Time, Chance, and Action
This work addresses the need for formal reasoning about actions in AI, though it appears incremental as it builds on existing logical frameworks without claiming broad SOTA improvements.
The paper tackles the problem of representing and reasoning about actions in stochastic environments by introducing a propositional temporal probability logic that integrates modal and probabilistic constructs to express chances of world states and indeterminacy in action effects.
To operate intelligently in the world, an agent must reason about its actions. The consequences of an action are a function of both the state of the world and the action itself. Many aspects of the world are inherently stochastic, so a representation for reasoning about actions must be able to express chances of world states as well as indeterminacy in the effects of actions and other events. This paper presents a propositional temporal probability logic for representing and reasoning about actions. The logic can represent the probability that facts hold and events occur at various times. It can represent the probability that actions and other events affect the future. It can represent concurrent actions and conditions that hold or change during execution of an action. The model of probability relates probabilities over time. The logical language integrates both modal and probabilistic constructs and can thus represent and distinguish between possibility, probability, and truth. Several examples illustrating the use of the logic are given.