Algorithms for distributed exploration

T. Walker; D. Kudenko; M.J.A. Strens; R. López de Mántaras; L. Saitta

Algorithms for distributed exploration

T. Walker, D. Kudenko, M.J.A. Strens, R. López de Mántaras (Editor), L. Saitta (Editor)

Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

In this paper we propose algorithms for a set of problems where a distributed team of agents tries to compile a global map of the environment from local observations. We focus on two approaches: one based on behavioural agent technology where agents are pulled (or repelled) by various forces, and another where agents follow a approximate planning approach that is based on dynamic programming. We study these approaches under different conditions, such as different types of environments, varying sensor and communication ranges, and the availability of prior knowledge of the map. The results show that in most cases the simpler behavioural agent teams perform at least as well, if not better, than the teams based on approximate planning and dynamic programming.

The research has not only practical implications for distributed exploration tasks, but also for analogous distributed search or optimisation problems.

Original language	English
Title of host publication	ECAI 2004: 16TH EUROPEAN CONFERENCE ON ARTIFICIAL INTELLIGENCE, PROCEEDINGS
Editors	R LopezdeMantaras, L Saitta
Place of Publication	AMSTERDAM
Publisher	IOS Press
Pages	84-88
Number of pages	5
ISBN (Print)	1-58603-452-9
Publication status	Published - 2004
Event	ECAI'2004 - Valencia, Spain Duration: 22 Aug 2004 → 27 Aug 2004

Conference

Conference	ECAI'2004
Country/Territory	Spain
City	Valencia
Period	22/08/04 → 27/08/04

Cite this

@inproceedings{ba8aa8bfcaf642ce805599f88dab089b,

title = "Algorithms for distributed exploration",

abstract = "In this paper we propose algorithms for a set of problems where a distributed team of agents tries to compile a global map of the environment from local observations. We focus on two approaches: one based on behavioural agent technology where agents are pulled (or repelled) by various forces, and another where agents follow a approximate planning approach that is based on dynamic programming. We study these approaches under different conditions, such as different types of environments, varying sensor and communication ranges, and the availability of prior knowledge of the map. The results show that in most cases the simpler behavioural agent teams perform at least as well, if not better, than the teams based on approximate planning and dynamic programming.The research has not only practical implications for distributed exploration tasks, but also for analogous distributed search or optimisation problems.",

author = "T. Walker and D. Kudenko and M.J.A. Strens and {L{\'o}pez de M{\'a}ntaras}, R. and L. Saitta",

year = "2004",

language = "English",

isbn = "1-58603-452-9",

pages = "84--88",

editor = "R LopezdeMantaras and L Saitta",

booktitle = "ECAI 2004: 16TH EUROPEAN CONFERENCE ON ARTIFICIAL INTELLIGENCE, PROCEEDINGS",

publisher = "IOS Press",

address = "Netherlands",

note = "ECAI'2004 ; Conference date: 22-08-2004 Through 27-08-2004",

}

TY - GEN

T1 - Algorithms for distributed exploration

AU - Walker, T.

AU - Kudenko, D.

AU - Strens, M.J.A.

A2 - López de Mántaras, R.

A2 - Saitta, L.

A2 - LopezdeMantaras, R

A2 - Saitta, L

PY - 2004

Y1 - 2004

N2 - In this paper we propose algorithms for a set of problems where a distributed team of agents tries to compile a global map of the environment from local observations. We focus on two approaches: one based on behavioural agent technology where agents are pulled (or repelled) by various forces, and another where agents follow a approximate planning approach that is based on dynamic programming. We study these approaches under different conditions, such as different types of environments, varying sensor and communication ranges, and the availability of prior knowledge of the map. The results show that in most cases the simpler behavioural agent teams perform at least as well, if not better, than the teams based on approximate planning and dynamic programming.The research has not only practical implications for distributed exploration tasks, but also for analogous distributed search or optimisation problems.

AB - In this paper we propose algorithms for a set of problems where a distributed team of agents tries to compile a global map of the environment from local observations. We focus on two approaches: one based on behavioural agent technology where agents are pulled (or repelled) by various forces, and another where agents follow a approximate planning approach that is based on dynamic programming. We study these approaches under different conditions, such as different types of environments, varying sensor and communication ranges, and the availability of prior knowledge of the map. The results show that in most cases the simpler behavioural agent teams perform at least as well, if not better, than the teams based on approximate planning and dynamic programming.The research has not only practical implications for distributed exploration tasks, but also for analogous distributed search or optimisation problems.

M3 - Conference contribution

SN - 1-58603-452-9

SP - 84

EP - 88

BT - ECAI 2004: 16TH EUROPEAN CONFERENCE ON ARTIFICIAL INTELLIGENCE, PROCEEDINGS

PB - IOS Press

CY - AMSTERDAM

T2 - ECAI'2004

Y2 - 22 August 2004 through 27 August 2004

ER -