Heuristic Search Optimisation Using Planning and Curriculum Learning Techniques

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F23%3A00373634" target="_blank" >RIV/68407700:21230/23:00373634 - isvavai.cz</a>

Result on the web

<a href="https://doi.org/10.1007/978-3-031-49008-8_39" target="_blank" >https://doi.org/10.1007/978-3-031-49008-8_39</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-031-49008-8_39" target="_blank" >10.1007/978-3-031-49008-8_39</a>

Result language

angličtina

Original language name

Heuristic Search Optimisation Using Planning and Curriculum Learning Techniques

Original language description

Learning a well-informed heuristic function for hard planning domains is an elusive problem. Although there are known neural network architectures to represent such heuristic knowledge, it is not obvious what concrete information is learned and whether techniques aimed at understanding the structure help in improving the quality of the heuristics. This paper presents a network model that learns a heuristic function capable of relating distant parts of the state space via optimal plan imitation using the attention mechanism which drastically improves the learning of a good heuristic function. The learning of this heuristic function is further improved by the use of curriculum learning, where newly solved problem instances are added to the training set, which, in turn, helps to solve problems of higher complexities and train from harder problem instances. The methodologies used in this paper far exceed the performances of all existing baselines including known deep learning approaches and classical planning heuristics. We demonstrate its effectiveness and success on grid-type PDDL domains, namely Sokoban, maze-with-teleports and sliding tile puzzles.

Czech name

—

Czech description

—

Type

D - Article in proceedings

CEP classification

—

OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

Result was created during the realization of more than one project. More information in the Projects tab.

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2023

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

Progress in Artificial Intelligence, 22nd EPIA Conference on Artificial Intelligence, EPIA 2023, Faial Island, Azores, September 5–8, 2023, Proceedings, Part I

ISBN

978-3-031-49007-1

ISSN

0302-9743

e-ISSN

1611-3349

Number of pages

13

Pages from-to

495-507

Publisher name

Springer, Cham

Place of publication

—

Event location

Faial Island

Event date

Sep 5, 2023

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

001160573500039