A temporal logic for reasoning about processes and plans

TLDR

Prior AI research has largely ignored the full complexity of time, especially continuous change and future indeterminacy. The paper introduces a first‑order temporal logic that enables naming and proving facts, events, plans, and world histories. The logic is first‑order and could support a temporal‑inference machine that tracks multiple timeline maps, one for each possible history. It yields analyses of causality, continuous quantitative change, the frame problem, and the link between tasks and actions.

Abstract

Much previous work in artificial intelligence has neglected representing time in all its complexity. In particular, it has neglected continuous change and the indeterminacy of the future. To rectify this, I have developed a first-order temporal logic, in which it is possible to name and prove things about facts, events, plans, and world histories. In particular, the logic provides analyses of causality, continuous change in quantities, the persistence of facts (the frame problem), and the relationship between tasks and actions. It may be possible to implement a temporal-inference machine based on this logic, which keeps track of several "maps" of a time line, one per possible history.