Maximizing Expected Impact in an Agent Reputation Network

Rens, Gavin and Nayak, Abhaya and Meyer, Thomas (2018) Maximizing Expected Impact in an Agent Reputation Network, Proceedings of KI 2018: Advances in Artificial Intelligence 41st German Conference on AI, 24-28 September 2018, Berlin, Germany, 11117, 99-106, Springer.

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Many multi-agent systems (MASs) are situated in stochastic environments. Some such systems that are based on the partially observ- able Markov decision process (POMDP) do not take the benevolence of other agents for granted. We propose a new POMDP-based framework which is general enough for the specification of a variety of stochastic MAS domains involving the impact of agents on each other’s reputa- tions. A unique feature of this framework is that actions are specified as either undirected (regular) or directed (towards a particular agent), and a new directed transition function is provided for modeling the effects of reputation in interactions. Assuming that an agent must maintain a good enough reputation to survive in the network, a planning algorithm is developed for an agent to select optimal actions in stochastic MASs. Preliminary evaluation is provided via an example specification and by determining the algorithm’s complexity.

Item Type: Conference paper
Uncontrolled Keywords: Learning Trust and reputation Planning Uncertainty POMDP
Subjects: Computing methodologies > Artificial intelligence
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Date Deposited: 10 Jan 2019
Last Modified: 10 Oct 2019 15:31

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