Logic-based Technologies for Multi-agent Systems: A Systematic Literature Review

Precisely when the success of artificial intelligence (AI) sub-symbolic techniques makes them be identified with the whole AI by many non-computerscientists and non-technical media, symbolic approaches are getting more and more attention as those that could make AI amenable to human understanding. Given the recurring cycles in the AI history, we expect that a revamp of technologies often tagged as “classical AI” – in particular, logic-based ones will take place in the next few years. On the other hand, agents and multi-agent systems (MAS) have been at the core of the design of intelligent systems since their very beginning, and their long-term connection with logic-based technologies, which characterised their early days, might open new ways to engineer explainable intelligent systems. This is why understanding the current status of logic-based technologies for MAS is nowadays of paramount importance. Accordingly, this paper aims at providing a comprehensive view of those technologies by making them the subject of a systematic literature review (SLR). The resulting technologies are discussed and evaluated from two different perspectives: the MAS and the logic-based ones

Temporal Planning in Dynamic Environments for P-CLAIM Agents

National audienceTime and uncertainty of the environment are very important aspects in the development of real world applications. Another important issue for the real world agents is, the balance between deliberation and reactivity. But most of the agent oriented programming languages ignore some or all of these important aspects. In this paper we try to fill this gap by presenting an extension to the architecture of CLAIM agent oriented programming language to endow the agents with the planning capability. We remove the assumption that agents’ actions are instantaneous. We are interested in the temporal planning of on the fly goals. A coherrent framework is proposed in which agents are able to generate, monitor and repair their temporal plans. Our proposed framework creates a balance between reactivity and deliberation. This work could be considered as a first step towards a complete temporal planning solution for an AOP language