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id ddss2004_ra-129
authors Ma, L., Th. Arentze, A. Borgers, and H. Timmermans
year 2004
title Using Bayesian Decision Networks for Knowledge Representation under Conditions of Uncertainty in Multi-Agent Land Use Simulation Models
source Van Leeuwen, J.P. and H.J.P. Timmermans (eds.) Recent Advances in Design & Decision Support Systems in Architecture and Urban Planning, Dordrecht: Kluwer Academic Publishers, ISBN 1-4020-24088, p. 129-144
summary Land suitability analysis typically involves the assessment of the suitability of land units without knowing the future spatial distribution of land use. Traditional planning techniques have used “algebraic equations” to express land suitability as a weighted function of suitability scores across multiple criteria. However, the existing multi-criteria evaluation methods do not systematically account for uncertainty about the land use in adjacent and other cells. This paper proposes an alternative approach to land suitability analysis that does address the problem of uncertainty. In particular, Bayesian decision networks are suggested as a means of knowledge representation for agents in a multi-agent land use simulation system. Bayesian decision networks model the uncertainty in terms of probabilities specified in the network representing the expertise of specialists with respect to specific land uses. This paper discusses the approach and illustrates its use in the context of a retail agent.
keywords Land Suitability Analysis, Multi-Agents, Knowledge Representation, Bayesian Decision Networks
series DDSS
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