An Information-Theoretic Analysis of Memory Bounds in a Distributed Resource Allocation Mechanism

Multiagent distributed resource allocation requires that agents act on limited, localized information with minimum communication overhead in order to optimize the distribution of available resources. When requirements and constraints are dynamic, learning agents may be necessary to allow for adaptation. One way of accomplishing learning is to observe past outcomes and use this information in order to improve future decisions. When limits in agents’ memory or observation capabilities are assumed, one must decide on how large should the observation window be. We investigate how this decision influences both agents’ and system’s performance in the context of a special class of distributed resource allocation problems, namely dispersion games. Our contribution is twofold. First, we show by numerical experiments over a specific dispersion game (the Minority Game) that in such scenario an agent’s performance is nonmonotonically correlated with her memory size when all other agents...

• Type: Conference
• Year: 2007
• Where: IJCAI
• Authors: Ricardo M. Araujo, Luís C. Lamb
• Keywords: Artificial Intelligence, Dispersion Game, Distributed Resource Allocation, IJCAI 2007, Multiagent Distributed Resource