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State estimation in multi-agent deci...

Del Vecchio, Domitilla.

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State estimation in multi-agent decision and control systems.

Record Type:	Electronic resources : Monograph/item
Title/Author:	State estimation in multi-agent decision and control systems./
Author:	Del Vecchio, Domitilla.
Description:	120 p.
Notes:	Source: Dissertation Abstracts International, Volume: 66-08, Section: B, page: 4463.
Contained By:	Dissertation Abstracts International66-08B.
Subject:	Artificial Intelligence. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3185623
ISBN:	0542273691

State estimation in multi-agent decision and control systems.
Del Vecchio, Domitilla.

State estimation in multi-agent decision and control systems. - 120 p.

Source: Dissertation Abstracts International, Volume: 66-08, Section: B, page: 4463.

Thesis (Ph.D.)--California Institute of Technology, 2005.

This thesis addresses the problem of estimating the state in multi-agent decision and control systems. In particular, a novel approach to state estimation is developed that uses partial order theory in order to overcome some of the severe computational complexity issues arising in multi-agent systems. Within this approach, state estimation algorithms are developed that enjoy provable convergence properties and are scalable with the number of agents.

ISBN: 0542273691Subjects--Topical Terms:

769149
Artificial Intelligence.

State estimation in multi-agent decision and control systems.
LDR:02285nmm 2200301 4500 001 1819894
005 20061006144029.5
008 130610s2005 eng d
020 $a 0542273691
035 $a (UnM)AAI3185623
035 $a AAI3185623
040 $a UnM $c UnM
100 1 $a Del Vecchio, Domitilla. $3 1909156
245 1 0 $a State estimation in multi-agent decision and control systems.
300 $a 120 p.
500 $a Source: Dissertation Abstracts International, Volume: 66-08, Section: B, page: 4463.
500 $a Adviser: Richard M. Murray.
502 $a Thesis (Ph.D.)--California Institute of Technology, 2005.
520 $a This thesis addresses the problem of estimating the state in multi-agent decision and control systems. In particular, a novel approach to state estimation is developed that uses partial order theory in order to overcome some of the severe computational complexity issues arising in multi-agent systems. Within this approach, state estimation algorithms are developed that enjoy provable convergence properties and are scalable with the number of agents.
520 $a The dynamic evolution of the systems under study are characterized by the interplay of continuous and discrete variables. Continuous variables usually represent physical quantities such as position, velocity, voltage, and current, while the discrete variables usually represent quantities internal to the decision protocol that are used for coordination, communication, and control. Within the proposed state estimation approach, the estimation of continuous and discrete variables is developed in the same mathematical framework as a joint continuous-discrete space is considered for the estimator. This way, the dichotomy between the continuous and discrete world is overcome for the purpose of state estimation.
520 $a Application examples are considered, which include the state estimation in competitive multi-robot systems and in multi-agent discrete event systems, and the monitoring of distributed environments.
590 $a School code: 0037.
650 4 $a Artificial Intelligence. $3 769149
650 4 $a Engineering, System Science. $3 1018128
690 $a 0800
690 $a 0790
710 2 0 $a California Institute of Technology. $3 726902
773 0 $t Dissertation Abstracts International $g 66-08B.
790 1 0 $a Murray, Richard M., $e advisor
790 $a 0037
791 $a Ph.D.
792 $a 2005
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3185623