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From: "Pei Wang" <pwang@cs.indiana.edu>
Subject: PhD Thesis on intelligent reasoning available
Message-ID: <199512171928.OAA09203@moose.cs.indiana.edu>
Organization: Computer Science, Indiana University
Date: Sun, 17 Dec 1995 14:28:18 -0500 (EST)
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        Non-Axiomatic Reasoning System 
        --- Exploring the essence of intelligence

			Pei Wang

		    Indiana University
		 Bloomington, Indiana, USA

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The ~180 page thesis is available on the web at URL:
http://www.cogsci.indiana.edu/farg/peiwang/papers.html

It may also be retrieved through anonymous ftp to ftp.cogsci.indiana.edu
as the file pub/wang.thesis.ps.gz (~430K).  Relevant information is in
the file pub/wang.README

Comments and questions are welcome.

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			ABSTRACT

Every artificial-intelligence research project needs a working
definition of ``intelligence'', on which the deepest goals and
assumptions of the research are based. In the project described in the
following chapters, ``intelligence'' is defined as the capacity to
adapt under insufficient knowledge and resources.  Concretely, an
intelligent system should be finite and open, and should work in real
time.

If these criteria are used in the design of a reasoning system, the
result is NARS, a non-axiomatic reasoning system.

NARS uses a term-oriented formal language, characterized by the use of
subject--predicate sentences. The language has an experience-grounded
semantics, according to which the truth value of a judgment is
determined by previous experience, and the meaning of a term is
determined by its relations with other terms. Several different types
of uncertainty, such as randomness, fuzziness, and ignorance, can be
represented in the language in a single way.

The inference rules of NARS are based on three inheritance relations
between terms. With different combinations of premises, revision,
deduction, induction, abduction, exemplification, comparison, and
analogy can all be carried out in a uniform format, the major
difference between these types of inference being that different
functions are used to calculate the truth value of the conclusion from
the truth values of the premises.

Since it has insufficient space--time resources, the system needs to
distribute them among its tasks very carefully, and to dynamically
adjust the distribution as the situation changes. This leads to a
``controlled concurrency'' control mechanism, and a ``bag-based''
memory organization.

A recent implementation of the NARS model, with examples, is
discussed.  The system has many interesting properties that are shared
by human cognition, but are absent from conventional computational
models of reasoning.

This research sheds light on several notions in artificial
intelligence and cognitive science, including symbol-grounding,
induction, categorization, logic, and computation. These are discussed
to show the implications of the new theory of intelligence.

Finally, the major results of the research are summarized, a
preliminary evaluation of the working definition of intelligence is
given, and the limitations and future extensions of the research are
discussed.

---------------------------- Pei Wang ---------------------------
Center for Research on Concepts and Cognition, Indiana University
     510 North Fess Street, Bloomington, Indiana 47408, USA
Office Phone: (812) 855-6965 	 E-mail: pwang@cogsci.indiana.edu
