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[Ron Sun]

Hybrid Connectionist-Symbolic Models: 
a report from the IJCAI'95 workshop on connectionist-symbolic integration

 Ron Sun
Department  of Computer Science
The University of Alabama
Tuscaloosa, AL 35487

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To appear in: AI Magazine, 1996.
9 pages.

ftp or Mosaic access:
ftp://cs.ua.edu/pub/tech-reports/sun.ai-magazine.ps

sorry, no hardcopy available.
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{\it The IJCAI Workshop on Connectionist-Symbolic Integration: From 
Unified to Hybrid Approaches} was held for two days
during August 19-20  in Montreal, Canada, in conjunction with the 
Fourteenth International Joint
Conference on Artificial Intelligence (IJCAI'95). 
The workshop was co-chaired by Ron Sun and Frederic Alexandre.
During the two days of the workshop, various presentations and discussions 
brought to light many new ideas, controversies, and syntheses.
The focus was on learning and architectures that feature hybrid representations
and support hybrid learning.

Hybrid models involve a variety of different types of processes and
representations, in both  learning and performance.
Therefore, multiple mechanisms interact in complex ways in most models.
We need to consider seriously ways of structuring these different components,
which thus occupy a clearly more prominent place in this area of research.
The hybridization of connectionist and symbolic models also
inherits the difficulty with learning from the symbolic 
side, and mitigates to some large extent the advantage that the purely 
connectionist models have in their learning abilities.
Considering the importance of learning, in both modeling cognition and
building intelligent systems, it is  crucial for researchers  in this area
to pay more attention to ways of enhancing hybrid models
in this regard  and to putting  learning back into hybrid models.