Computing Opportunities to Augment Plans for Novel Replanning during Execution

Abstract

Traditionally, planning provides for execution plans as sequences of actions with preconditions and effects. Execution monitoring identifies failure conditions when the preconditions of an action do not match the state. Interestingly, planning proceeds by consuming a given initial state and abandoning reasoning about any facts not true in that state. In this paper, we define opportunities as such missing facts, and contribute an algorithm to compute them and augment a plan for execution with them. We then introduce a new execution opportunity monitoring that focusedly checks for these opportunities at each execution state. Opportunistic replanning proceeds now from the new state including the detected opportunities.