Invited Speakers -- ICMAS '95
First International Conference on Multiagent Systems - ICMAS '95
June 12-15, 1995
San Francisco, California
Blissful Ignorance: Knowing Just Enough to Coordinate Well
Edmund H. Durfee
EECS Department
University of Michigan
1101 Beal Ave.
Ann Arbor, MI 48109
durfee@caen.engin.umich.edu
Abstract:
Coordination requires making decisions based on expectations of what
others will do, which usually implies having models of others. Better
(more complete, timely, and precise) models of others can improve the
accuracy of expectations, but acquiring and using such models carries
costs in perception, communication, computation, and/or memory.
Moreover, by committing to actions based on its models of others, and
by being committed to actions implicit in any model of itself that it
has projected to others, an agent might leave itself without
flexibility to respond to unexpected opportunities. A fundamental
component of the coordination process is thus to develop models that
convey enough knowledge to benefit agents, while at the same time
retaining enough ignorance among agents to avoid excessive
coordination costs and to respond flexibly to new circumstances. In
this talk, I describe various kinds of knowledge that could be
captured in an agent model and how the knowledge can impact the
quality and costs of coordination. I consider how different
restrictions on and simplifications of agent models have been
introduced that have led to practical coordination mechanisms for
applications including robotic coordination, decentralized resource
scheduling, and distributed information gathering. While, in most of
these mechanisms, the decision about how agents should model each
other is designed in, I also describe early steps in the development
of automated techniques that agents can use to find agent models that
are most appropriate for their coordination needs.
Computational Organization Research
Les Gasser
IMPACT Lab
School of Engineering
USC
Los Angeles, CA 90089
gasser@usc.edu
Abstract:
Large, dynamic ensembles of machines, intelligent agents, and people
interacting clearly exhibit organizational-level phenomena. These
"computational organizations"---organizations made up completely or
partly of computational participants---raise many exciting R&D
opportunities: computational models and representations of
organizational knowledge, explicit organizational ontologies,
simulating organizational activity or structuring, computational
approaches to building organization theories, coordination algorithms,
computational approaches to organization design, computational tools
for organization analysis, and so on. From a complementary point of
view, a growing worldwide community is researching phenomena of human
organizations using computational methods. Pressing research issues
in human organization theory and organization development are highly
amenable to computational modeling, theorizing, and experimentation.
These complementary research directions converge in what might be
termed "Computational Organization Research". Computational modeling,
analysis and simulation can help us to better understand generic
underlying principles of organization in both human and computational
settings, and to solve particular, pressing configuration
problems---issues that are both highly impactful and scientifically
challenging. The impacts on improved effectiveness and flexibility in
organizations can be great, and the theory, modeling technology, and
infrastructure are ready. This talk will motivate and survey current
computational organization research, and pose some core questions for
further development.
Parallel, Distributed and Multi-Agent Production Systems: Research
Foundations for Distributed Artificial Intelligence
Toru Ishida
Department of Information Science
Kyoto University
ishida@lab7.kuis.kyoto-u.ac.jp
Abstract:
Production systems have been widely used as expert system building
tools and recognize-and-act models in cognitive science. This talk is
intended to introduce parallel/distributed/multi-agent production
systems, and to reveal their possibilities as research foundations for
distributed artificial intelligence: a parallel production system as
an agent reactive architecture, a distributed production system as an
adaptive agent organization, and a multi-agent production system for
organizational learning. Production systems already have been equipped
with clear syntax and efficient pattern matching algorithms. Their
foundations can be further strengthened with recent advanced theories
such as linear logic, real-time search and reinforcement learning.
Developing Industrial Multi-Agent Systems
N. R. Jennings
Distributed AI Unit
Dept of Electronic Engineering
Queen Mary & Westfield College
University of London
N.R.Jennings@qmw.ac.uk
Abstract:
The development and deployment of multi-agent systems in real world
settings raises a number of important research issues and problems
which must be overcome if Distributed AI is to become a widespread
solution technology. Work undertaken in the context of the ARCHON
project has provided a number of important insights into these issues
- especially with respect to the domain of industrial control
applications. This talk will draw together many of these experiences
and will give details of an Electricity Transportation Management
System which is one of the world's first operational Distributed AI
systems.
MULTIAGENT PLANNING AS A SOCIAL PROCESS: VOTING, PRIVACY, AND MANIPULATION
Jeffrey S. Rosenschein
Institute of Computer Science
Hebrew University
Jerusalem, Israel
jeff@cs.huji.ac.il
Abstract:
The essence of effective multiagent planning is to coordinate and
merge the separate search activity of individual agents. When these
agents are self-motivated, however, the coordination and merging
process becomes more subtle---different agents are actually interested
in transforming the world in different ways, and have an interest in
influencing the group activity in certain directions. Under these
circumstances, multiagent planning has to incorporate elements of
consensus formation. Designed appropriately, the consensus process may
be able to protect the group against manipulative individuals, and, as
much as possible, maintain the privacy of agents' preferences. This
talk explores multiagent planning as a heuristic process of
distributed search and merging, and in particular considers techniques
(such as voting) that enable a group of self-motivated planning agents
to reach consensus effectively.
This is joint work with Eithan Ephrati, Computer Science Department,
the University of Pittsburgh.
What We Talk About When We Talk About Agents
Yoav Shoham
Computer Science Dept.
Stanford University
Stanford, CA 94305
shoham@flamingo.stanford.edu
Abstract:
The terms "software agent" and other, related ones, are used very
aggressively these days, in academia as well as industry. Some of the
work in the area is exciting, but the area suffers from a lack of
cohesion and variable quality. In the talk I will do the following.
First, I will drive home the diversity point by briefly discussing a
half dozen examples of agent-based work. The conclusion will be that
there is no definition of software agents that is at once reasonably
clear and covers most of the work in the field. Second, in lieu of
such a definition, I will outline one approach to understanding the
area, based on dimensions of agency. Finally, I will propose that this
approach is useful but inherently flawed, and propose an alternative
which I find more useful.
Coordination of Multiple Intelligent Agents in the Infosphere
Katia Sycara
School of Computer Science
Carnegie Mellon University
Pittsburgh, PA. 15213
e-mail: katia@cs.cmu.edu
Abstract:
The current explosion of data and information on the Internet-based
Infosphere has made the problem of locating information sources,
accessing them and combining information from them a very critical
task. The notion of Intelligent Software Agents has been proposed to
address this challenge, Intelligent Software Agents are programs that
must act on behalf of their users in order to perform laborious
information gathering tasks, such as locating and accessing
information from various on-line information sources, resolve
inconsistencies in the retrieved information, filter away irrelevant
or unwanted information, integrate information from heterogeneous
information sources and adapt over time to their users' information
needs and the shape of the Infosphere. These tasks are performed
automatically or with little help from the users. Most current
research on Intelligent Agents has focused on the development of a
single information gathering agent. It is clear, however, that the
Infosphere is the natural playground of Multiple Intelligent
Coordinating Agents. This arises from the vastness of the available
information, the heterogeneity of the information resources, the
diversity of information gathering and problem solving tasks that the
gathered information supports, and the presence of multiple users with
related information needs.
This talk will address issues involved in designing multiple
Intelligent Agents that coordinate and learn from each other in order
to support information gathering and integration. In addition, the
talk will discuss opportunities in the Infosphere for multi-agent
research and development. We will draw examples from our work on the
PLEIADES project which is developing distributed collections of
Intelligent Agents that cooperate, negotiate and learn from each other
in performing goal-directed information retrieval and integration to
support a variety of problem solving tasks.
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Last Update: 3 April 95