Regulating social exchanges in open MAS: The problem of reciprocal conversions between POMDPs and HMMs
An important problem in open multiagent systems is that of the regulation of social exchanges, toward producing social equilibrium. This problem may be generalized to the regulation of autonomous agents interactions when cooperating/competing in order to achieve their individual, collective objectives. In this paper, we take an abstract and generalizing approach to this issue. The problem is formalized as a regulation model for the sequential decision making of an agent, acting in an open partially observable stochastic environment, with the aim to induce another autonomous agent to interact in certain way, so as to lead both agents toward a target exchange state configuration. The regulation model is defined as a combination of a partially observable Markov decision process (POMDP), to structure the regulator agent decision process, with a Hidden Markov Model (HMM), to structure its exchange strategy learning process. The main challenge we face is the reciprocal conversion between POMDPs and HMMs. The solution we have found builds on the particular structures of the POMDPs and HMMs that arise in the context of the regulation of social exchanges, which allow for the establishment of a kind of isomorphism between the two models. This paper formally develops these ideas, stating and proving the conversion theorems, and shows their application to an example of regulation of social exchanges.
multiagent systems partially observable markov decision processes hidden markov models social exchange processes regulation of exchange processes social simulation