argumentbased

Argumentbased is an adjective used to describe approaches, systems, or analyses that rely on or are structured around arguments rather than other forms of justification. The term appears across philosophy, computer science, education, and cognitive science to emphasize the construction, evaluation, and exchange of arguments in reasoning processes.

In philosophy and logic, argumentbased methods involve identifying premises, inference steps, and conclusions, along with objections and counterarguments. The evaluation of competing arguments determines rational acceptability. In artificial intelligence and multi-agent systems, argumentbased reasoning refers to frameworks where agents generate, exchange, and counter arguments to justify decisions, resolve conflicts, or negotiate. Formal formalisms include abstract and structured argumentation, dialogue games, and assumption-based argumentation.

In education and science communication, argumentbased pedagogy invites learners to develop evidence-based arguments, assess counterclaims, and articulate justifications for conclusions, often using structured argument maps or Toulmin-style models.

In machine learning, argumentbased learning or argument-based explanation aims to provide reasons supporting predictions, sometimes by extracting arguments from trained models or by constructing surrogate argumentative explanations to enhance transparency.

Benefits commonly associated with argumentbased approaches include improved transparency, explainability, and critical-thinking skills, while challenges include designing scalable argument structures, handling conflicting or incomplete information, and measuring argument quality and relevance. Proponents argue that these methods support better justification and user trust, whereas critics point to potential complexity and resource demands.

See also: argumentation theory, formal argumentation, explainable AI, critical thinking, dialectics.