Recognizing and learning models of social exchange strategies for the regulation of social interactions in open agent societies

Brazilian Computer Society. Journal

Costa¹  Graç²  Antô²  Rocha ³  Diego Rodrigues Pereira⁶  alves⁸  nio Carlos da ⁹  aliz Pereira Dimuro⁹  Luciano Vargas Gonç^1,11 

[1] ãCentro de CiêInstituto Federal de EducaçPrograma de Póado de Farroupilha, Farroupilha, Brazil;cleo Avançncia da Computaçncia e Tecnologia do Rio Grande do Sul, Núncias Computacionais, Universidade Federal do Rio Grande, Rio Grande, Brazil;o em Ciêo, Ciêo, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil;s-Graduaç

关键词: Social exchange strategy;    Recognition and learning of social exchange strategies;    Self-regulation of social exchange strategies;    Partially observable Markov decision process;    Hidden Markov model;   

DOI  :  10.1007/s13173-011-0035-5

学科分类：农业科学（综合）

来源: Springer U K

【 摘 要 】

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.

【 授权许可】

CC BY