東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Exploring complex dynamics in multi ...

Alexandridis, Konstantinos T.

Linked to FindBook

Google Book

Amazon

博客來

Exploring complex dynamics in multi agent-based intelligent systems: Theoretical and experimental approaches using the Multi Agent-based Behavioral Economic Landscape (MABEL) model.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Exploring complex dynamics in multi agent-based intelligent systems: Theoretical and experimental approaches using the Multi Agent-based Behavioral Economic Landscape (MABEL) model./
Author:	Alexandridis, Konstantinos T.
Description:	261 p.
Notes:	Source: Dissertation Abstracts International, Volume: 67-09, Section: B, page: 4949.
Contained By:	Dissertation Abstracts International67-09B.
Subject:	Agriculture, Forestry and Wildlife. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3232142
ISBN:	9780542864230

Exploring complex dynamics in multi agent-based intelligent systems: Theoretical and experimental approaches using the Multi Agent-based Behavioral Economic Landscape (MABEL) model.
Alexandridis, Konstantinos T.

Exploring complex dynamics in multi agent-based intelligent systems: Theoretical and experimental approaches using the Multi Agent-based Behavioral Economic Landscape (MABEL) model. - 261 p.

Source: Dissertation Abstracts International, Volume: 67-09, Section: B, page: 4949.

Thesis (Ph.D.)--Purdue University, 2006.

This dissertation adopts a holistic and detailed approach to modeling spatially explicit agent-based artificial intelligent systems, using the Multi Agent-based Behavioral Economic Landscape (MABEL) model. The research questions that addresses stem from the need to understand and analyze the real-world patterns and dynamics of land use change from a coupled human-environmental systems perspective. Describes the systemic, mathematical, statistical, socio-economic and spatial dynamics of the MABEL modeling framework, and provides a wide array of cross-disciplinary modeling applications within the research, decision-making and policy domains. Establishes the symbolic properties of the MABEL model as a Markov decision process, analyzes the decision-theoretic utility and optimization attributes of agents towards comprising statistically and spatially optimal policies and actions, and explores the probabilogic character of the agents' decision-making and inference mechanisms via the use of Bayesian belief and decision networks. Develops and describes a Monte Carlo methodology for experimental replications of agent's decisions regarding complex spatial parcel acquisition and learning. Recognizes the gap on spatially-explicit accuracy assessment techniques for complex spatial models, and proposes an ensemble of statistical tools designed to address this problem. Advanced information assessment techniques such as the Receiver-Operator Characteristic curve, the impurity entropy and Gini functions, and the Bayesian classification functions are proposed. The theoretical foundation for modular Bayesian inference in spatially-explicit multi-agent artificial intelligent systems, and the ensembles of cognitive and scenario assessment modular tools build for the MABEL model are provided. Emphasizes the modularity and robustness as valuable qualitative modeling attributes, and examines the role of robust intelligent modeling as a tool for improving policy-decisions related to land use change. Finally, the major contributions to the science are presented along with valuable directions for future research.

ISBN: 9780542864230Subjects--Topical Terms:

783690
Agriculture, Forestry and Wildlife.

Exploring complex dynamics in multi agent-based intelligent systems: Theoretical and experimental approaches using the Multi Agent-based Behavioral Economic Landscape (MABEL) model.
LDR:03161nmm 2200301 4500 001 1828964
005 20071023113102.5
008 130610s2006 eng d
020 $a 9780542864230
035 $a (UMI)AAI3232142
035 $a AAI3232142
040 $a UMI $c UMI
100 1 $a Alexandridis, Konstantinos T. $3 1917838
245 1 0 $a Exploring complex dynamics in multi agent-based intelligent systems: Theoretical and experimental approaches using the Multi Agent-based Behavioral Economic Landscape (MABEL) model.
300 $a 261 p.
500 $a Source: Dissertation Abstracts International, Volume: 67-09, Section: B, page: 4949.
500 $a Adviser: Bryan C. Pijanowski.
502 $a Thesis (Ph.D.)--Purdue University, 2006.
520 $a This dissertation adopts a holistic and detailed approach to modeling spatially explicit agent-based artificial intelligent systems, using the Multi Agent-based Behavioral Economic Landscape (MABEL) model. The research questions that addresses stem from the need to understand and analyze the real-world patterns and dynamics of land use change from a coupled human-environmental systems perspective. Describes the systemic, mathematical, statistical, socio-economic and spatial dynamics of the MABEL modeling framework, and provides a wide array of cross-disciplinary modeling applications within the research, decision-making and policy domains. Establishes the symbolic properties of the MABEL model as a Markov decision process, analyzes the decision-theoretic utility and optimization attributes of agents towards comprising statistically and spatially optimal policies and actions, and explores the probabilogic character of the agents' decision-making and inference mechanisms via the use of Bayesian belief and decision networks. Develops and describes a Monte Carlo methodology for experimental replications of agent's decisions regarding complex spatial parcel acquisition and learning. Recognizes the gap on spatially-explicit accuracy assessment techniques for complex spatial models, and proposes an ensemble of statistical tools designed to address this problem. Advanced information assessment techniques such as the Receiver-Operator Characteristic curve, the impurity entropy and Gini functions, and the Bayesian classification functions are proposed. The theoretical foundation for modular Bayesian inference in spatially-explicit multi-agent artificial intelligent systems, and the ensembles of cognitive and scenario assessment modular tools build for the MABEL model are provided. Emphasizes the modularity and robustness as valuable qualitative modeling attributes, and examines the role of robust intelligent modeling as a tool for improving policy-decisions related to land use change. Finally, the major contributions to the science are presented along with valuable directions for future research.
590 $a School code: 0183.
650 4 $a Agriculture, Forestry and Wildlife. $3 783690
650 4 $a Environmental Sciences. $3 676987
650 4 $a Remote Sensing. $3 1018559
650 4 $a Artificial Intelligence. $3 769149
690 $a 0478
690 $a 0768
690 $a 0799
690 $a 0800
710 2 0 $a Purdue University. $3 1017663
773 0 $t Dissertation Abstracts International $g 67-09B.
790 1 0 $a Pijanowski, Bryan C., $e advisor
790 $a 0183
791 $a Ph.D.
792 $a 2006
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3232142