Keith S. Decker
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Associate Professor
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Dept. of Computer and Information Sciences
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University of Delaware
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77 E. Delaware Ave. (the AI/NLP GreenHouse)
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Newark, DE 19716-2586
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(302) 831-1959 (office)
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(302) 831-4091 (fax)
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decker@cis.udel.edu
Other Pages
EASSS-03 Complete Lecture Slides
EASSS-03 Changed Lecture Slides only
My
UD Courses 
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UMass
Distributed AI Page 
CMU
Intelligent Software Agents Page
My
Thesis Page 
Misc. Links
Research
My research program is concerned with the areas of Distributed AI and Multi-Agent
Systems. There are many problems that do not easily submit to centralized,
monolithic computational solutions. One reason that these problems arise
is because computer applications support people who are doing tasks in
the context of an existing organization. Just as a human organization was
designed or evolved so that individuals have different roles, responsibilities,
power, control over others, decision-making authority, etc., computer systems
that work within existing organizations need to respect these relationships.
Consider a nurse attempting to schedule a patient's tests, including an
x-ray, and the x-ray unit staff's need to provide services for many patients
while using their equipment efficiently. No centralized solution is possible
because 1) the humans involved will not give up their authority to a centralized
administrator/computer program, 2) the groups involved are actually attempting
to maximize different sets of performance/utility criteria. Other examples
include collaborative engineering design and agile manufacturing systems.
Coordination
Much of my research has focussed on the analysis and design of coordination
mechanisms for groups of collaborative software agents. Coordination can
be defined as the act of managing interdependencies between activities.
Many researchers have shown that there is no single best organization or
coordination mechanism for all task environments. The design of coordination
mechanisms for intelligent agents cannot rely on the principled construction
of agents alone, but must also rely on the structure and other characteristics
of the agents' task environment. One focus of this activity has been the
development of a framework called TAEMS
(Task Analysis, Environment Modeling, and Simulation) for representing
and reasoning about the salient features of a computational task environment.
Another focus has been on how software coordination mechanisms can be designed
to respond to particular features of the task environment structure; one
result has been the Generalized
Partial Global Planning family of coordination algorithms.
A second focus is on Integrating
Organizational Style with Environmental Characteristics. Organizational
styles can be operationalized as sets of coordination mechanisms that enforce
certain behaviors as part of the style. However, not all coordination behavior
arises from abstract organizational styles; much arises from standard responses
to specific environmental (problem domain) characteristics. This
research aims to extend environment and organization models, represent
standard responses to common environmental problems, and develop operational
specifications for organizational styles. This allows the interactions
between environmental constraints and organizational styles to be reasoned
about analytically. Designers are not doomed to create coordinated systems
with only a few simple organizational behaviors and large numbers of brittle
domain-specific coordination heuristics. Instead, given good environment
models and certain other constraints (often dictated by existing human
organizations), desiginers can make principled choices of computational
organizations and standard environment-influenced coordination behaviors.
This project will result in a much richer set of tools for building and
analyzing both multi-agent computer systems and integrating such systems
with existing human organizations in applications such as distributed information
gathering, distributed scheduling, and concurrent engineering.
It has been clear for some time that the Internet is a viable medium for
supplying the data needed for making various types of decisions. As more
useful data become available at different times and in multiple locations,
however, it becomes more difficult and time-consuming for a person to collect
and evaluate that data. Most current agent-oriented approaches to this
problem have focussed on single agents with general knowledge and capabilities
to perform a wide range of user-delegated information-finding tasks. Such
centralized approaches have several limitations: the need for an enormous
amount of knowledge in order to provide coverage for a variety of tasks;
the implied centralized processing bottleneck; the inability of most such
single agents to deal dynamically with the appearance of new agents and
information sources. One solution is to use multi-agent computer systems
to access, filter, evaluate, and integrate this information. We have been
developing multi-agent systems that can compartmentalize specialized task
knowledge, organize themselves to avoid processing bottlenecks, and cope
with dynamic changes in the agent and information-source landscape. We
have developed the DECAF
agent architecture and toolkit for quickly prototyping multi-agent
information gathering systems.
Bioinformatics
Our most comprehensive set of information gathering applications
centers around the area of bioinformatics. Today biological
information and algorithms for the analysis of biological data are
available on the Internet in many different locations with overlapping
content, different structure, and varied amounts of curation. Our
approach to these problems, called multi-agent information gathering,
is to apply multi-agent systems technologies to create software agents
for information retrieval, filtering, integration, analysis and
display. Currently we have developed a prototype system for the
automated annotation of herpesvirus sequences with homologs, motifs,
domains, and sub-cellular location predictions. The system
automatically produces a searchable database of this information HERE (coming soon, an
automated version produced for the coding segments of the chicken
genome). Other projects in progress include a new subsystem for
assisting biologists in functional annotation using the GO ontologies (try out our
visual display system GOFigure!);
automated UNIGENE cluster analysis; automated EST processing and
annotated consensus sequence database/web site creation; and soon the
integration of various forms of gene expression data.
Limited Rationality
Herbert Simon's theory of bounded rationality can be construed as a theory
of why humans form organizations---that no one person can process all the
necessary information and make all the correct decisions for everyone.
Thus control is shared, information filtered, decision-making authority
is passed to subordinates, we satisfice rather than optimize. Our work
considers problems where time and computational resources are limited for
computational agents also. This work on soft real-time approaches has resulted
in algorithms for making appropriate commitments to other agents in time-constrained
situations, and for using "design-to-time"
and "anytime" algorithms to schedule an agent's problem solving activities.
decker@cis.udel.edu
Last Update: 5/24/02