Neural Networks for Model-free and Scale-free Automated Planning

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F21%3A00354896" target="_blank" >RIV/68407700:21230/21:00354896 - isvavai.cz</a>

Result on the web

<a href="https://doi.org/10.1007/s10115-021-01619-8" target="_blank" >https://doi.org/10.1007/s10115-021-01619-8</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s10115-021-01619-8" target="_blank" >10.1007/s10115-021-01619-8</a>

Result language

angličtina

Original language name

Neural Networks for Model-free and Scale-free Automated Planning

Original language description

Automated planning for problems without an explicit model is an elusive research challenge. However, if tackled, it could provide a general approach to problems in real-world unstructured environments. There are currently two strong research directions in the area of artificial intelligence (AI), namely machine learning and symbolic AI. The former provides techniques to learn models of unstructured data but does not provide further problem solving capabilities on such models. The latter provides efficient algorithms for general problem solving, but requires a model to work with. Creating the model can itself be a bottleneck of many problem domains. Complicated problems require an explicit description that can be very costly or even impossible to create. In this paper, we propose a combination of the two areas, namely deep learning and classical planning, to form a planning system that works without a human-encoded model for variably scaled problems. The deep learning part extracts the model in the form of a transition system and a goal-distance heuristic estimator; the classical planning part uses such a model to efficiently solve the planning problem. Both networks in the planning system, we introduced, work with a problem in its graphic form and there is no need for any additional information to create the state transition system or to estimate a heuristic value. We proposed three different architectures for the heuristic estimator to compare different characteristics of well-known deep learning techniques. Besides the design of such planning systems, we provide experimental evaluation comparing the implemented techniques to classical model-based methods.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

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

Project

<a href="/en/project/GJ18-24965Y" target="_blank" >GJ18-24965Y: Privacy Preserving Multi-agent Planning</a><br>

Continuities

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

Publication year

2021

Confidentiality

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

Name of the periodical

Knowledge and Information Systems

ISSN

0219-1377

e-ISSN

0219-3116

Volume of the periodical

63

Issue of the periodical within the volume

12

Country of publishing house

DE - GERMANY

Number of pages

36

Pages from-to

3103-3138

UT code for WoS article

000713906900001

EID of the result in the Scopus database

2-s2.0-85118454002