No subject

Ron Sun
Edward Merrill
Todd Peterson

To appear in: Cognitive Science, Vol.25, No.2.  March 2001.

http://www.cecs.missouri.edu/~rsun/sun.CS99.ps
http://www.cecs.missouri.edu/~rsun/sun.CS99.pdf


ABSTRACT
This paper  presents a skill learning model CLARION.  Different from
existing models of mostly high-level skill learning that use a top-down
approach (that is, turning declarative knowledge into procedural knowledge
through practice), we adopt a bottom-up approach toward low-level skill 
learning, where procedural knowledge develops first and
declarative knowledge develops later.  Our model is formed
by integrating connectionist, reinforcement, and symbolic learning
methods to perform on-line reactive learning.  It adopts a two-level 
dual-representation framework (Sun 1995), with a combination of localist 
and distributed representation.  We compare the model with human data in 
a minefield navigation task, demonstrating some  match between the model 
and human data in several respects.




A new paper on consciousness:
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Computation, Reduction, and Teleology of Consciousness 

Ron Sun

To appear in:  {\it Cognitive Systems Research}, Vol.1, No.4, 2001.

http://www.cecs.missouri.edu/~rsun/sun.jcsr-cons10.ps
http://www.cecs.missouri.edu/~rsun/sun.jcsr-cons10.pdf



ABSTRACT
This paper aims  to explore mechanistic and teleological explanations 
of consciousness.  In terms of mechanistic explanations, it
critiques various existing views, especially those
embodied by existing computational cognitive models.
In this regard, the paper  argues in favor of the explanation
based on the distinction between localist (symbolic) representation and
distributed representation (as formulated  in
the connectionist literature), which reduces the phenomenological
difference to a mechanistic difference.  Furthermore, to establish a
teleological explanation of consciousness,
the paper  discusses the issue of the functional role of consciousness
on the basis of the afore-mentioned mechanistic explanation.
A proposal based on synergistic interaction between the conscious and 
the unconscious is advanced that encompasses various existing views 
concerning the functional roles of consciousness.
This two-step deepening explanation has some empirical support,
in the form of a cognitive model and various cognitive data that it captures.


Also, a previous paper on accounting for consciousness computationally:
--------------------------------------------------
Accounting for the Computational Basis of Consciousness: A Connectionist
Approach

Ron Sun

Appeared in: Consciousness and Cognition, 1999.

http://www.cecs.missouri.edu/~rsun/sun.CC99.ps
http://www.cecs.missouri.edu/~rsun/sun.CC99.pdf


ABSTRACT
This paper argues for an explanation of the mechanistic 
(computational) basis of consciousness that is based on the
distinction between localist (symbolic) representation and
distributed representation, the ideas of which have been put 
forth in the connectionist literature.  A model is developed 
to substantiate and test this approach.  The paper also explores 
the issue of the functional roles of consciousness,
in relation to the proposed mechanistic explanation of consciousness.
The model,  embodying the representational difference, is able
to account for the functional role of consciousness, in the form 
of the synergy between the conscious and the unconscious.
The fit between the model and various cognitive phenomena and data
(documented in the psychological literatures) is discussed to 
accentuate the plausibility of the model and its explanation of 
consciousness.  Comparisons with existing models of consciousness 
are made in the end.


-----------------------------------------------------------------
Symbol Grounding: A New Look At An Old Idea

by Ron Sun

Appeared in:  Philosophical Psychology, Vol.13, No.2, pp.149-172.  2000.

http://www.cecs.missouri.edu/~rsun/sun.PP00.ps
http://www.cecs.missouri.edu/~rsun/sun.PP00.pdf



ABSTRACT

Symbols should be grounded, as has been argued before.
But we insist that they should be grounded not only in subsymbolic
activities, but also in the interaction between the agent and the
world.  The point is that concepts are not formed
in isolation (from the world), in abstraction, or ``objectively".
They are formed in relation to the experience of agents,
through their perceptual/motor apparatuses, in their world and
linked to their goals and actions.
In this paper, we will take a detailed look at this relatively old issue,
using a new perspective, aided by our work  of computational cognitive
model development.









Finally,  a previous paper on computational aspects of the model:
---------------------------------
Autonomous Learning of Sequential Tasks: Experiments and Analyses

by Ron Sun, Todd Peterson

Appeared in:  IEEE Transactions on Neural Networks, Vol.9, No.6, pp.1217-1234.  November, 1998.

http://www.cecs.missouri.edu/~rsun/sun.tnn98.ps


ABSTRACT:
This paper presents  a novel learning model  CLARION, which is a 
hybrid model based on the two-level approach proposed in Sun (1995).
The model integrates neural, reinforcement, and symbolic learning 
methods to perform on-line,  bottom-up learning (i.e.,
learning that goes from neural to symbolic representations).
The model utilizes both procedural and declarative knowledge
(in neural and symbolic representations respectively),
tapping into the  synergy of the two types of processes.
It was applied to deal with sequential decision tasks.
Experiments and analyses  in various ways are reported 
that shed light on the advantages of the model.


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Prof. Ron Sun                           http://www.cecs.missouri.edu/~rsun
CECS Department                         phone: (573) 884-7662
University of Missouri-Columbia         fax:   (573) 882 8318 
201 Engineering Building West
Columbia, MO 65211-2060                 email: rsun at cecs.missouri.edu 

http://www.cecs.missouri.edu/~rsun
http://www.cecs.missouri.edu/~rsun/journal.html
http://www.cecs.missouri.edu/~rsun/clarion.html
===========================================================================