0638656 20260225144518.820250905235959.9 4 s. E cav_un_epca*064623916-19PragueCzech University of Life Sciences Prague2025GuyTatiana ValentinePelikánMartinKárnýMiroslavGajAleksejRužejnikovJurijRumanMarko markov decision process (MDP) multiagent systems trust modelling policy inference agent interaction dynamics information fusion cav_un_auth*0491463 Ružejnikov Jurij UTIA-B Adaptivní systémy Department of Adaptive Systems AS AS CZ 80 K Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0101092 Guy Tatiana Valentine UTIA-B Adaptivní systémy Department of Adaptive Systems AS AS Department of Adaptive Systems Ústav teorie informace a automatizace AV ČR, v. v. i. https://library.utia.cas.cz/separaty/2025/AS/ruzejnikov-0638656.pdf CA21169 EU-COST XE cav_un_auth*0452289 In multiagent systems (MAS), agents often share policy information to influence one another’s decisions. Agent interactions can be categorized as either adversarial or cooperative, and these behaviors can be intentional or unintentional. In the intentional case, agents may share misleading policies to either hinder or support other agents’ decision-making, whereas in the unintentional case, the interaction is merely incidental. From a single-agent perspective, the agent must be able to adapt to various interaction types. This work models MAS using the Multiagent Markov Decision Process (MMDP) and introduces a necessary condition for both intentional cooperative and adversarial interactions. We classify possible policy communications by their truthfulness and intent, and we lay the groundwork for a dynamic, trust-based framework that allows an agent to evaluate and incorporate shared policy information. The proposed approach enables robust and adaptive behaviour in both cooperative and adversarial environments. cav_un_auth*0491465 DYNALIFE 2025 : Conference on QUANTUM INFORMATION AND DECISION MAKING IN LIFE SCIENCES 20250428 20250429 Prague CZ BB 10000 10100 10101 2026 2 PO RVO:67985556 https://hdl.handle.net/11104/0370224 cav_un_auth*0322033 Česká zemědělská univerzita v Praze, Provozně ekonomická fakulta PEF ČZU CZ S BERGERSON, Sage, 2021. Multi-agent inverse reinforcement learning: Suboptimal demonstrations and alternative solution concepts. 2025 KÁRNÝ, Miroslav, and HŮLA, František, 2021. Fusion of probabilistic unreliable indirect information into estimation serving to decision making. International Journal of Machine Learning and Cybernetics. Vol. 12, no. 12, pp. 3367–3378. 2025 QUINN, Anthony, KÁRNÝ, Miroslav, and GUY, Tatiana V., 2017. Optimal design of priors constrained by external predictors. International Journal of Approximate Reasoning. Vol. 84, pp. 150–158. 2025 VARSHNEY, Pramod K., 1997. Distributed detection and data fusion. Signal Processing Series. 2025 C SCOPUS PUBMED WOS Online kniha abstraktů cav_un_epca*0646239 DYNALIFE 2025 : Quantum Information and Decision Making in Life Sciences: Book of Abstracts Czech University of Life Sciences Prague 2025 Prague 16 19 Guy Tatiana Valentine 340 Pelikán Martin 340 Kárný Miroslav 340 Gaj Aleksej 340 Ružejnikov Jurij 340 Ruman Marko 340