[ACT-R-users] Special Issue CFP

Tue Sep 15 12:52:24 EDT 2009

Call For Papers
Journal of Artificial General Intelligence
Special Issue on
Model Comparison for Cognitive Architectures and AGI

The purpose of this special issue is to explore the merits of a comparative
approach for understanding Artificial General Intelligent (AGI) systems.
 This approach is common in the field of cognitive modeling, where different
theories of cognition are instantiated as computational architectures and
applied to common tasks to establish to their respective scope and limits.
Within the field of cognitive modeling, comparison efforts have been
recognized as crucial for making scientific progress, and the method is now
finding its way into a number of related fields in cognitive science.  But
model comparison need not be viewed only as a theoretical exercise; in fact,
the drive to implement unified theories of cognition as computational
architectures and test them against a range of human performance data in
dynamic, complex and potentially ill-structured task environments is, at
root, no different than the call to develop AI systems that can generalize
beyond narrow, task-specific applications.  In this light, we view model
comparison as a means to advance both cognitive science and the study of AGI
systems and to reconcile traditions that historically emerged as
complementary but have since evolved, for all practical purposes, as
independent disciplines.

The structure and content of this special issue are influenced by a
particular model comparison challenge recently organized to explore a
generic dynamic decision making task, the Dynamic Stocks and Flows (DSF)
(please see: 
http://www.hss.cmu.edu/departments/sds/ddmlab/modeldsf/results.html for
details). The DSF task was designed to be as simple and accessible as
possible to computational modelers while focusing on two key ubiquitous
components of general intelligence: the control of dynamical systems and the
prediction of future events.  A general call for participation was submitted
to invite independent modelers using distinct computational approaches to
simulate human performance in DSF.  Participants in this challenge developed
computational models to simulate human performance on the DSF task in a
variety of conditions.  The goal was to reproduce human behavior, including
learning, mistakes and limitations in a way that would generalize to new
conditions of the task undisclosed to the modelers.  Results from three of
the models submitted were selected for presentation at the 2009
International Conference on Cognitive Modeling.  Human learning data in DSF
as well as the results from all the models participating in the model
comparison are available on the comparison web site and can be used for the
purposes of analyses and publication in this special issue.

We welcome submissions from those who participated in the DSF model
comparison challenge as well as from those who are in a position to comment
on the following general topics relevant to model comparison within the
context of the DSF challenge:

·        Many computational fields have seen the emergence of challenge
tasks to prod the development of new techniques and measure their progress
toward the goal (e.g., Robocup).  What are the requirements of such
challenge tasks for AGI?  Should they provide independent tests of specific
capacities, integrated tests of functionality, or both?
·        Progress is often measured on the relative evaluation of
alternatives in a common setting.  But what are the constraints of such
comparisons for cognitive models?  Are acceptable mechanisms limited to
those that are judged cognitively - or even biologically - plausible? Should
the complexity of a model be taken into account?  Which levels of
description are acceptable? Should models aim to predict human performance
in new conditions, or is suitable post hoc reproduction of known performance
data sufficient?
·        The methodology developed by cognitive psychology for evaluating
fits of model to human data is strongly dependent upon experimental control
and scales poorly to complex, open-ended tasks. Sets of criteria for
evaluating cognitive architectures have been proposed, but specific
instantiations on AGI-level tasks have been lacking.
·        Human behavior models based on cognitive architectures are usually
developed for very specific tasks and at substantial effort to the modeler.
 While cognitive architectures keep being refined, cumulative progress in
the form of model reuse has been elusive. New mechanisms and/or practices
for composing and/or generalizing models of simple tasks are required for
scaling up to models suitable for general, open-ended intelligence.
·        Despite their stated goal of providing an integrated theory of
human intelligence, specific cognitive architectures are usually applied to
a relatively narrow set of cognitive activities, often laboratory tasks.
 Attempts to apply cognitive architectures to open-ended, naturalistic
environments (using virtual or robotic embodiments) have raised substantial
issues about their robustness and scalability beyond laboratory
environments.
Submission
Submissions should be sent by December 1st, 2009 to DSFChallenge at gmail.com.
Manuscripts should conform to the JAGI formatting guidelines that can be
found at the journal¹s web site, http://journal.agi-network.org/, and should
not exceed 20 pages of total length.  Manuscripts will be submitted to a
traditional anonymous peer-review process with publication of accepted
contributions expected by summer 2010.  Authors will be required to provide
the final camera-ready, formatted and copy-edited manuscript. Inquiries
regarding this special issue can be sent to DSFChallenge at gmail.com or
directly to any of the special issue editors at the addresses below.
Special Issue Editors
Christian Lebiere
Psychology Department, Carnegie Mellon University
cl at cmu.edu

Cleotilde Gonzalez
Social and Decision Sciences Department, Carnegie Mellon University
coty at cmu.edu

Walter Warwick
MA&D Operation, Alion Science and Technology
wwarwick at alionscience.com

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