Mone: Mutation Oriented Norm Evolution

• Published in: IEEE access 2020, Vol.8, p.205386-205395
• Main Authors: Wang, Xianchang, Fu, Ronghao, Zhang, Rui
• Format: Article
• Language: English
• Subjects: Algorithms; Blackboard-system; Context; Evolution; Legged locomotion; Monitoring; Multi-Agent System; Mutation; Norm Revision; Norms; Robots; Semantics; Task analysis; Terminology; Testing
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2020.3037798

A norm regulates the run-time behavior of the agent with the action and condition to trigger the action. Because of the incomplete understanding of the world, the result of the action may be different for the same agent with the 'same' context, or unintended different context. To study this phenomenon, the classical norm definition is extended from the condition-action pair to cover the expectation , in order to verify the result of the action. The Norm evolution can be defined as a gradual process which makes a norm more complete and effective. In the terminology of evolution, a norm is called mutated if the result contradicts to the expectation, i.e. at least one of the expected conditions is invalid. At run-time, norms are executed in series. A mutation brings new knowledge to the following states and might affect the later execution of the norms. Such knowledge provides will help the norm designer to complete the definitions. A mutation based norm evolution (Mone) method is proposed in this paper to detect the mutations, to propagate the evidence and to crossover the norms for completeness. The method is formalized in the Description Logic, and implemented with the algorithms for mutation detection and norm crossover. The case study illustrates the Description Logic \mathcal {ALCI} of the method and shows the potential to evolve the norms autonomously in the Blackboard system, GBBopen.