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This paper introduces Context-Dependent Argumentation Frameworks (CDAFs) as a formal extension of Dung's theory to model how agents strategically activate specific perspectives or regimes when evaluating arguments. By deriving defeat functions from relevance sets, the research addresses critical gaps in handling context-dependent manipulation and complexity bounds within multi-agent systems. The work advances foundational AI logic by providing rigorous tools for analyzing strategic behavior where standard conflict-free semantics are insufficient.
Choosing the Lens: Strategic Perspective Activation in Context-Dependent Argumentation
arXiv:2605.31581v2 Announce Type: replace Abstract: The same arguments often need to be evaluated under different external regimes. An agent with influence over the regime has a strategic lever that standard formalisms do not directly capture. We introduce context-dependent argumentation frameworks (CDAFs), an extension of Dung's theory in which a defeat function determines, per context, which attacks succeed. Blocked attacks are inverted rather than deleted, so extensions stay conflict-free with respect to the attack relation. A perspective-labeled specialisation derives the defeat function from a relevance set $\rho$ and a priority $\pi$. The relevance set is the agent's action space. In a small worked example, the agent's target argument is rejected under every full-relevance priority, yet accepted under a partial activation whose outcome no VAF audience can mirror. We define the corresponding decision problem, ACTIVATION-MANIPULATION, and record baseline complexity bounds. For grounded semantics with mandatory perspectives the problem is NP-complete, and the hardness comes from the activation choice itself.
Abstract:The same arguments often need to be evaluated under different external regimes. An agent with influence over the regime has a strategic lever that standard formalisms do not directly capture. We introduce context-dependent argumentation frameworks (CDAFs), an extension of Dung's theory in which a defeat function determines, per context, which attacks succeed. Blocked attacks are inverted rather than deleted, so extensions stay conflict-free with respect to the attack relation. A perspective-labeled specialisation derives the defeat function from a relevance set $\rho$ and a priority $\pi$. The relevance set is the agent's action space. In a small worked example, the agent's target argument is rejected under every full-relevance priority, yet accepted under a partial activation whose outcome no VAF audience can mirror. We define the corresponding decision problem, ACTIVATION-MANIPULATION, and record baseline complexity bounds. For grounded semantics with mandatory perspectives the problem is NP-complete, and the hardness comes from the activation choice itself.
Artificial Intelligence (cs.AI)
arXiv:2605.31581 [cs.AI]
(or arXiv:2605.31581v2 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2605.31581 Focus to learn more arXiv-issued DOI via DataCite
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