MDP-Based Analysis of Agent Interactions: From Collaborative to Adversarial Dynamics

Kód výsledku v IS VaVaI

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Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

MDP-Based Analysis of Agent Interactions: From Collaborative to Adversarial Dynamics

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

MDP-Based Analysis of Agent Interactions: From Collaborative to Adversarial Dynamics

Popis výsledku anglicky

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.

Druh

O - Ostatní výsledky

CEP obor

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OECD FORD obor

10101 - Pure mathematics

Projekt

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Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů