arXiv:2605.31581v1 Announce Type: new 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. 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, t
The increasing complexity and context-dependency of AI reasoning demand more sophisticated foundational models that can dynamically adapt their argumentative frameworks.
This research provides a theoretical framework for AI agents to strategically activate perspectives, enabling more nuanced and impactful decision-making in complex environments.
AI argumentation models can move beyond static evaluation to dynamic, context-aware reasoning, allowing agents to manipulate the 'lens' through which arguments are processed.
- · AI agents developers
- · Strategic planning software
- · Decision support systems
- · Autonomous systems
- · Static AI reasoning models
- · Deterministic expert systems
AI agents will exhibit improved adaptability and strategic capabilities in context-dependent scenarios.
The ability to influence context will become a new frontier for AI agent interaction and competition.
This could lead to more sophisticated 'AI diplomacy' or negotiation, where agents strategically frame information to achieve desired outcomes.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.AI