AAAI 1995 Fall Symposium Series Call for Participation

[AAAI]

AAAI 1995 Fall Symposium Series 
Call for Participation

November 10-12, 1995

Massachusetts Institute of Technology 
Cambridge, Massachusetts

Sponsored by the American Association for Artificial Intelligence 
445 Burgess
Drive, Menlo Park, CA 94025 
(415) 328-3123 (voice)
(415) 321-4457 (fax)
fss at aaai.org

The American Association for Artificial Intelligence presents the 1995 Fall
Symposium Series, to be held Friday through Sunday, November 10-12, 1995, at 
the Massachusetts Institute of Technology.

The topics of the eight symposia in the 1995 Fall Symposium Series are:

-  Active Learning 
-  Adaptation of Knowledge for Reuse 
-  AI Applications in Knowledge Navigation and Retrieval 
-  Computational Models for Integrating Language and Vision 
-  Embodied Language and Action 
-  Formalizing Context 
-  Genetic Programming 
-  Rational Agency: Concepts, Theories, Models, and Applications

Symposia will be limited to between forty and sixty participants. Each
participant will be expected to attend a single symposium. Working notes 
will be prepared and distributed to participants in each symposium.

A general plenary session, in which the highlights of each symposium will be
presented, will be held on Saturday, November 11, and an informal reception 
will be held on Friday, November 10.

In addition to invited participants, a limited number of other interested
parties will be able to register in each symposium on a first-come, 
first-served basis. Registration will be available by 1 August, 1995. 
To obtain registration information write to the AAAI at 445 Burgess Drive, 
Menlo Park, CA 94025 (fss at aaai.org).

Submission Dates

-  Submissions for the symposia are due on April 14, 1995. 
-  Notification of acceptance will be given by May 19, 1995. 
-  Material to be included in the working notes of the symposium must be
   received by September 1, 1995.

See the appropriate section below for specific submission requirements for each
symposium.

This document is available as 
http://www.ai.mit.edu/people/las/aaai/fss-95/fss-95-cfp.html

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			ACTIVE LEARNING

An active learning system is one that can influence the training data it
receives by actions or queries to its environment. Properly selected, these
actions can drastically reduce the amount of data and computation required by a
machine learner. Active learning has been studied independently by researchers
in machine learning, neural networks, robotics, computational learning theory,
experiment design, information retrieval, and reinforcement learning, among
other areas. This symposium will bring researchers together to clarify the
foundations of active learning and point out synergies to build on.

Submission Information

Potential participants should submit a position paper (at most two pages)
discussing what the participant could contribute to a dialogue on active
learning and/or what they hope to learn by participating. Suggested topics
include:

Theory: What are the important results in the theory of active learning and what
are important open problems? How much guidance does theory give to application?

Algorithms: What successful algorithms have been found for active learning? How
general are they? For what tasks are they appropriate?

Evaluation: How can accuracy, convergence, and other properties of active
learning algorithms be evaluated when, for instance, data is not sampled
randomly?

Taxonomy: What kinds of information are available to learners (e.g. membership
vs. equivalence queries, labeled vs. unlabeled data) and what are the ways
learning methods can use them? What are the commonalities among methods studied
by different fields?

Papers should be sent to David D. Lewis, lewis at research.att.com, AT&T Bell
Laboratories, 600 Mountain Ave., Room 2C-408, Murray Hill, NJ 07974-0636.
Electronic mail submissions are strongly preferred.

Symposium Structure

The symposium will be broken into sessions, each dedicated to a major theme
identified within the position papers. Sessions will begin with a background
presentation by an invited speaker, followed by brief position statements from
selected participants. A significant portion of each session will be reserved
for group discussion, guided by a moderator and focused on the core issue for
the session. The final session of the symposium will accommodate new issues that
are raised during sessions.

Organizing Committee

David A. Cohn (cochair), MIT, cohn at psyche.mit.edu; David D. Lewis (cochair),
AT&T Bell Labs, lewis at research.att.com; Kathryn Chaloner, U. Minnesota ; Leslie
Pack Kaelbling, Brown U.; Robert Schapire, AT&T Bell Labs; Sebastian Thrun, U.
Bonn; Paul Utgoff, U. Mass Amherst.

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		ADAPTATION OF KNOWLEDGE FOR REUSE

Several areas in AI address issues of creating and storing knowledge constructs
(such as cases, plans, designs, specifications, concepts, domain theories,
schedules). There is broad interest in reusing these constructs in similar
problem-solving situations so as to avoid expensive re-derivation. Adaptation
techniques have been developed to support reuse in frameworks such as analogical
problem solving, case-based reasoning, problem reformulation, or representation
change and task domains such as creativity, design, planning, program
transformation or software reuse, schedule revision, and theory revision.

However, many open issues remain, and progress on such issues as case adaptation
would substantially assist many researchers and practitioners. Our goals are to
characterize the approaches to adaptation employed in various AI subfields,
define the core issues in adaptation of knowledge, and advance the
state-of-the-art in addressing these issues. We intend that presentations will
investigate novel solutions to unsolved problems on adaptation, reflect diverse
viewpoints, and focus on adaptation issues that are common to several subfields
of AI. Discussions will be held on the strengths and limitations of adaptation
techniques and their interrelationships.

Invited talks will be given by experts who will discuss methods for the
adaptation of various types of knowledge constructs. Two panels will be held.
First, researchers studying knowledge adaptation from different perspectives
will discuss how approaches used in their community differ from those used
elsewhere, focusing on their potential benefits for other problems. Panelists in
the second panel will lead discussions on identifying the core issues in
knowledge adaptation raised in the presentations and the impact of the proposed
methods on addressing these issues.

Submission Information

Anyone interested in presenting relevant material is invited to email PostScript
submissions to aha at aic.nrl.navy.mil using the six-page AAAI-94 proceedings
format. Anyone interested in attending is asked to submit a two-page research
statement and a list of relevant publications. Please see
http://www.aic.nrl.navy.mil/~aha/aaai95-fss/home.html for further information.

Organizing Committee

David W. Aha (cochair), NRL, aha at aic. nrl.navy.mil; Brian Falkenhainer, Xerox;
Eric K. Jones, Victoria University; Subbarao Kambhampati, Arizona State
University; David Leake, Indiana University; Ashwin Ram (cochair), Georgia
Institute of Technology, ashwin at cc.gatech.edu.

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	AI APPLICATIONS IN KNOWLEDGE NAVIGATION AND RETRIEVAL

The diversity and volume of accessible on-line data is increasing dramatically.
As a result, existing tools for searching and browsing information are becoming
less effective. The increasing use of non-text data such as images, audio and
video has amplified this trend.

Knowledge navigation systems are knowledge-based interfaces to information
resources. They allow users to investigate the contents of complex and diverse
sources of data in a natural manner. For example, intelligent browsers that can
help direct a user through a large multi-dimensional information space, agents
that users can direct to perform information finding tasks, or knowledge-based
intermediaries that employ retrieval strategies to gather information relevant
to a userUs request.

The purpose of this symposium is to examine the state of the art in knowledge
navigation by examining existing applications and by discussing new techniques
and research directions. We encourage two types of submissions: work-in-progress
papers that point towards the future of this research area, and demonstrations
of knowledge navigation systems. Some research issues of interest:

-  Indexing: What indexing methods are appropriate and feasible for knowledge
   navigation systems? How can indices be extracted from data?

-  Retrieval: What retrieval methods are appropriate for knowledge navigation?
   What retrieval strategies can be employed?

-  Learning: How can knowledge navigation systems adapt to a changing knowledge
   environment and to user needs?

-  User interfaces: What are the characteristics of a useful navigational
   interface? What roles can or should an "agent" metaphor play in such 
   interfaces? How can a navigation system orient the user in the 
   information space?

-  Multi-source integration: How can multiple data and knowledge sources be
   integrated to address usersU needs?

-  Multimedia: What are the challenges presented by multimedia information
   sources?

Submission Information

The symposium will consist of invited talks, presentations, and hands-on
demonstration/discussion sessions. Interested participants should submit a short
paper (8 pages maximum) addressing a research issue in knowledge navigation or
describing a knowledge navigation system that can be made available for hands-on
demonstration at the symposium. System descriptions should clearly indicate the
novel and interesting features of the system to be presented and its
applicability to the central problems in knowledge navigation. Those wishing to
demonstrate should also include a one-page description of their hardware and
connectivity requirements. Send, by email,  either a URL pointing to a
PostScript version of the paper or the PostScript copy itself to 
aiakn at cs.uchicago.edu. Or, send 5 hard copies to Robin Burke, AI Applications 
in Knowledge Navigation, University of Chicago, Department of Computer Science.
1100 E. 58th St. Chicago, IL 60637. For further information, a web page for this
symposium is located at http://www-cs.uchicago.edu/ ~burke/aiakn.html

Organizing Committee

Robin Burke (chair), University of Chicago, burke at cs.uchicago.edu; Catharine
Baudin, NASA Ames; Su-Shing Chen, National Science Foundation; Kristian Hammond,
University of Chicago; Christopher Owens, Bolt, Beranek & Newman.

Program Committee

Ray Bariess, Institute for the Learning Sciences; Alon Levy, AT&T Bell
Laboratories; Jim Mayfield, University of Maryland, Baltimore County; Dick
Osgood, Andersen Consulting.

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	COMPUTATIONAL MODELS FOR INTEGRATING LANGUAGE AND VISION

This symposium will focus on research issues in developing computational models
for integrating language and vision. The intrinsic difficulty of both natural
language processing and computer vision has discouraged researchers from
attempting integration, although in some cases it may simplify individual tasks
like collateral-based vision, resolving ambiguous sentences through the use of
visual information.

Developing a bridge between language and vision is nontrivial, because the
correspondence between words and images is not one-to-one. Much has been said
about the necessity of linking language and perception for a system to exhibit
intelligent behavior, but there has been relatively little work on developing
computational models for this task. A natural-language understanding system
should be able to understand and make references to the visual world. The use of
scene-specific context (obtained from written or spoken text accompanying a
scene) could greatly enhance the performance of computer vision systems.

Some topics to be addressed are:

-  use of collateral text in image and graphics understanding

-  generating natural-language descriptions of visual data (e.g., event
   perception in image sequences)

-  identifying and extracting visual information from language

-  understanding spatial language, spatial reasoning

-  knowledge representation for linguistic and visual information, hybrid
   (language and visual) knowledge bases

-  use of visual data in disambiguating/understanding text

-  content-based retrieval from integrated text/image databases

-  language-based scene modeling (e.g., picture or graphics generation)

-  cognitive theories connecting language and perception

Submission Information

The symposium will consist of invited talks, panel discussions, individual
presentations and group discussions. Those interested in making a presentation
should submit a technical paper (not to exceed 3,000 words). Other participants
should submit either a position paper or a research abstract. Email submissions
in postscript format are encouraged. Send to burhans @cs.buffalo.edu.
Alternatively, 4 hard copies may be sent to Rohini Srihari, CEDAR/SUNY at
Buffalo, UB Commons, 520 Lee Entrance Suite 202, Buffalo, NY 14228-2567. Further
information on this symposium may be found at
http://www.cedar.buffalo.edu/Piction/FSS95/CFP.html. Please address questions to
Debra Burhans (burhans at cs.buffalo.edu) or Rajiv Chopra (rchopra at cs.buffalo.edu).

Organizing Committee

Janice Glasgow, Queen's University; Ken Forbus, Northwestern University; Annette
Herskovits, Wellesley College; Gordon Novak, University of Texas at Austin;
Candace Sidner, Lotus Development Corporation; Jeffrey Siskind, University of
Toronto; Rohini K. Srihari (chair), CEDAR, SUNY at Buffalo,
rohini at cedar.buffalo. edu; Thomas M. Strat, SRI International; David Waltz, NEC
Research Institute.

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			EMBODIED LANGUAGE AND ACTION

This symposium focuses on agents that can use language or similar communication,
such as gesture, to facilitate extended interactions in a shared physical or
simulated world. We examine how this embodiment in a shared world both
stimulates communication and provides a resource for understanding it. Our focus
is on the design of artificial agents, implemented in software, hardware, or as
animated characters. Papers should clearly relate the technical content
presented to one of the following tasks:

-  Two or more communicating agents work together to construct, carry out
   maintenance on, or destroy a physical or simulated artifact (Collaborative
   Engagement)

-  An agent assists a human by fetching or delivering physical or software
   objects. The human communicates with the agent about what is to be fetched or
   delivered to where. (Delivery Assistance)

We solicit papers on the following issues (not to the exclusion of others):

-  Can task contexts act as resources for communication by simplifying the
   interpretation and production of communicative acts?

-  How does physical embodiment and its concomitant resource limitation affect
   an agentUs ability to interpret or generate language?

-  Can architectures designed to support perception and action support language
   or other forms of communication?

-  How can agents to mediate between the propositional representations of
   language and the (often) non-propositional representations of perception and
   action?

-  What tradeoffs exist between the use of communication to improve the agents'
   task performance and the additional overhead involved in understanding and
   generating messages?

-  Do differences between communication used to support concurrent task
   execution and communication used to support planning, reflect deeper 
   differences in agent ability?

-  What is the role of negotiation, whether of task responsibilities, or of
   reference and meaning, in such situated task environments?

Submission Information

Interested participants should submit either (1) a paper (in 12 pt font, not to
exceed 3000 words), or (2) a statement of interest briefly describing the
authorUs relevant work in this area and listing recent relevant publications.
Send contributions, plain ascii or postscript, to ian at ai. mit.edu. If electronic
submission is impossible, mail 6 copies to Ian Horswill, MIT Artificial
Intelligence Laboratory, 545 Technology Square, Cambridge, MA 02139.

Organizing Committee

John Batali, UCSD; Jim Firby, University of Chicago; Ian Horswill (cochair),
MIT, ian at ai.mit.edu; Marilyn Walker (cochair), Mitsubishi Cambridge Research
Labs, walker at merl.com; Bonnie Webber, University of Pennsylvania.

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			FORMALIZING CONTEXT

The notion of context has played an important role in AI systems for many years.
However, formal logical explication of contexts remains an area of research with
significant open issues. This symposium will provide a forum for discussing
formalizations of contexts, approaches to resolving open issues, and application
areas for context formalisms.

The most ambitious goal of formalizing contexts is to make automated reasoning
systems which are never permanently stuck with the concepts they use at a given
time because they can always transcend the context they are in. Such a
capability would allow the designer of a reasoning system to include only such
phenomena as are required for the system's immediate purpose, retaining the
assurance that if a broader system is required later, "lifting rules" can be
devised to restate the facts from the narrow context in the broader context with
qualifications added as necessary. A formal theory of context in which sentences
are always considered as asserted within a context could provide a basis for
such transcendence.

Formal theories of context are also needed to provide a representation of the
context associated with a particular circumstance, e.g. the context of a
conversation in which terms have particular meanings that they wouldn't have in
the language in general. Linguists and philosophers have already studied similar
notions of context. An example is the situation theory that has been proposed in
philosophy and applied to linguistics. However, these theories usually lie
embedded in the analysis of specific linguistic constructions, so locating the
exact match with AI concerns is itself a research challenge.

This symposium aims to bring together researchers who have studied or applied
contexts in AI or related fields. Technical papers dealing with formalizations
of context, the problem of generality, and use of context in common sense
reasoning are especially welcome. However, survey papers which focus on contexts
from other points of view, such as philosophy, linguistics, or natural language
processing, or which apply contexts in other areas of AI, are also encouraged.

Submission Information

Persons wishing to make presentations should submit papers (up to 12 pages, 12
pt font). Persons wishing only to attend should submit a 1-2 page research
summary including a list of relevant publications. A PostScript file or 8 paper
copies should be sent to the program chair, Sasa Buvac Department of Computer
Science Stanford University Stanford CA 94305-2140 buvac at sail.stanford.edu.
Limited funding will be available to support student travel.

Organizing Committee

Sasa Buvac (chair), Stanford University, buvac at sail.stanford.edu; Richard Fikes,
Stanford University; Ramanathan Guha, MCC; Pat Hayes, Beckman Institute; John
McCarthy, Stanford University; Murray Shanahan, Imperial College; Robert
Stalnaker, MIT; Johan van Benthem, University of Amsterdam. Genetic programming
(GP) extends the genetic algorithm to the domain of computer programs. In
genetic programming, populations of programs are genetically bred to solve
problems. Genetic programming can solve problems of system identification,
classification, control, robotics, optimization, game-playing, and pattern
recognition.

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			GENETIC PROGRAMMING

Starting with a primordial ooze of hundreds or thousands of randomly created
programs composed of functions and terminals appropriate to the problem, the
population is progressively evolved over a series of generations by applying the
operations of Darwinian fitness proportionate reproduction and crossover (sexual
recombination).   
Topics of interest for the symposium include:
 
-  The theoretical basis of genetic programming 
-  Applications of genetic programming
-  Rigorousness of validation techniques 
-  Hierarchical decomposition, e.g. automatically defined functions 
-  Competitive coevolution 
-  Automatic parameter tuning 
-  Representation issues 
-  Genetic operators 
-  Establishing standard benchmark problems 
-  Parallelization techniques 
-  Innovative variations 

Submission Information

The format of the symposium will encourage interaction and discussion, but 
will also include formal presentations. Persons wishing to make a presentation 
should submit an extended abstract of up to 2500 words of their work in 
progress or completed work. For accepted abstracts, full papers will be due 
at a date closer to the symposium.

Persons not wishing to make a presentation are asked to submit a one-page 
description of their research interests since there may be limited room 
for participation.

Submit your abstract or one-page description as plain text electronically 
by Friday April 14, 1995, with a hard-copy backup to Eric V. Siegel, AAAI 
GP Symposium Co-Chair, Columbia University, Department of Computer Science,
500 W 120th Street, New York, NY 10027, USA; telephone: 212-939-7112, 
fax: 212-666-0140, e-mail: evs at cs.columbia.edu.

Organizing Committee

Robert Collins, USAnimation, Inc.; Frederic Gruau, Stanford University; 
John R. Koza (co-chair), Stanford University, koza at cs.stanford.edu; 
Robert Collins, US Animation, Inc.; Conor Ryan, University College Cork; 
Eric V. Siegel (co-chair), Columbia University, evs at cs.columbia.edu; 
Andy Singleton, Creation Mechanics, Inc.; Astro Teller, Carnegie-Mellon 
University.

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RATIONAL AGENCY: CONCEPTS, THEORIES, MODELS, AND APPLICATIONS

This symposium explores conceptions of rational agency and their implications
for theory, research, and practice. The view that intelligent systems are, or
ought to be, rational agents underlies much of the theory and research in
artificial intelligence and cognitive science. However, no consensus exists on a
proper view of agency or rationality principles for practical agents.

Traditionally agents are presumed disposed toward purposive action. However,
agent theories abound in which behavior is fundamentally reactive. Some theories
emphasize agents' abilities to manage private belief systems. Others focus on
agents' interactions with their environment, sometimes including other agents.
Application builders have recently broadened the term "agent" to mean any
embedded system performing tasks to support human users.

Rationality accounts are equally diverse. Rationality involves having reasons
warranting particular beliefs (epistemic rationality) or particular desires and
actions (strategic or practical rationality). Many agent models propose
epistemic rationality criteria such as logical consistency or consequential
closure. Other agent models base practical rationality on classical or
non-monotonic logics for reasoning about action. Such logicist views are now
being challenged by decision theoretic accounts emphasizing optimal action under
uncertainty, including recent work on decision theoretic principles of
metareasoning for limited rationality.

Our symposium will explore the diverse views of rational agency. Through
informal presentations and group discussion, participants will critically
examine agency concepts and rationality principles, review computational agent
models and applications, and consider promising directions for future work on
this topic.

Submission Information

Prospective participants should submit a brief paper (5 pages or less)
describing their research in relation to any of the following questions:

-  Is rationality important; must an agent be rational to be successful?

-  What are suitable principles of epistemic, strategic, and limited
   rationality?

-  Are rationality principles applicable to retrospective processes such as
   learning?

-  What are general requirements on rational agent architectures?

-  How, if at all, must a model of rational agency be modified to account for
   social, multi-agent interaction?

Those wishing to make a specific presentation should describe its contents in
their concept paper. Note: While we recognize that our topic lends itself to
formal analysis, we also encourage discussion of experimental work with
implemented agents. Postscript files of concept papers should be sent by email
only to the program chair, fehling at lis.stanford.edu.

Organizing Committee

Michael Fehling (chair), Stanford University, fehling at lis.stanford.edu; Don
Perlis, University of Maryland; Martha Pollack, University of Pittsburgh; John
Pollock, University of Arizona.