Reputation-driven Decision-making in Networks of Stochastic Agents
This work addresses reputation-driven interactions in multi-agent systems, but it is incremental as it builds on and refines an existing framework.
The paper tackles the problem of multi-agent decision-making in networks where reputation is crucial, proposing RepNet-MDP to address mathematical inconsistencies and intractability in prior work, with experiments showing agents can adapt behavior based on others' reliability.
This paper studies multi-agent systems that involve networks of self-interested agents. We propose a Markov Decision Process-derived framework, called RepNet-MDP, tailored to domains in which agent reputation is a key driver of the interactions between agents. The fundamentals are based on the principles of RepNet-POMDP, a framework developed by Rens et al. in 2018, but addresses its mathematical inconsistencies and alleviates its intractability by only considering fully observable environments. We furthermore use an online learning algorithm for finding approximate solutions to RepNet-MDPs. In a series of experiments, RepNet agents are shown to be able to adapt their own behavior to the past behavior and reliability of the remaining agents of the network. Finally, our work identifies a limitation of the framework in its current formulation that prevents its agents from learning in circumstances in which they are not a primary actor.