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Logic-based technologies for multi-agent systems: a systematic literature review

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TLDR
A comprehensive view of logic-based technologies for agents and multi-agent systems is provided by making them the subject of a systematic literature review (SLR) and the resulting technologies are discussed and evaluated from two different perspectives.
Abstract
Precisely when the success of artificial intelligence (AI) sub-symbolic techniques makes them be identified with the whole AI by many non-computer-scientists 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.

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Citations
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References
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Cochrane Handbook for Systematic Reviews of Interventions

TL;DR: The Cochrane Handbook for Systematic Reviews of Interventions is the official document that describes in detail the process of preparing and maintaining Cochrane systematic reviews on the effects of healthcare interventions.
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Artificial Intelligence: A Modern Approach

TL;DR: In this article, the authors present a comprehensive introduction to the theory and practice of artificial intelligence for modern applications, including game playing, planning and acting, and reinforcement learning with neural networks.
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Graph-Based Algorithms for Boolean Function Manipulation

TL;DR: In this paper, the authors present a data structure for representing Boolean functions and an associated set of manipulation algorithms, which have time complexity proportional to the sizes of the graphs being operated on, and hence are quite efficient as long as the graphs do not grow too large.
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Intelligent Agents: Theory and Practice

TL;DR: Agent theory is concerned with the question of what an agent is, and the use of mathematical formalisms for representing and reasoning about the properties of agents as discussed by the authors ; agent architectures can be thought of as software engineering models of agents; and agent languages are software systems for programming and experimenting with agents.
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