Experiments on robustness and deception in a coalition formation model

In the last few years coalition formation algorithms have been proposed as a possible way of modeling autonomous agent cooperation in multi‐agent systems. This work is based on a previously proposed coalition formation model founded on game theory for a class of task‐oriented problems that guarantees an optimum task allocation and a stable profit division. In this paper we study two properties of the model that are very important for application in real‐life scenarios: robustness and tolerance to an agent's misbehavior. First, we study the robustness of this model as regards the effect the agent's failure has on the resultant profits of the coalition formation. Secondly, we also study the coalition formation model in the presence of misbehaving agents. Agents have some kind of execution autonomy, and they can deceive or mislead each other when they reveal their information, if they believe this will give them more profits. Copyright © 2005 John Wiley & Sons, Ltd.

[1]  Ricardo Conejo,et al.  A Stable and Feasible Payoff Division for Coalition Formation in a Class of Task Oriented Domains , 2001, ATAL.

[2]  Anatol Rapoport,et al.  Theories of Coalition Formation , 1998 .

[3]  Sarit Kraus,et al.  Feasible Formation of Coalitions Among Autonomous Agents in Nonsuperadditive Environments , 1999, Comput. Intell..

[4]  Hector J. Levesque,et al.  The adaptive agent architecture: achieving fault-tolerance using persistent broker teams , 2000, Proceedings Fourth International Conference on MultiAgent Systems.

[5]  Jeffrey S. Rosenschein and Gilad Zlotkin.  Rules of Encounter , 1994 .

[6]  Alan Burns,et al.  Real-time systems and their programming languages , 1986, International computer science series.

[7]  Felix F. Wu,et al.  A kernel-oriented algorithm for transmission expansion planning , 2000 .

[8]  Rachid Guerraoui,et al.  Software-Based Replication for Fault Tolerance , 1997, Computer.

[9]  Katia P. Sycara,et al.  Customer coalitions in the electronic marketplace , 2000, AGENTS '00.

[10]  Onn Shehory,et al.  Coalition structure generation with worst case guarantees , 2022 .

[11]  Guillermo Ricardo Simari,et al.  Multiagent systems: a modern approach to distributed artificial intelligence , 2000 .

[12]  Sarit Kraus,et al.  Methods for Task Allocation via Agent Coalition Formation , 1998, Artif. Intell..

[13]  Tuomas Sandholm,et al.  Distributed rational decision making , 1999 .

[14]  Ralph Deters,et al.  Improving fault-tolerance by replicating agents , 2002, AAMAS '02.

[15]  O. Marin.  The DARX Framework: Adaptive Fault Tolerance for Agent Systems , 2003 .

[16]  Staffan Haegg,et al.  A Sentinel Approach to Fault Handling in Multi-Agent Systems , 1996, DAI.

[17]  Leonard N. Foner,et al.  Entertaining agents: a sociological case study , 1997, AGENTS '97.

[18]  Sandip Sen,et al.  Searching for optimal coalition structures , 2000, Proceedings Fourth International Conference on MultiAgent Systems.

[19]  Peter Fankhauser,et al.  Arbitration Protocols for Competing Software Agents , 2001, Int. J. Cooperative Inf. Syst..