In this paper, we present a framework for resolving conflicts between personal and normative goals in normative agent systems. The conflicts occur in the decision making process of time-constrained tasks of those goals. The agents observe the environment and perform the tasks based on their obligation to an authority, their desires, and intentions. They select and execute the tasks from a set of pre-compiled tasks based on their beliefs of the reward and penalty associated with the selected tasks. To resolve the conflicts within the constraint of the tasks’ duration, we supplement the agents’ normative capacity with two essential functions: Sacrifice and Diligence. The Sacrifice function enables an agent to reason and discard any tasks that have lower priorities to make way for accomplishment of the normative goal. The Diligence function enables an agent to increase its effort in accomplishing the normative goal in time-constrained situations. We simulate these situations and present the results.
This paper presents a contribution to research on norms detection by proposing a technique, which is called the Potential Norms Detection Technique (PNDT). The literature proposes that an agent changes or updates its norms based on the variables of the local environment and the amount of thinking about its behaviour. Consequently, any changes on these two variables cause the agent to use the PNDT to update the norms in complying with the domain’s normative protocol. This technique enables an agent to update its norms even in the absence of sanctions from a third-party enforcement authority as found in some work, which entail sanctions by a third-party to detect and identify the norms. The PNDT consists of five components: agent’s belief base; observation process; Potential Norms Mining Algorithm (PNMA) to detect the potential norms and identify the normative protocol; verification process, which verifies the detected potential norms; and updating process, which updates the agent’s belief base with new normative protocol. The authors then demonstrate the operation of the algorithm by testing it on a typical scenario and analyse the results on several issues.
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