TY - JOUR
T1 - Revealing Hidden Preconditions and Effects of Compound HTN Planning Tasks - A Complexity Analysis
AU - Olz, Conny
AU - Biundo, Susanne
AU - Bercher, Pascal
N1 - Publisher Copyright:
© 2021, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.
PY - 2021/5/18
Y1 - 2021/5/18
N2 - In Hierarchical Task Network (HTN) planning, compound tasks need to be refined into executable (primitive) action sequences. In contrast to their primitive counterparts, compound tasks do not show preconditions or effects. Thus, their implications on the states in which they are applied are not explicitly known: they are "hidden"in and depending on the decomposition structure. We formalize several kinds of preconditions and effects that can be inferred for compound tasks in totally ordered HTN domains. As relevant special case we introduce a problem relaxation which admits reasoning about preconditions and effects in polynomial time. We provide procedures for doing so, thereby extending previous work, which could only deal with acyclic models. We prove our procedures to be correct and complete for any totally ordered input domain. The results are embedded into an encompassing complexity analysis of the inference of preconditions and effects of compound tasks, an investigation that has not been made so far.
AB - In Hierarchical Task Network (HTN) planning, compound tasks need to be refined into executable (primitive) action sequences. In contrast to their primitive counterparts, compound tasks do not show preconditions or effects. Thus, their implications on the states in which they are applied are not explicitly known: they are "hidden"in and depending on the decomposition structure. We formalize several kinds of preconditions and effects that can be inferred for compound tasks in totally ordered HTN domains. As relevant special case we introduce a problem relaxation which admits reasoning about preconditions and effects in polynomial time. We provide procedures for doing so, thereby extending previous work, which could only deal with acyclic models. We prove our procedures to be correct and complete for any totally ordered input domain. The results are embedded into an encompassing complexity analysis of the inference of preconditions and effects of compound tasks, an investigation that has not been made so far.
UR - http://www.scopus.com/inward/record.url?scp=85123926419&partnerID=8YFLogxK
U2 - 10.1609/aaai.v35i13.17414
DO - 10.1609/aaai.v35i13.17414
M3 - Conference article
AN - SCOPUS:85123926419
SN - 2159-5399
VL - 35
SP - 11903
EP - 11912
JO - Proceedings of the AAAI Conference on Artificial Intelligence
JF - Proceedings of the AAAI Conference on Artificial Intelligence
IS - 13
T2 - 35th AAAI Conference on Artificial Intelligence, AAAI 2021
Y2 - 2 February 2021 through 9 February 2021
ER -