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Argumentation In Artificial Intelligence

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The study of argumentation, in its classical presentation within philosophy, can be thought of as being concerned with how statements are offered, discussed, and resolved in the setting of subjects on which numerous divergent viewpoints may exist. Thus, from Aristotle to the present day, philosophical investigations of argumentation have addressed such topics as: the mechanisms by which “legitimate” argumentation in support of a claim can be distinguished from “flawed” argumentation; analyses of the typical structures that constitute argument components & argumentation development; and the processes by which participants in debate can elicit “legitimate” argumentation.

Within the simplified overview of argumentation outlined in the preceding paragraph, one can, already, identify a number of themes whose elements embody issues of a computational nature in the following:

  • Defining the component parts of an argument and their interaction

  • Identifying rules and protocols describing argumentation processes

  • Distinguishing legitimate from invalid arguments

  • Determining conditions under which further discussion is redundant.

Much of the formal computational approach of argumentation has its roots in ideas gained from AI-inspired contributions to logic and deductive reasoning, which is no coincidence. So, core concepts in mathematical proof theory include: precisely defined means for expressing assertions (e.g. formulae in a given logical language); accepted bases on which to build theorems (e.g. collections of axioms); procedures prescribing the means by which additional theorems can be derived from existing theorems and axioms (e.g. templates for inference rules); and precise concept definitions.

While the structural elements presented in this view of mathematical reasoning have proven to be a useful foundation in the development of argumentation-based models in AI, the formal apparatus and methods of mathematical reasoning are distinct from those of importance when considering the concept of argumentation as it is known from everyday contexts, such as in political deliberation. While there are some parallels that can be drawn, debaters have a set of agreed-upon premises, as well as a knowledge of when contributions to a conversation are “irrational” or incorrect.

The impact of non-classical logics on AI arguments

Birnbaum, Flowers, and McGuire’s work, in which a structural model of argument encompassing notions of support and attack within a graph theoretic base comprising propositional forms is applied to textual reasoning; and Alvarado and Dyer’s approaches, to the analysis of editorial presentation, are early studies using argumentation inspired methods in AI contexts.

The challenges of reasoning and explanation in the presence of inadequate and unclear knowledge were undoubtedly the early motives that brought argumentation theory into usage in AI. The shortcomings of classical propositional logic as a way of addressing issues had been outlined in Reiter’s significant work, and a pressing focus of work during most of the 1980s and early 1990s was to build on the growth of non-monotonic logic treatments within AI.

As a result, argumentation was initially accepted as a viable supporting strategy for achieving a formal account of non-monotonic reasoning, rather than as a paradigm worth studying on its own. The involvement of philosophers and legal theorists in AI reasoning and argumentation was a watershed moment in the transition to computationally based argument models.

Source & Reference : ScienceDirect

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