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Knowledge gives a strong support to autonomous agents in multi-agent systems and thus the evolution of agent’s knowledge needs a great attention since it has a control on agents’ behaviors and has effect on their decisions making. The problem is to allow agents to detect and decide whether they need more domain knowledge and allow their knowledge to evolve consistently and automatically. This paper utilizes ontologies to represent the internal knowledge of agents instead of utilizing them only as a shared conceptualization. Consequently, the paper proposes a model of bottom-up instance-driven ontology evolution that allows the internal ontologies of agents to evolve automatically and consistently in run time based on agents’ interactions. Experiments are designed and implemented to evaluate our model in different situations. One of its results shows that an empty internal ontology of one agent could evolve automatically in runtime by 88.3% through its interactions with other agents. Moreover, a comparison between the proposed approach and literature review approaches is presented to compare between their different features and techniques. This paper is considered a step forward to automate ontology evolution for agents in multiagent environment.

BOEM: A Model for Automating Detection and Evolution of Distributed Ontologies in Multi-Agent Environment