Making DL-Lite Planning Practical

Publikation: Beitrag in Buch/Konferenzbericht/Sammelband/GutachtenBeitrag in KonferenzbandBeigetragenBegutachtung

Beitragende

Abstract

Planning in the presence of background ontologies is a topic of long-standing interest in AI. It combines the problems of (1) belief update complexity and (2) state-space combinatorics. DL-Lite offers an attractive solution to (1), with belief updates possible at the ABox level. Indeed, it has been shown that DL-Lite planning can be compiled into the commonly used planning language PDDL. Yet that compilation was previously found to be infeasible for off-the-shelf planning systems. Here we analyze the reasons for this problem and find that the bottleneck lies in the planner pre-processes, in particular in the naïve DNF transformations used to compile the PDDL input into the planners' internal representations. Consequently, we design a PDDL pre-compiler realizing a polynomial DNF transformation. We leverage a particular PDDL language feature (“derived predicates”) to avoid the need for excessive control structure. Our pre-compiler turns out to be quite effective: the previous bottleneck disappears, and experiments on a broad range of benchmarks demonstrate the first practical technology for DL-Lite planning.

Details

OriginalspracheEnglisch
TitelProceedings of the 18th International Conference on Principles of Knowledge Representation and Reasoning (KR 2021)
Redakteure/-innenMeghyn Bienvenu, Gerhard Lakemeyer, Esra Erdem
Seiten641-645
Seitenumfang5
ISBN (elektronisch)9781956792997
PublikationsstatusVeröffentlicht - 2021
Peer-Review-StatusJa

Externe IDs

Scopus 85126192962
ORCID /0000-0001-9936-0943/work/142238111

Schlagworte

ASJC Scopus Sachgebiete