AAAI Fall Symposium: LEARNING COMPLEX BEHAVIORS IN ADAPTIVE INTELLIGENT SYSTEMS

[Dr. Simon Kasif]

AAAI 1996 FALL SYMPOSIUM

LEARNING COMPLEX BEHAVIORS IN ADAPTIVE INTELLIGENT SYSTEMS

November 9--11, 1996

Additional registration Information and a copy of the registration 
forms can be found in http://www.aaai.org/Symposia/Fall/1996/


Program Committee:

Simon Kasif, (co-chair), University of Illinois-Chicago/Johns Hopkins Univ.
Stuart Russell,  (co-chair), University of California, Berkeley
Robert C. Berwick, Massachusetts Institute of Technology
Tom Dean, Brown University
Russell Greiner,  Siemens Corporate Research
Michael Jordan, Massachusetts Institute of Technology
Leslie Kaebling, Brown University
Daphne Koller, Stanford University
Andy Moore, Carnegie Mellon University 
Dan Roth, Weizmann Institute of Science and Harvard University.


ABSTRACT

The symposium will consist of invited talks, submitted papers, and
panel discussions focusing on practical algorithms and theoretical
frameworks that support learning to perform complex behaviors and
cognitive tasks.  These include tasks such as reasoning and planning
with uncertainty, perception, natural language processing and
large-scale industrial applications.

The underlying theme is the automated construction and improvement of
complete intelligent agents, which is closer in spirit to the goals of
AI than learning simple classifiers.  We expect to have an
interdisciplinary meeting with participation of researchers from AI,
Neural Networks, Machine Learning, Uncertainty in AI and Knowledge
Representation.

Some of the key issues we plan to address are: 

- Development of new theoretical frameworks for analysis of broader
  learning tasks such as learning to reason, learning to act, and
  reinforcement learning.
- Scalability of learning systems such as reinforcement learning.
- Learning complex language tasks.
- Research on agents that learn to behave ``rationally'' in 
  complex environments.
- Learning and reasoning with complex representations.
- Generating new benchmarks and devising a methodological framework 
  for studying empirical scalability of algorithms that 
  learn complex behaviors.
- Empirical and theoretical analysis of the scalability of different
  representations and learning methods.



TENTATIVE PROGRAM

**********SCHEDULE SUBJECT TO CHANGE**********

Saturday, November 9, 1996

Morning

9:00--10:30am

Opening Remarks
Simon Kasif (UIC and Johns Hopkins University) and Stuart Russell (Berkeley)


An Engineering Architecture for Intelligent Systems            (45 min)
   Jim Albus (NIST)
  

10:00--10:40am,  Reinforcement Learning

Temporal Abstraction in Model-Based Reinforcement Learning      (20 min)
   R. Sutton (U.Mass)

Hierarchical Reinforcement Learning                             (20 min)
   F.~Kirchner (GMD)

11:00am--12:30pm, Session II: Reinforcement Learning (cont)

Why Did TD-Gammon Work?                                         (20 min)
   J. Pollack and A. Blair (Brandeis U.)

Learning Task Relevant State Spaces with a                      (20 min)
Utile Distinction Test
   A. McCallum (U. Rochester)

Policy Based Clustering for Markov Decision Problems            (20 min)
   R. Parr (Berkeley)

Optimality Criteria In Reinforcement Learning                    (20 min)
   S. Mahadevan (USF)

Discussion                                                       (10 min)


AFTERNOON

2:00--3:30pm Session III:  Learning and Knowledge Representation


Learning to Reason                                               (20 min)
   D. Roth (Harvard U. and Weizmann Institute)


Learning the Parameters of First Order Probabilistic Rules       (20 min)
   D. Koller and A. Pfeffer (Stanford)


Learning Knowledge and Structure                                 (20 min)
   J. Pearl

Learning Independence Structure                                  (20 min)
   E. Ristad (Princeton)

Discussion                                                       (10 min)

4:00--5:30pm Session IV:  Learning Complex Behaviors 

A Survey of Positive Results on Automata Learning                (20 min)
  K. Lang (NEC)

Learning to Plan                                                 (20 min)
   E. Baum (NEC)

World Modelling: Learning  Knowledge Representation              (50 min)
   S. Russell, J. Albus, A. A. Moore, E. Baum, M. Jordan

Sunday, November 10, 1996

Morning

9:00--10:30am, Session V:  Learning and Knowledge Representation

A Neuroidal Architecture for Knowledge Representations             (45 min)
   Les Valiant (Harvard)

Learning to be Competent                                           (20 min)
    R. Khardon (Harvard)

Concept Learning for Geometric Reasoning                           (20 min)
   E. Sacks (Purdue)  

Discussion                                                         (10 min)


11:00am--12:30pm, Session VI:  Learning Principles in Natural Language

Some Advances in Transformation-Based Part-of-Speech Tagging        (20 min)
    E. Brill (JHU)

Explaining Language Change: Complex Consequences of Simple          (20 min)
Learning Algorithms
   P. Nyogi (MIT and Lucent) and Robert Berwick (MIT)

Learning the Lexical Semantics of Spatial Motion Verbs from         (20 min)
Camera Input
   J. Siskind (Technion)

Computational Learning Theory for Natural Language:                 (20 min)