A General Approach to Distributed and Privacy-Preserving Heuristic Computation

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F19%3A00336495" target="_blank" >RIV/68407700:21230/19:00336495 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.1007/978-3-030-37494-5_4" target="_blank" >https://doi.org/10.1007/978-3-030-37494-5_4</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-030-37494-5_4" target="_blank" >10.1007/978-3-030-37494-5_4</a>

Jazyk výsledku

angličtina

Název v původním jazyce

A General Approach to Distributed and Privacy-Preserving Heuristic Computation

Popis výsledku v původním jazyce

Multi-agent planning (MAP) has recently gained traction in both planning and multi-agent system communities, especially with the focus on privacy-preserving multi-agent planning, where multiple agents plan for a common goal but with private information they do not want to disclose. Heuristic search is the dominant technique used in MAP and therefore it is not surprising that a significant attention has been paid to distributed heuristic computation, either with or without the concern for privacy. Nevertheless, most of the distributed heuristic computation approaches published so far are ad-hoc algorithms tailored for the particular heuristic. In this work we present a general, privacy-preserving, and admissible approach to distributed heuristic computation. Our approach is based on an adaptation of the technique of cost partitioning which has been successfully applied in optimal classical planning. We present the general approach, a particular implementation, and an experimental evaluation showing that the presented approach is competitive with the state of the art while having the additional benefits of generality and privacy preservation.

Název v anglickém jazyce

A General Approach to Distributed and Privacy-Preserving Heuristic Computation

Popis výsledku anglicky

Multi-agent planning (MAP) has recently gained traction in both planning and multi-agent system communities, especially with the focus on privacy-preserving multi-agent planning, where multiple agents plan for a common goal but with private information they do not want to disclose. Heuristic search is the dominant technique used in MAP and therefore it is not surprising that a significant attention has been paid to distributed heuristic computation, either with or without the concern for privacy. Nevertheless, most of the distributed heuristic computation approaches published so far are ad-hoc algorithms tailored for the particular heuristic. In this work we present a general, privacy-preserving, and admissible approach to distributed heuristic computation. Our approach is based on an adaptation of the technique of cost partitioning which has been successfully applied in optimal classical planning. We present the general approach, a particular implementation, and an experimental evaluation showing that the presented approach is competitive with the state of the art while having the additional benefits of generality and privacy preservation.

Druh

C - Kapitola v odborné knize

CEP obor

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OECD FORD obor

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

Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

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

Rok uplatnění

2019

Kód důvěrnosti údajů

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

Název knihy nebo sborníku

Agent and Artificial Intelligence - 11th International Conference, ICAART 2019

ISBN

978-3-030-37493-8

Počet stran výsledku

17

Strana od-do

55-71

Počet stran knihy

363

Název nakladatele

Springer

Místo vydání

Cham

Kód UT WoS kapitoly

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