Haifa, Israel. July 31–August 5, 2022.
Copyright © 2022 International Joint Conferences on Artificial Intelligence Organization
Automated planning is the task of finding a sequence of actions that reach a desired goal, given a description of their applicability and their effects on the world. In temporal planning, actions have a duration and can overlap in time. In modern temporal planning formalisms, two features have been introduced which are very useful from a modeling perspective, but are not yet thoroughly understood: intermediate conditions and effects (ICE) and conditional effects. The expressive power of such constructs is yet not well comprehended, especially when time is dense, and no minimum separation is required between mutex events. This paper reveals that both ICE and conditional effects do not add expressive power with regards to common temporal planning formalisms. In particular, we show how they can be compiled away using a polynomial-size encoding that makes no assumptions on the time domain. This encoding advances our understanding of these features, and allow their use with simple temporal planners that lack their support. Moreover, it provides a constructive proof that temporal planning with ICE and conditional effects remains PSPACE-complete.