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

Answer set programming with resources

In this paper, we propose an extension of Answer Set Programming (ASP) to support declarative reasoning on consumption and production of resources. We call the proposed extension RASP, standing for "Resourced ASP". Resources are modeled by introducing special atoms, called amount-atoms, to which we associate quantities that represent the available amount of a certain resource. The "firing" of a RASP-rule involving amount-atoms can both consume and produce resources. A RASP-rule can be fired several times, according to its definition and to the available quantities of required resources. We define the semantics for RASP programs by extending the usual answer set semantics. Different answer sets correspond to different possible allocations of available resources. We then propose an implementation based on standard ASP-solvers. The implementation consists of a standard translation of each RASP-rule into a set of plain ASP rules and of an inference engine that manages the firing of RASP-rules