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. (shrink)

At 8 a.m. I get in my car and set off for work. At 7:59 a.m., before I started it, my car was at rest; at 8:01 a.m. it is in motion. When a thing is not in motion, it is at rest, and when it is not at rest, it is in motion. But what was the state of the car at 8:00 a.m., as I was starting it? It would be inaccurate to say that it was in motion (...) but it would be inaccurate, also, to say that it was at rest: it was “just starting.” But, if whenever a thing is not in motion it is at rest, the state of “just starting”—or, of course, the comparable state of “just stopping”—must be either a state of motion or a state of rest. Which? (shrink)