Metaphysics of Planning Domain Descriptions

Authors

  • Siddharth Srivastava United Technologies Research Center, Berkeley
  • Stuart Russell University of California Berkeley
  • Alessandro Pinto United Technologies Research Center, Berkeley

DOI:

https://doi.org/10.1609/aaai.v30i1.10118

Keywords:

Automated Planning, Abstraction, Planning Domain Descriptions

Abstract

STRIPS-like languages (SLLs) have fostered immense advances in automated planning. In practice, SLLs are used to express highly abstract versions of real-world planning problems, leading to more concise models and faster solution times. Unfortunately, as we show in the paper, simple ways of abstracting solvable real-world problems may lead to SLL models that are unsolvable, SLL models whose solutions are incorrect with respect to the real-world problem, or models that are inexpressible in SLLs. There is some evidence that such limitations have restricted the applicability of AI planning technology in the real world, as is apparent in the case of task and motion planning in robotics. We show that the situation can be ameliorated by a combination of increased expressive power — for example, allowing angelic nondeterminism in action effects — and new kinds of algorithmic approaches designed to produce correct solutions from initially incorrect or non-Markovian abstract models.

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Published

2016-02-21

How to Cite

Srivastava, S., Russell, S., & Pinto, A. (2016). Metaphysics of Planning Domain Descriptions. Proceedings of the AAAI Conference on Artificial Intelligence, 30(1). https://doi.org/10.1609/aaai.v30i1.10118

Issue

Section

Technical Papers: Knowledge Representation and Reasoning