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Original title:
MDP-Based Analysis of Agent Interactions: From Collaborative to Adversarial Dynamics
Authors: Ružejnikov, Jurij ; Guy, Tatiana Valentine
Document type: Papers
Conference/Event: DYNALIFE 2025 : Conference on QUANTUM INFORMATION AND DECISION MAKING IN LIFE SCIENCES, Prague (CZ), 20250428
Year: 2025
Language: eng
Abstract: 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.
Keywords: agent interaction dynamics; information fusion; markov decision process (MDP); multiagent systems; policy inference; trust modelling
Project no.: CA21169
Funding provider: EU-COST
Host item entry: DYNALIFE 2025 : Quantum Information and Decision Making in Life Sciences: Book of Abstracts
Institution: Institute of Information Theory and Automation AS ČR (web)
Document availability information: Fulltext is available at external website.
External URL: https://library.utia.cas.cz/separaty/2025/AS/ruzejnikov-0638656.pdf
Original record: https://hdl.handle.net/11104/0370224
Permalink: http://www.nusl.cz/ntk/nusl-692643
The record appears in these collections:
Research > Institutes ASCR > Institute of Information Theory and Automation
Conference materials > Papers
Authors: Ružejnikov, Jurij ; Guy, Tatiana Valentine
Document type: Papers
Conference/Event: DYNALIFE 2025 : Conference on QUANTUM INFORMATION AND DECISION MAKING IN LIFE SCIENCES, Prague (CZ), 20250428
Year: 2025
Language: eng
Abstract: 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.
Keywords: agent interaction dynamics; information fusion; markov decision process (MDP); multiagent systems; policy inference; trust modelling
Project no.: CA21169
Funding provider: EU-COST
Host item entry: DYNALIFE 2025 : Quantum Information and Decision Making in Life Sciences: Book of Abstracts
Institution: Institute of Information Theory and Automation AS ČR (web)
Document availability information: Fulltext is available at external website.
External URL: https://library.utia.cas.cz/separaty/2025/AS/ruzejnikov-0638656.pdf
Original record: https://hdl.handle.net/11104/0370224
Permalink: http://www.nusl.cz/ntk/nusl-692643
The record appears in these collections:
Research > Institutes ASCR > Institute of Information Theory and Automation
Conference materials > Papers
Record created 2025-12-15, last modified 2026-03-01