Abstract
We propose a new framework for reasoning about the reputation of multiple agents, based on the partially observable Markov decision process (POMDP). It is general enough for the specification of a variety of stochastic multi-agent system (MAS) domains involving the impact of agents on each other’s reputations. Assuming that an agent must maintain a good enough reputation to survive in the system, a method for an agent to select optimal actions is developed.
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Acknowledgements
Gavin Rens was supported by a Clause Leon Foundation postdoctoral fellowship while conducting this research. This research has been partially supported by the Australian Research Council (ARC), Discovery Project: DP150104133 as well a grant from the Faculty of Science and Engineering, Macquarie University. This work is based on research supported in part by the National Research Foundation of South Africa (Grant number UID 98019). Thomas Meyer has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agr. No. 690974.
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Rens, G., Nayak, A., Meyer, T. (2018). Maximizing Expected Impact in an Agent Reputation Network. In: Trollmann, F., Turhan, AY. (eds) KI 2018: Advances in Artificial Intelligence. KI 2018. Lecture Notes in Computer Science(), vol 11117. Springer, Cham. https://doi.org/10.1007/978-3-030-00111-7_9
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DOI: https://doi.org/10.1007/978-3-030-00111-7_9
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