Negotiation protocol comparison for task allocation in highly dynamic environments

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Abstract

A robust negotiation protocol is required for a multi-agent simulation involving two adversarial teams in a highly dynamic and hostile environment. In this environment agent failure is possible due to a number of circumstances such as running out of fuel or being destroyed by other agents. This paper compares three existing negotiation protocols: the Contract Net Protocol (Smith, 1980), the Distributed Contract Net Protocol (Cano and Carbo, 2006), the Extended Contract Net Protocol (Aknine et al., 2004) and two protocols developed by the authors (termed herein the `Simple' and `Hybrid' protocols). The objective of this paper is to determine which protocol is best suited to our application in terms of scalability, robustness against agent failure, communication overhead, and response time. To evaluate these negotiation protocols an experiment was conducted, involving three different test cases, which varied the availability of agents at different stages of the negotiation process. In these test cases a team of software agents (the `blue team') were tasked with destroying a number of stationary targets (the `red team'). The experimental results showed that the Contract Net Protocol (CNP) was suitable for low risk environments due to its low communication overhead, while the Distributed Contract Net Protocol (DCNP) was more suitable for high-risk environments due to its greater robustness against agent failure. However, this robustness was achieved at the expense of greatly increased communication. An alternate approach that showed promising results was to use a Hybrid protocol that switched between CNP and DCNP depending on the environment. Additional work is required to develop the Hybrid protocol further.

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APA

Noack, K., Marsh, L., & Shekh, S. (2015). Negotiation protocol comparison for task allocation in highly dynamic environments. In Proceedings - 21st International Congress on Modelling and Simulation, MODSIM 2015 (pp. 718–724). Modelling and Simulation Society of Australia and New Zealand Inc. (MSSANZ). https://doi.org/10.36334/modsim.2015.c6.noack

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