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We propose a novel method for assessing the reputation of agents in multiagent systems that is capable of exploiting the structure and semantics of rich agent interaction protocols and agent communication languages. Our method is based on using so-called conversation models, i.e. succinct, qualitative models of agents' behaviours derived from the application of data mining techniques on protocol execution data in a way that takes advantage of the semantics of inter-agent communication available in many multiagent systems. Contrary to existing systems, which only allow for querying agents regarding their assessment of others' reputation in an outcome-based way (often limited to distinguishing between "successful" and "unsuccessful" interactions), our method allows for contextualised queries regarding the structure of past interactions, the values of content variables, and the behaviour of agents across different protocols. Moreover, this is achieved while preserving maximum privacy for the reputation querying agent and the witnesses queried, and without requiring a common definition of reputation, trust or reliability among the agents exchanging reputation information. A case study shows that, even with relatively simple reputation measures, our qualitative method outperforms quantitative approaches, proving that we can meaningfully exploit the additional information afforded by rich interaction protocols and agent communication semantics.
|Title of host publication||Proceedings of the Eleventh International Conference on Autonomous Agents Multiagent Systems (AAMAS 2012)|
|Number of pages||8|
|Publication status||Published - 2012|