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Previous work modeled the social exchange regulation problem as a POMDP (Partially Observable Markov Decision Process), and defined the<jats:bold>policyToBDIplans<\/jats:bold>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\u2019 social exchange strategies, thus extending its applicability to open societies, where new partner agents can freely appear at any time. For the recognition problem,<jats:italic>patterns of refusals<\/jats:italic>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\u2019s 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\u00a0discussion 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.<\/jats:p>","DOI":"10.1007\/s13173-011-0035-5","type":"journal-article","created":{"date-parts":[[2011,8,5]],"date-time":"2011-08-05T15:31:33Z","timestamp":1312558293000},"page":"143-161","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":26,"title":["Recognizing and learning models of social exchange strategies for the regulation of social interactions in open agent societies"],"prefix":"10.1007","volume":"17","author":[{"given":"Gra\u00e7aliz Pereira","family":"Dimuro","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ant\u00f4nio Carlos","family":"da\u00a0Rocha\u00a0Costa","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Luciano Vargas","family":"Gon\u00e7alves","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Diego Rodrigues","family":"Pereira","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2011,8,6]]},"reference":[{"issue":"9","key":"35_CR1","doi-asserted-by":"publisher","first-page":"1049","DOI":"10.1016\/j.robot.2010.05.006","volume":"58","author":"E Aaron","year":"2010","unstructured":"Aaron E, Admoni H (2010) Action selection and task sequence learning for hybrid dynamical cognitive agents. 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