Partially observable game-theoretic agent programming in Golog

DOI10.1016/j.ijar.2019.12.017zbMath1434.68583OpenAlexW2994961737WikidataQ126332028 ScholiaQ126332028MaRDI QIDQ2300459

Thomas Lukasiewicz, Alberto Finzi

Publication date: 27 February 2020

Published in: International Journal of Approximate Reasoning (Search for Journal in Brave)

Full work available at URL: https://ora.ox.ac.uk/objects/uuid:6f9e6eb1-f4e9-4fae-8111-8def7d64b70b

zbMATH Keywords

multi-agent systemscognitive roboticsreasoning about actions and changeagent programming

Mathematics Subject Classification ID

Applications of game theory (91A80) Theory of programming languages (68N15) Stochastic games, stochastic differential games (91A15) Problem solving in the context of artificial intelligence (heuristics, search strategies, etc.) (68T20) Artificial intelligence for robotics (68T40) Agent technology and artificial intelligence (68T42)

Related Items (2)

Special issue on probabilistic logic programming (PLP 2018) ⋮ POGTGolog

Uses Software

• GOLOG
• Gambit
• GTGolog

Cites Work

• Planning and acting in partially observable stochastic domains
• Learning generalized policies from planning examples using concept languages
• Computing Nash equilibria by iterated polymatrix approximation
• The independent choice logic for modelling multiple agents under uncertainty
• A global Newton method to compute Nash equilibria.
• Reasoning about noisy sensors and effectors in the situation calculus
• Some contributions to the metatheory of the situation calculus
• Strategy Representation and Reasoning in the Situation Calculus
• Equilibrium Points of Bimatrix Games
• Logics in Artificial Intelligence
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