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>From: dietrich@bingsuns.cc.binghamton.edu (dietrich)
Subject: Jetai 4.1
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Organization: State University of New York at Binghamton
Date: Fri, 29 May 1992 20:04:26 GMT

Journal of Experimental and Theoretical Artificial Intelligence
  Volume 4, number 1.

  Here are the abstracts from issue 4.1.  Also in this issue we
announced our new editorial structure.  We have six new associated
editors: Jack Adams-Weber (Psychology, Brock U., St. Catharines,
Ontario), Ken Ford (CS, Univ. West Florida), Clark Glymour,
(Philosophy, Carnegie Mellon Univ.), Pat Hayes (CS, Stanford), Jordan
Pollack (CS, Ohio State), and Jozef Kelemen (AI, Comenius Univ.,
Bratislava, Czechoslovakia).  These editors will help me with
publishing decisions.


1.
Connectionism and functionalism: the importance of being a subsymbolist

    Marcello Frixione and Giuseppe Spinelli
    Department of Communications, Computer and Systems Science
     University of Genoa, Via Opera Pia 11A, 16145 Genoa, ITALY
    Dept. of Philosophy, Univ. of Genoa

Abstract.  In recent years the development of connectionist theories and of
various subsymbolic approaches to the study of the mind, and the renewed
interest in the relations between the study of the mind and the
neuroscience have had significant repercussions on the philosophical
foundations of artificial intelligence and cognitive science, and on
important questions of the philosophy of mind.  Various approaches to the
problem of mental representations have been formulated, in some sense
alternative to classic approaches of artificial intelligence and cognitive
science.  We suggest that the problem of modeling the reference of mental
symbols from a cognitive point of view requires the abandonment of a purely
symbolic approach, and the adoption of a subsymbolic level of
representation.  Some philosophical consequences of a subsymbolic level of
this kind are discussed.  After distinguishing between the problem of
reference and that of intentionality (which cannot be solved positing a
subsymbolic level of representation), we shall see how a subsymbolic
approach can be compatible with a functionalist view of the mind, in the
wider sense.  Finally, some consequences of subsymbolic models of reference
regarding the problem of the inverted spectrum are described.

2.
On the semantics of inheritance networks

  Yannis Dimopoulos
  Department of Informatics, Athens University of Economics and Business
  76 Patission Street, Athens 104 34, GREECE

Abstract.  A semantics for inheritance reasoning is presented which allows
both strict and defeasible knowledge to be represented.  The approach
proposed considers the semantics as consisting of two parts:  the content
theory which describes the knowledge about the world and a "process model"
which explains how the knowledge in the network is processed.  Each process
model is expressed by an algorithm which, given two sets of properties for
each class or individual, computes a new set of properties.  It is argued
that the "clash of intuitions" which appears in the literature on
inheritance reasoning, is a clash of process models, as, in certain
situations, different process models give different meaning to the links of
a network.  An attempt is also made to explain the instability of an
inheritance reasoning system.  It is argued that instability should not be
surprising if the extra meaning that the process model assigns to a network
is taken into account.  This is because, in certain process models, no link
in a network is considered as redundant.  Some general questions on
inheritance reasoning are finally raised.

3.
Analysis system for Sinhalese unit structure

  S. Herath*, T. Ikeda^, S. Ishizaki#, Y. Anzai%, and H. Aiso%
  *Southwest Texas State University, San Marcos, Texas 78666
  #Electrotechnical Lab, Tsukuba-shi, Japan
  %Keio Univ.  Yokohama, Japan
  ^Gifu Univ. Japan

Abstract.  Sinhalese is the major language in Sri Lanka, spoken by 15
million people.  It has never been analyzed by a computer.  The paper
describes an original computational linguistic analysis for Sinhalese.  A
machine representation of Sinhalese script is developed.  It provides an
easy means of input and facilitates the formalization of the linking
phenomena.  An analysis system for Sinhalese morphology is developed.  A
Sinhalese sentence consists of a few units separated by spaces.  The unit
structure is formalized as a root and suffixes.  Connection rules and
linking rules are developed.  The grammatical features of a unit are
characterized by a set of attributes, and a mechanism to compute these
attributes is developed from the features of the root and suffixes.  The
unit structure involves much important grammatical information such as case
and attributes.  The analyser can handle any kind of Sinhalese unit
efficiently.  The system will be used as the base for machine processing of
Sinhalese, its syntactic and semantic analysis, and other applications.


4.
Abstract symbol systems (an exercise of the bottom-up approach in
artificial intelligence)

  Juraj Hromkovic, Jozef Kelemen, Juraj Waczulik
  Department of Computer Science, Dept. of AI, Institute
	of Comp. Sci., Comenius University,
   842 15 Bratislava, CZECHOSLOVAKIA

Abstract.  Instead of some ad hoc reductions of the intellectual capacities
of human beings an alternative approach to artificial intelligence is
suggested that consists of the expansion of the capabilities of well-known
(theoretical models of) symbol systems.  By some computationally
realistic axioms, an abstract computational device -- the abstract symbol
system -- is defined.  Then some specific types of the abstract symbol
system are defined, namely the static and the dynamic ones, and the
corresponding computational powers and complexities are examined and
compared.  A formal proof is given that a kind of abstract symbol system --
the dynamic symbol system -- can execute in real time (after some training
and some increasing of its own architectural complexity) all Turing machine
computations.


5. 
Handling noise in EBL: an abductive approach

  Ganesh Mani
  Computer Sciences Department, University of Wisconsin-Madison,
  1210 W. Dayton Street, Madison, WI 53706

Abstract.  Noise is a problem in any real-world domain.  A system's
sustained good performance in such domains hinges on its ability to handle
noise gracefully.  Explanation-based learning (EBL) systems typically
ignore noise because of the insistence on generating deductive proofs.  Two
algorithms to handle noise in an EBL framework, using an abductive
approach, are discussed.  The first algorithm, using a noise-free domain
theory and noise-free training examples, attempts to correct noisy test
examples by matching them against the existing, operational concept.  The
second algorithm, based on an inductive-statistical approach, tries to
recover from the effect of noisy training examples by aggregating evidence
from the test examples.  These algorithms have been validated using
examples from two markedly different domains.




