2018
DOI: 10.1016/j.knosys.2017.11.031
|View full text |Cite
|
Sign up to set email alerts
|

Improving hierarchical task network planning performance by the use of domain-independent heuristic search

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1
1
1
1

Citation Types

0
3
0

Year Published

2020
2020
2024
2024

Publication Types

Select...
7
1

Relationship

0
8

Authors

Journals

citations
Cited by 12 publications
(4 citation statements)
references
References 8 publications
0
3
0
Order By: Relevance
“…Improving hierarchical task network planning performance by the use of domain-independent heuristic search [11] an extended HTN formalism named HTN-e is built so that heuristics from classical planning can be easily adapted to work in the HTN setting. A modified algorithm called OTDh which combines domain independent heuristics is with ordered task decomposition is proposed.…”
Section: Related Workmentioning
confidence: 99%
“…Improving hierarchical task network planning performance by the use of domain-independent heuristic search [11] an extended HTN formalism named HTN-e is built so that heuristics from classical planning can be easily adapted to work in the HTN setting. A modified algorithm called OTDh which combines domain independent heuristics is with ordered task decomposition is proposed.…”
Section: Related Workmentioning
confidence: 99%
“…Such additional information takes the form of heuristics, preferences, and advice. There is also a branch of approaches that order HTN methods using a heuristic function that defines the distance between the goal state of the given planning problem and the goal states of methods [81,82,83]. The latter approaches usually aim at improving the planning performance regardless of the resulting plan quality and agent attitudes, and are, therefore, out of scope here.…”
Section: Informed Decision-making In Htn Planningmentioning
confidence: 99%
“…where K(v i ) is the node v i criticality before quantization; K(v i ) is the node v i criticality before quantization; K(M) is the node matching criticality of the S and B networks; v i is the node criticality before quantization [38]; v i is the node criticality before node quantization;…”
Section: Mathematical Model Of Node Matching and Evolutionary Solutionsmentioning
confidence: 99%