A synthesis of automated planning and reinforcement learning for efficient, robust decision-making

DOI10.1016/j.artint.2016.07.004zbMath1392.68387OpenAlexW2518731509WikidataQ57948840 ScholiaQ57948840MaRDI QIDQ334800

Mohammad Hasan, M. Dambrine

Publication date: 1 November 2016

Published in: Artificial Intelligence (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.artint.2016.07.004

zbMATH Keywords

reinforcement learningrobot learninganswer-set programmingautomated planningautonomous robot

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Problem solving in the context of artificial intelligence (heuristics, search strategies, etc.) (68T20) Logic programming (68N17) Artificial intelligence for robotics (68T40)

Related Items (5)

An adaptive deep Q-learning strategy for handwritten digit recognition ⋮ Bridging Commonsense Reasoning and Probabilistic Planning via a Probabilistic Action Language ⋮ Unnamed Item ⋮ Deliberative acting, planning and learning with hierarchical operational models ⋮ Elaboration Tolerant Representation of Markov Decision Process via Decision-Theoretic Extension of Probabilistic Action Language +

Uses Software

• Potassco

Cites Work

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• Planning and acting in partially observable stochastic domains
• 10.1162/153244303765208377
• Finding similar/diverse solutions in answer set programming
• Recent advances in hierarchical reinforcement learning
• Relational reinforcement learning

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