This episode explores a formal theory of situations, causality, and actions designed to help computer programs reason about these concepts. The theory defines a "situation" as a partial description of a state of affairs and introduces fluents—predicates or functions representing conditions like "raining" or "at(I, home)." Fluents can be interpreted using predicate calculus or modal logic.The theory uses the "can" operator to express the ability to achieve goals or perform actions in specific situations, with axioms related to causality and action capabilities. Two examples illustrate the theory in action: the Monkey and Bananas problem, showing how a monkey can obtain bananas by using a box, and a Simple Endgame, analyzing a winning strategy in a two-person game.The episode concludes by comparing the proposed logic with Prior's logic of time distinctions, discussing possible extensions and acknowledging differences in their approach to inevitability.https://apps.dtic.mil/sti/tr/pdf/AD0785031.pdf

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