Case-Based Reasoning for Estuarine Model Design

Passone, Sara; Chung, Pei-Jung; Nassehi, V.

doi:10.1007/3-540-46119-1_43

Cited by 5 publications

(3 citation statements)

References 4 publications

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“…The work of Oppacher and Duego revealed that using a GA for case adaptation can produce strong cases in a smaller case library [4], where De Silva Garza et al and Louis and Xu found that GA adaptation produces novel solutions to new problems [4,12]. Domains covered using a GA for CBR adaptation include: tablet formulation [13,14]; modelling a Checkers game [1]; Open Shop Scheduling and Rescheduling [12]; developing lay out design of residences so they conform to the principles of feng shui [4]; reducing database size in applications [15]; and estimating the flow rates in Estuaries [16].…”

Section: Previous Adaptation and Learning Methodsmentioning

confidence: 99%

Case-Base Injection Schemes to Case Adaptation Using Genetic Algorithms

Grech

Main

2004

Lecture Notes in Computer Science

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Abstract. Case adaptation has always been a difficult process to engineer within the case-based reasoning (CBR) cycle. To combat the difficulties of CBR adaptation, such as its domain dependency, computational cost and the inability to produce novel cases to solve new problems, genetic algorithms (GAs) have been applied to CBR adaptation. As the quality of cases stored in a case library has a significant effect on the solutions produced by a case-based reasoner, it is important to investigate the impact of the quality and quantity of cases injected into a GA initial population for adapting fitter solutions to new problems. This work explores a method applying a GA to CBR adaptation, where a learning mechanism is applied to feed knowledge back from the CBR revision stage into the reuse stage, allowing the GA to learn which mutations result in invalid solutions. In collaboration with this learning mechanism, the number of cases to be injected, and the fitness of cases to be injected from retrieval into reuse is explored. The fitness of adapted cases and their response to our developed learning feedback is also trialled through varying the size and quality of the GA initial population.

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Section: Previous Adaptation and Learning Methodsmentioning

confidence: 99%

Case-Base Injection Schemes to Case Adaptation Using Genetic Algorithms

Grech

Main

2004

Lecture Notes in Computer Science

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“…Environmental applications include decision support [90,121], modelling estuarine behaviour [126], planning fire-fighting [9], managing wastewater treatment plants [138,137,132,164], monitoring air quality [86], minimising environmental impact through chemical process design [96] and weather prediction [139].…”

Section: Descriptionmentioning

confidence: 99%

Artificial Intelligence techniques: An introduction to their use for modelling environmental systems

Chen

Jakeman

Norton

2008

Mathematics and Computers in Simulation

252

104

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“…CBR has been utilized in some environmental problems: managing wastewater treatment plants (Roda et al 2001), monitoring air quality (Kalapanidas and Avouris 2001;Lekkas, Avouris, and Viras 1994), modeling estuarine behavior (Passone, Chung, and Nassehi 2002), land use classification (X. Li, Ye, and Liao 2004), and soil mapping (Shi et al 2004).…”

Section: Knowledge-based Intelligent Systemsmentioning

confidence: 99%

Artificial Intelligence Solutions for Urban Land Dynamics: A Review

Silva

2010

Journal of Planning Literature

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Artificial intelligence (AI) systems are widely accepted as a technology offering an alternative way to tackle complex and dynamic problems in urban studies. The goal of this article is a review of current literature in the field of planning and AI. The aim of this review is to increase the understanding of how AI approaches urban and land dynamics modeling processes and how, as a result, researchers can structure that knowledge and choose the correct approaches to embed in their models. For this purpose, the authors review the applications of AI techniques in urban land dynamics domain as well as the emerging challenges they face. The authors discuss hybrid AI systems as a need resulting from the trend in planning policy to develop more holistic approaches. The authors conclude that, although challenges exist, AI-based approaches offer promising solutions for urban and land dynamics.

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Case-Based Reasoning for Estuarine Model Design

Cited by 5 publications

References 4 publications

Case-Base Injection Schemes to Case Adaptation Using Genetic Algorithms

Case-Base Injection Schemes to Case Adaptation Using Genetic Algorithms

Artificial Intelligence techniques: An introduction to their use for modelling environmental systems

Artificial Intelligence Solutions for Urban Land Dynamics: A Review

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