How New Individuals Behave in a Heterogeneous Community: A Computational Approach to Norm Assimilation Using Agent-Based Systems

Heterogeneous community entails a number of social groups that adopt similar/different social norms. In such community, new individuals who join a new social group should be able to decide with which group they could assimilate based on their capabilities/values/manners. Otherwise, they would be penalized by other members in the group for violating some norms which they cannot comply.Using this approach, software agents would have better reasoning in simulating human society. In this paper, the authors propose a norms assimilation theory, in which a new agent attempts to assimilate with a social group's norms. This theory builds an approach to norm assimilation, analyzes the cases for an agent to decide to assimilate with a social group and develops a mathematical model to measure the assimilation cost and the agent's ability. The approach is developed based on the agent's internal belief about its ability and desire, and its external belief about the cost of assimilating with a number of social groups. The significance of this research is two-fold. Firstly, the study paves the way to future design of intelligent systems, i.e., software agents or robots, to closely mimic human social interactions.Secondly, the norm assimilation using agent-based system could be potentially utilized to simulate some social issues such as immigrants, new students, expatriate etc. The experiments that have been conducted demonstrate that an agent in the domain is able to calculate the assimilation cost and decide which social group to join.     

Development and implementation of a technique for norms-adaptable agents in open multi-agent communities

In open normative multi-agent communities, an agent is not usually and explicitly given the norms of the host agents. Thus, when it is not able to adapt the communities’s norms, it is totally deprived of accessing resources and services from the host. Such circumstance severely affects its performance resulting in failure to achieve its goal. Consequently, this study attempts to overcome this deficiency by proposing a technique that enables an agent to detect the host’s potential norms via self-enforcement and update its norms even in the absence of sanctions from a third-party. The authors called this technique as the potential norms detection technique (PNDT). The PNDT consists of five components: Agent’s belief base; observation process; potential norms mining algorithm (PNMA); verification process; and updating process. The authors demonstrate the operation of the PNMA algorithm by testing it on a typical scenario and analyzing the results on several perspectives. The tests’ results show that the PNDT performs satisfactorily albeit the success rate depends on the environment variables settings.