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      Reasoning in Highly Reactive Environments

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          Abstract

          The aim of my Ph.D. thesis concerns Reasoning in Highly Reactive Environments. As reasoning in highly reactive environments, we identify the setting in which a knowledge-based agent, with given goals, is deployed in an environment subject to repeated, sudden and possibly unknown changes. This is for instance the typical setting in which, e.g., artificial agents for video-games (the so called "bots"), cleaning robots, bomb clearing robots, and so on are deployed. In all these settings one can follow the classical approach in which the operations of the agent are distinguished in "sensing" the environment with proper interface devices, "thinking", and then behaving accordingly using proper actuators. In order to operate in an highly reactive environment, an artificial agent needs to be: 1. Responsive -> The agent must be able to react repeatedly and in a reasonable amount of time; 2. Elastic -> The agent must stay reactive also under varying workload; 3. Resilient -> The agent must stay responsive also in case of internal failure or failure of one of the programmed actions in the environment. Nowadays, thanks to new technologies in the field of Artificial Intelligence, it is already technically possible to create AI agents that are able to operate in reactive environments. Nevertheless, several issues stay unsolved, and are subject of ongoing research.

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          Answer Set Programming for Procedural Content Generation: A Design Space Approach

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            Reactive Answer Set Programming

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              Grounding and Solving in Answer Set Programming

              Answer set programming is a declarative problem solving paradigm that rests upon a workflow involving modeling, grounding, and solving. While the former is described by Gebser and Schaub (2016), we focus here on key issues in grounding, or how to systematically replace object variables by ground terms in a effective way, and solving, or how to compute the answer sets of a propositional logic program obtained by grounding.
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                Author and article information

                Journal
                18 September 2019
                Article
                10.4204/EPTCS.306.57
                1909.08260
                5624044b-9134-422c-8103-509efb4fa551

                http://arxiv.org/licenses/nonexclusive-distrib/1.0/

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                Custom metadata
                EPTCS 306, 2019, pp. 420-426
                In Proceedings ICLP 2019, arXiv:1909.07646
                cs.AI cs.LO
                EPTCS

                Theoretical computer science,Artificial intelligence
                Theoretical computer science, Artificial intelligence

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