AAAI SYMPOSIUM ANNOUNCEMENT

[2nd account for S.Kasif]

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                     C A L L   F O R   P A P E R S

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	LEARNING COMPLEX BEHAVIORS IN ADAPTIVE INTELLIGENT SYSTEMS 

                           AAAI Fall Symposium
                           November 9-11, 1996
                      Cambridge, Massachusetts, USA


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                      Submissions due April 15, 1996  

 		      See the symposium home page at
	    http://www.cs.jhu.edu/complex/symposium/cfp.html

Call for Papers

The machine learning community made an important methodological transition 
by identifying a collection of benchmarks that can be used for comparative
testing of learning (typically classification) algorithms.  While the resulting
comparative research contributed substantially to progress in the field, a
number of recent studies have shown that very simple representations, such as
depth-two decision trees, naive Bayes-classifiers or perceptrons, perform
relatively well on many of the benchmarks which are typically static
fixed-size databases.  At the same time, when knowledge representations are
hand-crafted for solving complex tasks they are typically rather large and are
often designed to cope with complex dynamic environments.

This symposium will attempt to bridge this gap by increasing the focus of the
meeting towards the study of algorithms that learn to perform complex
behaviors and cognitive tasks such as reasoning and planning with uncertainty,
perception, natural language processing and large-scale industrial
applications.  An additional important subgoal is emphasizing scalability of
learning algorithms (e.g. reinforcement learning) in these complex
domains.  Our main motivation is to have an interdisciplinary meeting that
focuses on "rational" agents that learn complex behaviors which is closer in
spirit to the goals of AI than learning simple classifiers.  We expect to draw
selected researchers from AI, Neural Networks, Machine Learning, Uncertainty,
and Computer Science Theory.

Some of the key issues we plan to address are: 

 * Research on agents that learn to behave "rationally" in complex
   environments.

 * Discovering parameters that can be used to measure the empirical
   complexity of learning a complex domain. 

 * Generating new benchmarks and devising a methodological framework for
   studying empirical scalability of algorithms that learn complex
   behaviors. 

 * Broadening the focus of learning to achieve a particular functionality in
   response to the demands generated by the domain, rather than learning a
   particular representation (e.g. learning to answer queries of the form:
   "what is the probability of X given Y" may be easier than learning a
   complete probability distribution on n variables). 

 * Discussing the hypothesis that current learning algorithms require
   substantial knowledge engineering and close familiarity with the problem
    domain in order to learn complex behaviors. 

 * Scalability of different representations and learning methods. 

The symposium will consist of invited talks, submitted papers, and panel
discussions on topics such as 
 * learning complex i/o behaviors; 
 * learning optimization and planning; 
 * learning to reason; 
 * learning to reason with uncertainty; and 
 * learning to perform complex cognitive tasks.
We will invite short technical papers on these issues as well as position
papers relating learning and issues in knowledge representation; comparative
papers that illustrate the capabilities of different representations to
achieve the same functionality; and papers providing specific benchmarks that
demonstrate the scalability of a particular representation or paradigm.

SUBMISSION INFORMATION

Prospective participants are encouraged to submit extended abstracts (5-8
pages) addressing the research issues above.   Please refer to an extended
version of the call for papers that provides additional submission information
and a tentative program (available on the WEB at:
http://www.cs.jhu.edu/complex/symposium/cfp.html).  Electronic submissions as
well as inquiries about the program should be sent to complex at cs.jhu.edu.


IMPORTANT DATES

Submissions must be received by: 15 April 1996

Notification of acceptance on or before: 17 May 1996

Camera-ready copy for working notes due: 23 Aug 1996


ORGANIZING COMMITTEE

S. Kasif (co-chair), Johns Hopkins Univ.; S. Russell (co-chair), Berkeley;
B. Berwick, MIT; T. Dean, Brown Univ. R.  Greiner, Siemens Research;
M. Jordan, MIT; L. Kaebling, Brown Univ.; D. Koller, Stanford Univ.; 
A. Moore, CMU; D. Roth, Weizmann Institute;

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Fall Symposia are sponsored by the American Association for Artificial
Intelligence (AAAI).  More information about the Fall Symposium on
"LEARNING COMPLEX BEHAVIORS" can be found at:

	  http://www.cs.jhu.edu/complex/symposium/cfp.html