16238835
Spatial agent‐based modelling
The convergence of object-oriented approaches to data modelling within geographic information science together with increasingly fine-scale data collection approaches and discrete-event simulations has created new opportunities to build dynamic representations of geographical systems (Gimblett 2002, Brown et al. 2005). In particular, agent-based modelling has become an important representational paradigm within both the biological (DeAngelis and Gross 1992) and social (Epstein and Axtell 1996) sciences. A software agent can be constructed as a selfdirected object, i.e. it has the ability to satisfy internal goals or objectives through actions and decisions based on a set of internal rules or strategies (Iglesias et al. 1999) and can be used in one of two primary modes: instrumental and representational. In the instrumental mode, agents are created for the primary purpose of performing a computing task to reduce reliance on the attention and cognition of a human operator, i.e. the agents stand in for the computer user. A good example is the system described by Sengupta and Bennett (2003) that uses agents to assist new users of geographic information systems in their application for decision support. In representational mode, the agent is designed for the primary purpose of behaving in a manner that is consistent with that of a third party, i.e. another person, group, institution, animal, or other animate object. Although this distinction is somewhat arbitrary and, in practice, not always clear, it highlights the range of purposes to which agent-based modeling is put within the articles presented here. This special issue draws together six papers that were presented at the GeoComputation 2005 conference, held on 1–3 August 2005 in Ann Arbor, Michigan and jointly hosted by the School of Natural Resources and Environment at the University of Michigan and the Institute for Geospatial Research and Education at Eastern Michigan University. As we surveyed the themes of papers presented at the conference, agent-based modelling was clearly a dominant one. Though the papers here tended to focus on issues associated with using agent-based modelling in representational mode, a couple of papers hinted at instrumental uses. For example, Parunak et al. discuss how agents can be used to solve the problems of navigation in heterogeneous landscapes, largely an instrumental application in which the agents perform computations collectively to solve complex spatial problems. Graniero and Robinson present a framework for building coupled agentlandscape models and illustrate the framework with representational agent-based
15121856
Agent-based and analytical modeling to evaluate the effectiveness of greenbelts
We present several models of residential development at the rural–urban fringe to evaluate the effectiveness of a greenbelt located beside a developed area, for delaying development outside the greenbelt. First, we develop a mathematical model, under two assumptions about the distributions of service centers, that represents the trade-off between greenbelt placement and width, their effects on the rate of development beyond the greenbelt, and how these interact with spatial patterns of aesthetic quality and the locations of services. Next, we present three agent-based models (ABMs) that include agents with the potential for heterogeneous preferences and a landscape with the potential for heterogeneous attributes. Results from experiments run with a one-dimensional ABM agree with the starkest of the results from the mathematical model, strengthening the support for both models. Further, we present two different two-dimensional ABMs and conduct a series of experiments to supplement our mathematical analysis. These include examining the effects of heterogeneous agent preferences, multiple landscape patterns, incomplete or imperfect information available to agents, and a positive aesthetic quality impact of the greenbelt on neighboring locations. These results suggest how width and location of the greenbelt could help determine the effectiveness of greenbelts for slowing sprawl, but that these relationships are sensitive to the patterns of landscape aesthetic quality and assumptions about service center locations. # 2004 Elsevier Ltd. All rights reserved.
3111420
Path dependence and the validation of agent‐based spatial models of land use
In this paper, we identify two distinct notions of accuracy of land‐use models and highlight a tension between them. A model can have predictive accuracy: its predicted land‐use pattern can be highly correlated with the actual land‐use pattern. A model can also have process accuracy: the process by which locations or land‐use patterns are determined can be consistent with real world processes. To balance these two potentially conflicting motivations, we introduce the concept of the invariant region, i.e., the area where land‐use type is almost certain, and thus path independent; and the variant region, i.e., the area where land use depends on a particular series of events, and is thus path dependent. We demonstrate our methods using an agent‐based land‐use model and using multi‐temporal land‐use data collected for Washtenaw County, Michigan, USA. The results indicate that, using the methods we describe, researchers can improve their ability to communicate how well their model performs, the situations or instances in which it does not perform well, and the cases in which it is relatively unlikely to predict well because of either path dependence or stochastic uncertainty.
5345095
A Parallel Genetic Heuristic for the Quadratic Assignment Problem
14214005
Incorporating a Metropolis method in a distribution estimation using Markov random field algorithm
Markov random field (MRF) modelling techniques have been recently proposed as a novel approach to probabilistic modelling for estimation of distribution algorithms (EDAs) (S. K. Shakya et al., 2004). An EDA using this technique was called distribution estimation using Markov random fields (DEUM). DEUM was later extended to DEUM/sub d/ (S. Shakya et al., 2005). DEUM and DEUM/sub d/ use a univariate model of probability distribution, and have been shown to perform better than other univariate EDAs for a range of optimization problems. This paper extends DEUM/sub d/ to incorporate a simple Metropolis method and empirically shows that for linear univariate problems the proposed univariate MRF models are very effective. In particular, the proposed DEUM/sub d/ algorithm can find the solution in O(n) fitness evaluations. Furthermore, we suggest that the Metropolis method can also be used to extend the DEUM approach to multivariate problems.
195710721
Special Issue: Machine Learning in Design
This issue of AIEDAM is based on a workshop on Machine Learning in Design held at the 1996 Conference on Artificial Intelligence in Design, AID'96 (Gero & Sudweeks, 1996), the third of such workshops, with the previous two being held at AID'92 (Gero, 1992) and at AID'94 (Gero & Sudweeks, 1994). The first two workshops also resulted in special issues of AIEDAM (Maher et al., 1994; Duffy et al., 1996).
2572723
Using a Markov network model in a univariate EDA: an empirical cost-benefit analysis
This paper presents an empirical cost-benefit analysis of an algorithm called Distribution Estimation Using MRF with direct sampling (DEUMd). DEUMd belongs to the family of Estimation of Distribution Algorithm (EDA). Particularly it is a univariate EDA. DEUMd uses a computationally more expensive model to estimate the probability distribution than other univariate EDAs. We investigate the performance of DEUMd in a range of optimization problem. Our experiments shows a better performance (in terms of the number of fitness evaluation needed by the algorithm to find a solution and the quality of the solution) of DEUMd on most of the problems analysed in this paper in comparison to that of other univariate EDAs. We conclude that use of a Markov Network in a univariate EDA can be of net benefit in defined set of circumstances.
28079143
Provably Secure Implicit Certificate Schemes
Optimal mail certificates, introduced in [12], are efficient types of implicit certificates which offer many advantages over traditional (explicit) certificates. For example, an optimal mail certificate is small enough to fit on a two-dimensional digital postal mark together with a digital signature. This paper defines a general notion of security for implicit certificates, and proves that optimal mail certificates are secure under this definition.