Formal model of multi-agent architecture of a software system based on knowledge interpretation

Abstract

The use of agents across diverse domains within computer science and artificial intelligence is experiencing a notable surge in response to the imperatives of adaptability, efficiency, and scalability. The subject of this study is the application of formal methods to furnish a framework for knowledge interpretation with a specific focus on the agent-based paradigm in software engineering. This study aims to advance a formal approach to knowledge interpretation by leveraging the agent-based paradigm. The objectives are as follows: 1) to examine the current state of the agent-based paradigm in software engineering; 2) to describe the basic concepts of the knowledge interpretation approach; 3) to study the general structure of the rule extraction task; 4) to develop the reference structure of knowledge interpretation; 5) to develop a multi-agent system architecture; 6) and to discuss the research results. This study employs formal methods, including the use of closed path rules and predicate logic. Specifically, the integration of closed path rules contributes to the extraction and explication of facts from extensive knowledge bases. The obtained results encompass the following: 1) a rule mining approach grounded in closed path rules and tailored for processing extensive datasets; 2) a formalization of relevance that facilitates the scrutiny and automated exclusion of irrelevant fragments from the explanatory framework; and 3) the realization of a multi-agent system predicated on the synergy among five distinct types of agents, dedicated to rule extraction and the interpretation of acquired knowledge. This paper provides an example of the application of the proposed formal tenets, demonstrating their practical context. The conclusion underscores that the agent-based paradigm, with its emphasis on decentralized and autonomous entities, presents an innovative framework for handling the intricacies of knowledge processing. It extends to the retrieval of facts and rules. By distributing functions across multiple agents, the framework offers a dynamic and scalable solution to effectively interpret vast knowledge repositories. This approach is particularly valuable in scenarios where traditional methods may struggle to cope with the volume and complexity of information.