2013/02/28 by Nils Bulling, Valentin Goranko
Computer Science · Mathematics · #Artificial intelligence #Bayesian Modeling and Causal Inference #Computer science #Economics #Extension (predicate logic) #Game theory #Human–computer interaction #Logic, Reasoning, and Knowledge #Logical analysis #Logical framework #Logical reasoning #Machine learning #Management science #Mathematical economics #Mathematics #Multi-Agent Systems and Negotiation #Programming language #Qualitative property #Qualitative reasoning #Quantitative analysis (chemistry) #cs.LO #cs.MA
paper · pdf · doi:10.4204/eptcs.112.8
published as EPTCS 112, 2013, pp. 33-41 · In Proceedings SR 2013, arXiv:1303.0071
openalex publication_date 2013/02/28 · arxiv created 2013/03/04 · arxiv updated 2013/03/05 · openalex created_date 2016/06/24 · openalex updated_date 2026/08/05
We propose a logical framework combining a game-theoretic study of abilities of agents to achieve quantitative objectives in multi-player games by optimizing payoffs or preferences on outcomes with a logical analysis of the abilities of players for achieving qualitative objectives of players, i.e., reaching or maintaining game states with desired properties. We enrich concurrent game models with payoffs for the normal form games associated with the states of the model and propose a quantitative extension of the logic ATL* enabling the combination of quantitative and qualitative reasoning.