Actions and Events in Interval Temporal Logic

Abstract

Representing and reasoning about the dynamic aspects of the world — primarily about actions and events — is a problem of interest to many different disciplines. In Artificial Intelligence (AI), we are interested in such problems for a number of reasons — in particular, to model the reasoning of intelligent agents as they plan to act in the world and to reason about causal effects in the world. More specifically, a general representation of actions and events has to support the following somewhat overlapping tasks:

• Prediction: Given a description of a scenario, including actions and events, what will (or is most likely to) happen?

• Planning: Given an initial description of the world and a desired goal, find a course of action that will (or is most likely to) achieve that goal.

• Explanation: Given a set of observations about the world, find the best explanation of the data. When the observations are another agent’s actions and the explanation desired is the agent’s plan, the problem is called plan recognition.