Maximizing Expected Impact in an Agent Reputation Network -- Technical Report
This work addresses reputation management in multi-agent systems, which is incremental as it builds on existing POMDP methods with new directed features.
The paper tackles the problem of planning in stochastic multi-agent systems where agents must maintain reputation to survive, proposing a new POMDP-based framework with directed actions and a transition function for reputation effects, and develops a planning algorithm with preliminary evaluation of its complexity.
Many multi-agent systems (MASs) are situated in stochastic environments. Some such systems that are based on the partially observable 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 reputations. 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.