Autonomous Agents and Multi-Agent Systems
Department of Computer Science
Mobile-agent systems allow applications to distribute their resource consumption across the network. By prioritizing applications and publishing the cost of actions, it is possible for applications to achieve faster performance than in an environment where resources are evenly shared. We enforce the costs of actions through markets where user applications bid for computation from host machines. \par We represent applications as collections of mobile agents and introduce a distributed mechanism for allocating general computational priority to mobile agents. We derive a bidding strategy for an agent that plans expenditures given a budget and a series of tasks to complete. We also show that a unique Nash equilibrium exists between the agents under our allocation policy. We present simulation results to show that the use of our resource-allocation mechanism and expenditure-planning algorithm results in shorter mean job completion times compared to traditional mobile-agent resource allocation. We also observe that our resource-allocation policy adapts favorably to allocate overloaded resources to higher priority agents, and that agents are able to effectively plan expenditures even when faced with network delay and job-size estimation error.
Jonathan Bredin, Rajiv T. Maheswaran, Çagri Imer, Tamer Başar, David Kotz, and Daniela Rus. Computational Markets to Regulate Mobile-Agent Systems. In Autonomous Agents and Multi-Agent Systems, May 2003. 10.1023/A:1022923422570
Dartmouth Digital Commons Citation
Bredin, Jonathan; David Kotz; Rus, Daniela; Maheswaran, Rajiv T.; Imer, Cagri; and Başar, Tamer, "Computational Markets to Regulate Mobile-Agent Systems" (2003). Dartmouth Scholarship. 3321.