Reversed planning graphs for relevance heuristics in AI planning
(2005) 2nd Starting Artificial Intelligence Researchers Symposium 117. p.29-38- Abstract
- Most AI planning heuristics are reachability heuristics, in the sense that they estimate the minimum plan length from the initial state to a search state. Such heuristics are best suited for use in regression state-space planners, since a progression planner would have to reconstruct the heuristic function at each new search state. However, some domains (or problem instances within a certain domain) are better suited for progression search, motivating the need for relevance heuristics that estimate the distance from a search state to the goal state. In this paper we show how to construct reversed planning graphs that can be used for computing new relevance heuristics, based on the work on extracting reachability heuristics from planning... (More)
- Most AI planning heuristics are reachability heuristics, in the sense that they estimate the minimum plan length from the initial state to a search state. Such heuristics are best suited for use in regression state-space planners, since a progression planner would have to reconstruct the heuristic function at each new search state. However, some domains (or problem instances within a certain domain) are better suited for progression search, motivating the need for relevance heuristics that estimate the distance from a search state to the goal state. In this paper we show how to construct reversed planning graphs that can be used for computing new relevance heuristics, based on the work on extracting reachability heuristics from planning graphs, and a general framework for reversing planning domains. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/1406298
- author
- Pettersson, Mats Petter LU
- organization
- publishing date
- 2005
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- host publication
- Planning, Scheduling and Constraint Satisfaction: From Theory to Practice
- volume
- 117
- pages
- 29 - 38
- publisher
- IOS Press
- conference name
- 2nd Starting Artificial Intelligence Researchers Symposium
- conference location
- Valencia, Spain
- conference dates
- 2004-08-23 - 2004-08-24
- external identifiers
-
- wos:000228899700004
- ISSN
- 0922-6389
- ISBN
- 978-1-58603-484-9
- language
- English
- LU publication?
- yes
- id
- 06b0f56c-fc94-4d97-8266-a1093005a072 (old id 1406298)
- date added to LUP
- 2016-04-01 16:14:51
- date last changed
- 2021-05-05 10:22:14
@inproceedings{06b0f56c-fc94-4d97-8266-a1093005a072,
abstract = {{Most AI planning heuristics are reachability heuristics, in the sense that they estimate the minimum plan length from the initial state to a search state. Such heuristics are best suited for use in regression state-space planners, since a progression planner would have to reconstruct the heuristic function at each new search state. However, some domains (or problem instances within a certain domain) are better suited for progression search, motivating the need for relevance heuristics that estimate the distance from a search state to the goal state. In this paper we show how to construct reversed planning graphs that can be used for computing new relevance heuristics, based on the work on extracting reachability heuristics from planning graphs, and a general framework for reversing planning domains.}},
author = {{Pettersson, Mats Petter}},
booktitle = {{Planning, Scheduling and Constraint Satisfaction: From Theory to Practice}},
isbn = {{978-1-58603-484-9}},
issn = {{0922-6389}},
language = {{eng}},
pages = {{29--38}},
publisher = {{IOS Press}},
title = {{Reversed planning graphs for relevance heuristics in AI planning}},
volume = {{117}},
year = {{2005}},
}