bibtype	C - Conference Paper (international conference)
ARLID	0638656
utime	20251009095819.6
mtime	20250905235959.9
title (primary) (eng)	MDP-Based Analysis of Agent Interactions: From Collaborative to Adversarial Dynamics
specification	page_count 4 s. media_type E
serial	ARLID cav_un_epca*0639790 title 2025 DYNALIFE Conference on QUANTUM INFORMATION AND DECISION MAKING IN LIFE SCIENCES PROGRAMME and ABSTRACTS publisher place Prague name Czech University of Life Sciences Prague year 2025
keyword	markov decision process (MDP)
keyword	multiagent systems
keyword	trust modelling
keyword	policy inference
keyword	agent interaction dynamics
keyword	information fusion
author (primary)	ARLID cav_un_auth*0491463 name1 Ružejnikov name2 Jurij institution UTIA-B full_dept (cz) Adaptivní systémy full_dept (eng) Department of Adaptive Systems department (cz) AS department (eng) AS country CZ share 80 garant K fullinstit Ústav teorie informace a automatizace AV ČR, v. v. i.
author	ARLID cav_un_auth*0101092 name1 Guy name2 Tatiana Valentine institution UTIA-B full_dept (cz) Adaptivní systémy full_dept Department of Adaptive Systems department (cz) AS department AS full_dept Department of Adaptive Systems fullinstit Ústav teorie informace a automatizace AV ČR, v. v. i.
source	url https://library.utia.cas.cz/separaty/2025/AS/ruzejnikov-0638656.pdf
cas_special	project project_id CA21169 agency EU-COST country XE ARLID cav_un_auth*0452289 abstract (eng) 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. action ARLID cav_un_auth*0491465 name DYNALIFE 2025 : Conference on QUANTUM INFORMATION AND DECISION MAKING IN LIFE SCIENCES dates 20250428 mrcbC20-s 20250429 place Prague country CZ RIV BB FORD0 10000 FORD1 10100 FORD2 10101 reportyear 2026 num_of_auth 2 presentation_type PO inst_support RVO:67985556 permalink https://hdl.handle.net/11104/0370224 mrcbC61 1 cooperation ARLID cav_un_auth*0322033 name Česká zemědělská univerzita v Praze, Provozně ekonomická fakulta institution PEF ČZU country CZ confidential S mrcbC71 BERGERSON, Sage, 2021. Multi-agent inverse reinforcement learning: Suboptimal demonstrations and alternative solution concepts. 2025 mrcbC71 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 mrcbC71 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 mrcbC71 VARSHNEY, Pramod K., 1997. Distributed detection and data fusion. Signal Processing Series. arlyear 2025 mrcbU02 C mrcbU14 SCOPUS mrcbU24 PUBMED mrcbU34 WOS mrcbU56 Online kniha abstraktů mrcbU63 cav_un_epca*0639790 2025 DYNALIFE Conference on QUANTUM INFORMATION AND DECISION MAKING IN LIFE SCIENCES PROGRAMME and ABSTRACTS Czech University of Life Sciences Prague 2025 Prague