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Anticipatory Behavior of Software Agents in Self-organizing Negotiations

  • Jan Ole BerndtEmail author
  • Otthein Herzog
Chapter
Part of the Cognitive Systems Monographs book series (COSMOS, volume 29)

Abstract

Software agents are a well-established approach for modeling autonomous entities in distributed artificial intelligence. Iterated negotiations allow for coordinating the activities of multiple autonomous agents by means of repeated interactions. However, if several agents interact concurrently, the participants’ activities can mutually influence each other. This leads to poor coordination results. In this paper, we discuss these interrelations and propose a self-organization approach to cope with that problem. To that end, we apply distributed reinforcement learning as a feedback mechanism to the agents’ decision-making process. This enables the agents to use their experiences from previous activities to anticipate the results of potential future actions. They mutually adapt their behaviors to each other which results in the emergence of social order within the multiagent system. We empirically evaluate the dynamics of that process in a multiagent resource allocation scenario. The results show that the agents successfully anticipate the reactions to their activities in that dynamic and partially observable negotiation environment. This enables them to maximize their payoffs and to drastically outperform non-anticipating agents.

Keywords

Nash Equilibrium Team Manager Combinatorial Auction Acceptance Level Member Agent 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Center for Computing and Communication Technologies (TZI)Universität BremenBremenGermany

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