Depth-bounded Reasoning. Classical Propositional Logic

D'Agostino, Marcello; Gabbay, Dov; Larese, Costanza

ISBN 10: 1848904428 / ISBN 13: 9781848904422

Verlag: College Publications, 2024

Beschreibung

Bestandsnummer des Verkäufers I-9781848904422

Bibliografische Details

• Titel: Depth-bounded Reasoning. Classical Propositional Logic
• Verlag: College Publications
• Erscheinungsjahr: 2024
• Sprache: Englisch
• ISBN-10: 1848904428
• ISBN-13: 9781848904422
• Einband: Softcover
• Zustand: New

Über diesen Titel

Inhaltsangabe

The "cost of reasoning", i.e., the cognitive or computational effort required by non-ideal, resource-bounded (human or artificial) agents in order to perform non-trivial inferences, is a crucial issue in philosophy, AI, economics and cognitive (neuro)science. Accounting for this fundamental variable in modelling real-world reasoning and decision-making is one of the most important and difficult challenges in the theory of rationality. With this volume, we are launching a series that, under the general title of "Logic and Bounded Rationality", aims to create a community of researchers from several areas that wish to cooperate towards a systematic logical view of bounded rationality.

However, a key stumbling block for any effort in this direction, is that a basic component of many reasoning and decision making tasks, namely deductive reasoning in propositional logic, is computationally hard. Hence, in this first volume of the series we offer a novel view of classical propositional logic. We present an "informational semantics" for the classical operators whose proof-theoretical presentation is a system of classical natural deduction that, unlike Gentzen's and Prawitz's systems, yields a simple way of measuring the "depth" of an inference. This approach leads to defining, in a natural way, a sequence of tractable depth-bounded deduction systems. As recent applications in formal argumentation and non-monotonic reasoning suggest, our approach provides a plausible model for representing rational agents with increasing, albeit limited, computational resources.