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                | INVITED SPEAKERS  | 
               
               
                
                  
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                    Michael N. Huhns (bio) (abstract)  
                      University of South Carolina 
                      From DPS to MAS to .: Continuing the Trends | 
                   
                 
                    
                  
                    
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                      Klaus G. Troitzsch (abstract) 
                      Universität Koblenz-Landau 
                      Perspectives and Challenges of Agent-Based Simulation as a Tool  for 
Economics and Other Social Sciences | 
                     
                                     
                   
 
                   
                     
                  
                    
                      
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                        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 | 
                       
                     
                     
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                        Michael N. Huhns bio 
                        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. 
                    
                    
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                    Klaus G. Troitzsch
                  Universität 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.  | 
               
              
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                    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.  
                      
                      
                      
                      
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