Heuristic Learning in Domain-Independent Planning: Theoretical Analysis and Experimental Evaluation
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F21%3A10439480" target="_blank" >RIV/00216208:11320/21:10439480 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1007/978-3-030-71158-0_12" target="_blank" >https://doi.org/10.1007/978-3-030-71158-0_12</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-030-71158-0_12" target="_blank" >10.1007/978-3-030-71158-0_12</a>
Alternative languages
Result language
angličtina
Original language name
Heuristic Learning in Domain-Independent Planning: Theoretical Analysis and Experimental Evaluation
Original language description
Automated planning deals with the problem of finding a sequence of actions leading from a given state to a desired state. The state-of-the-art automated planning techniques exploit informed forward search guided by a heuristic which is used to estimate a distance from a state to a goal state. In this paper, we present a technique to automatically construct an efficient heuristic for a given domain. The proposed approach is based on training a deep neural network using a set of solved planning problems as training data. We use a novel way of extracting features for states developed specifically for planning applications. Our experiments show that the technique is competitive with state-of-the-art domain-independent heuristic. We also introduce a theoretical framework to formally analyze behaviour of learned heuristics. We state and prove several theorems that establish bounds on the worst-case performance of learned heuristics.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
<a href="/en/project/GA18-07252S" target="_blank" >GA18-07252S: MoRePlan: Modeling and Reformulating Planning Problems</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2021
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
AGENTS AND ARTIFICIAL INTELLIGENCE, ICAART 2020
ISBN
978-3-030-71158-0
ISSN
0302-9743
e-ISSN
1611-3349
Number of pages
26
Pages from-to
254-279
Publisher name
SPRINGER INTERNATIONAL PUBLISHING AG
Place of publication
CHAM
Event location
Valletta
Event date
Feb 22, 2020
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000722435000012