Abstract
Abstract Regulation of social exchanges refers to controlling social exchanges between agents so that the balance of exchange values involved in the exchanges are continuously kept—as far as possible—near to equilibrium. Previous work modeled the social exchange regulation problem as a POMDP (Partially Observable Markov Decision Process), and defined the policyToBDIplans algorithm to extract BDI (Beliefs, Desires, Intentions) plans from POMDP models, so that the derived BDI plans can be applied to keep in equilibrium social exchanges performed by BDI agents. The aim of the present paper is to extend that BDI-POMDP agent model for self-regulation of social exchanges with a module, based on HMM (Hidden Markov Model), for recognizing and learning partner agents’ social exchange strategies, thus extending its applicability to open societies, where new partner agents can freely appear at any time. For the recognition problem, patterns of refusals of exchange proposals are analyzed, as such refusals are produced by the partner agents. For the learning problem, HMMs are used to capture probabilistic state transition and observation functions that model the social exchange strategy of the partner agent, in order to translate them into POMDP’s action-based state transition and observation functions. The paper formally addresses the problem of translating HMMs into POMDP models and vice versa, introducing the translation algorithms and some examples. A discussion on the results of simulations of strategy-based social exchanges is presented, together with an analysis about related work on social exchanges in multiagent systems.
Highlights
In Piaget’s Theory of Social Exchanges [41], social interactions are seen as service exchanges between pairs of agents, together with the subjective evaluation of those exchanges by the agents themselves, by means of the so-called social exchange values: the investment value for performing a service or the satisfaction value for receiving it
A society is said to be in social equilibrium if the balances of the exchange values are equilibrated for the successive exchanges occurring along the time
For our hybrid BDI-POMDP model [38] to have the social exchange regulation process internalized in the agent model, we introduced the policyToBDIplans algorithm, which extracts BDI plans from policy graphs related to optimal policies of POMDP models defined for the different social exchange strategies that the partner agents may follow, one plan for each different exchange strategy [39]
Summary
In Piaget’s Theory of Social Exchanges [41], social interactions are seen as service exchanges between pairs of agents, together with the subjective evaluation of those exchanges by the agents themselves, by means of the so-called social exchange values: the investment value for performing a service or the satisfaction value for receiving it. The BDI-POMDP-HMM agent transforms the acquired HMM’s probabilistic transition and observation functions into POMDP’s probabilistic action-based state transition and observation functions, obtaining a POMDP model of the previously unknown social exchange strategy, allowing the extraction of BDI plans for the regulation process, by using the policyToBDIplans algorithm [38, 39]. Another challenge that we are addressing in this paper is how to integrate a POMDP model and a HMM, that.
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