Michael N. Huhns (bio) (abstract)
University of South Carolina
From DPS to MAS to .:† Continuing the Trends


Klaus G. Troitzsch (abstract)
Universitšt Koblenz-Landau
Perspectives and Challenges of Agent-Based Simulation as a Tool for
Economics and Other Social Sciences

Manuela M. Veloso (abstract)
ACM/SIGART Autonomous Agents Research Award 2009
Computer Science Department, Carnegie Mellon University
Teams of Robots: A Fascinating Multiagent Research Adventure


Michael N. Huhns
University of South Carolina

Dr. Michael N. Huhns is the NCR Professor of Computer Science and Engineering and director of the Center for Information Technology at the University of South Carolina.† His degrees in electrical engineering are from the University of Michigan (B.S.) and the University of Southern California (M.S. and Ph.D.).† He is the author of six books and more than 200 papers in multiagent systems, service-oriented computing, and ontologies. With Munindar Singh, he coauthored the textbook Service-Oriented Computing: Semantics, Processes, Agents [Wiley 2005].† He serves on the editorial boards for 12 journals and is a founding member of the International Foundation for Multiagent Systems, a Senior Member of the ACM, and a Fellow of the IEEE.†

Abstract: From DPS to MAS to .:† Continuing the Trends

The most interesting of the computing challenges are those that involve the problems and opportunities afforded by massive decentralization.† The problems and opportunities arise in domains where controlled action is necessary, but centralized control is infeasible.† These are the traditional domains of distributed problem solving and multiagent systems, and they include
  • Healthcare for patients
  • Grocery shopping for consumers
  • Re-architected IT systems for the U.S. Navy
  • Individualized traffic control
  • Energy distribution
  • Public finances
  • Bandwidth allocation
However, the current incarnations of these domains are scaled well beyond anything envisioned originally.† Nevertheless, traditional techniques derived from artificial intelligence are still mostly appropriate.† Specifically, representation, reasoning, learning, planning, and situated semantics -- when distributed computationally and extended to multiple loci of intelligence -- will all be part of potential solutions.† They will affect not only the ways systems will be implemented and executed, but also the ways they will be developed.† Newer aspects of solutions will include:
  • Agents that represent individual preferences
  • Market mechanisms
  • Consensus behavior
This talk will focus on the domains and their challenges.† It will then describe the trends that I have observed in our research technologies and show how they can be used to confront the challenges.† It is hoped that new avenues of research will be revealed from following the trends.


Klaus G. Troitzsch

Universitšt Koblenz-Landau

Abstract: Perspectives and Challenges of Agent-Based Simulation as a Tool for
Economics and Other Social Sciences

This talk will argue that the agent-based simulation approach is just the one appropriate to the social sciences (including economics). Although there were many predecessor approaches, which tried to build formal models of social systems, all of them fell short of the peculiar features of the objects of all social sciences: complex systems consisting of numerous
autonomous actors who interact with each other, who take on different roles at the same time, who are conscious of their interactions and roles and who can communicate with the help of symbolic languages even about the counterfactual.

These human actors are unlike physical particles although their behaviour might sometimes be quite similar to physical particles when humans occur in very large numbers (but they are most interesting when they form only small networks). Real human actors would not concede that their behaviour is stochastic, they will always assert that their actions are deliberate (but
at the same time these actions are not entirely predictable). Human actors are not entirely rational although their behaviour might sometimes seem as if they were (but they are most interesting when their rationality is only bounded and when their payoff is multidimensional).

Social systems seem to be the most adaptive systems that we know about, and this is why we could perhaps use them as patterns for artificial adaptive systems --- and if we knew enough about the modes of operations of human social systems, social sciences could even contribute to agent-based modelling in other fields.

Manuela M. Veloso
Computer Science Department
Carnegie Mellon University

The selection committee for the ACM/SIGART Autonomous Agents Research Award is pleased to announce that Prof. Manuela M. Veloso of Carnegie Mellon University is the recipient of the 2009 award. Prof. Veloso has made significant and sustained contributions to Autonomous Agents and Multiagent Systems in the areas of planning and control learning in multi-agent systems. Prof. Veloso's research is particularly noteworthy for its focus on the effective construction of teams of robot agents where cognition, perception, and action are seamlessly integrated to address planning, execution, and learning tasks. She has made significant contributions to agents in uncertain and dynamic environments, including distributed robot localization and world modeling, strategy selection in multiagent systems in the presence of adversaries, planning by analogical reuse, and more recently, robot learning from demonstration. Her research contributions have also been realized concretely in the form of teams of robot soccer playing agents that have won several international championships at the annual RoboCup robot soccer competitions. Her impact and visibility has been consistently high over the past two decades for her technical contributions, for her impressive robot teams, and for her leadership within the research community.

Abstract: Teams of Robots: A Fascinating Multiagent Research Adventure

I will share my challenging journey of research on multi-robot systems. Robots are physical agents with a seamless integration of perception, cognition, and action. My presentation will be focused on teams of intelligent autonomous robots performing tasks in highly uncertain domains, in particular in robot soccer and indoor tasks. Robots need to jointly assess the state of their environment, communicate with each other, make decisions, execute actions towards the achievement of team objectives, and learn from observation and feedback based on the outcome of their actions.† I will present the solutions we created, and discuss some of many remaining open questions. The talk reports on joint work with my extraordinary past and present students.