the other end of the axis lies the current expert
system technology.  The extreme is just programming various solution
methods in so-called AI languages, e.g. Lisp or Prolog or the
proprietary systems like ART, KEE and OPS-5 using a variety of
techniques that have been found useful for expressing the specialized
knowledge of a domain expert and reasoning methods used by him.
It is common to use logical formulas in expressing the knowledge
but to use more-or-less ad hoc programs for manipulating it.  This
often achieves speed in specialized domains at the cost of generality.

	Apart from the schemas outlined above, AI researchers have
pioneered machine implementation of sophisticated sensory and motor
activities, specifically in robotics, speech recognition and computer
vision.  Each of these involves intellectual mechanisms that go
beyond the particular topic.  For example, computer vision involves
pattern matching problems that also occur elswhere in AI.

	Discussing the varied programs of AI research would be
too long for this report, so two subtopics have been chosen for
more detailed treatment --- AI formalisms using mathematical
logic and and computer vision.