Norm: an expert system for development control in underdeveloped operational contexts

Borri, Dino; Conte, Emilia; Pace, F; Selicato, Francesco

doi:10.1068/b210035

Cited by 7 publications

(5 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Although Leary and Rodriguez-Bachiller (1989) argued that this expertise was more structured and capable of being articulated than expected, their decision-tree method seems to have the following problems: (a) In development control it is often not easy to subdivide a goal into several definite subgoals; (b) The decision tree may oversimplify the relationships among the subgoals; and (c) The method tends to tell the user what he or she should or should not do, rather than how to do it. Meanwhile, interesting conclusions can be drawn from the practical experience of some research (Barbanente et al, 1995;Borri et al, 1994). (a) Human experts often resort to similar cases to streamline their problem-solving.…”

Section: Why Use Cbs In Development Control?mentioning

confidence: 99%

“…1 Introduction Development control is one of the most common areas of application of knowledgebased systems (KBS) in urban planning (for example, see Barbanentc et al, 1995;Borri et al, 1994;Hcikkila and Blewett, 1992;Leary, 1987;Leary, 1988;Leary and Rodriguez-Bachiller, 1989;Sharp et al, 1991). This is mainly because it is considered to be a domain that is quite structured but also has some flexibility.…”

mentioning

confidence: 99%

“…Development control is largely guided by ordinances and regulations. It is thought to have a well-structured framework and solutions (Barbanente et al, 1995;Borri et al, 1994;Leary, 1988;Leary and Rodriguez-Bachiller, 1989). Its problem-solving process would easily fit the rulesof-thumb architecture that is commonly used in KBS.…”

mentioning

confidence: 99%

See 2 more Smart Citations

The integration of case-based systems and GIS in development control

Shi

Ych

1999

Environ. Plann. B

View full text Add to dashboard Cite

Case-based systems (CBS) are a type of knowledge-based system (KBS) based on case-based reasoning (CBR), in which the underlying philosophy is to use previous cases to interpret or solve a new problem. There are many advantages of CBR over rule-based reasoning, which is the method adopted by most current KBS for development control. The knowledge representation scheme of CBS is better than rule-based reasoning in terms of recording and representing knowledge that is difficult to express with explicit rules or is too case specific. The results derived directly from real cases in CBR are more convincing and acceptable to planners. Development control is used to examine the applicability of CBR in urban planning. It was selected because, amongst all the functions of urban planning, it is relatively easy to deal with the problems of building a CBS for development control, such as how to define a case, how to index cases, how to retrieve and adapt a case, and how to obtain the outcome of a decision. A system that integrates a CBR shell (ESTEEM) and GIS package (ArcView) was developed to demonstrate the process of building a CBS to assist planners in the Planning Department of Hong Kong to handle planning applications in development control.

show abstract

Section: Why Use Cbs In Development Control?mentioning

confidence: 99%

mentioning

confidence: 99%

See 1 more Smart Citation

The integration of case-based systems and GIS in development control

Shi

Ych

1999

Environ. Plann. B

View full text Add to dashboard Cite

show abstract

“…If one is keen enough to further the art and science of one's own field, anything is worth a try. Hence several articles have been published in Environment and Planning B on the application of expert systems to environmental planning, for example, those by Borri et al (1994), Han et al (1991), Leary (1988), and Davis and Grant (1987). But sure enough, the more successful applications have tended to be in the less ambiguous, more procedurally exact areas of planning, such as interpretation of planning legislation and identification of what is possible under a set of building regulations.…”

Section: Revivalmentioning

confidence: 99%

Guest Editorial

Wyatt¹

1996

Environ Plann B Plann Des

View full text Add to dashboard Cite

Transcending the retreat from rationalityOne of the aims of this issue is to provide something for planning practitioners. Unlike their academic cousins, planning practitioners have seldom had the luxury of 'retreating from rationality'. They have always had a job to do, rationally; in this job they were once comprehensively assisted by researchers into computer-aided planning techniques. Yet the intensity of academia's retreat from rationality and the popularity of its rejection of 'rational comprehensiveness' have tended to inhibit such assistance for at least two decades.Recently, however, there have been signs of a regrouping. New forms of 'organic' computing have emerged and their application to planning problems has been widespread. They therefore have the potential to restore academia's technical assistance back to something like the levels enjoyed by practitioners during the 1950s and 1960s. Accordingly, although it hardly proclaims that a second quantitative revolution is almost at hand (Openshaw, 1993), this special issue attempts to present, clearly, some cutting-edge techniques which have great promise.The techniques of interest can be loosely grouped under the umbrella term of 'connectionist' computing techniques. Each focuses on connections within data. They include simulated neural networks, which 'learn' to connect inputs with their associated outputs, and genetic algorithms, which connect parts of some problem solutions to parts of other problem solutions in order to 'breed' improved solutions. They also include cellular automata, which connect the simple behaviours of individual elements in order to generate complex models of the overall system.To appreciate fully the true potential of these techniques, it is necessary to understand how their planning applications evolved. And all applications were able to evolve only because of at least four decades of progress within three separate disciplines-computer-aided planning, artificial intelligence, and connectionist computing itself. Each of these fields passed through similar phases of euphoria in the 1950s and 1960s, disillusion during the 1970s and 1980s, and revival throughout the 1990s. But there were differences in the depths of unpopularity to which their techniques sank during the dark days, there were differences in their levels of 'born again' enthusiasm during the 1990s, and so today there are differences in the nature and potential of their respective contributions.Accordingly, in this editorial essay I will trace the evolution of each field. The conclusion is that connectionist-type computer-aided planning is far less ambitious, and more modest, than the modelling which occurred during the heady days of the 1960s.But I will also argue that the chances of a genuinely productive partnership between humanist and boffin are far greater this time around. Moreover, I will suggest there are signs that computer-based comprehensive contributions to planning, as distinct from the segmented and restricted contributions of today, may one day becom...

show abstract

“…Many potential applications have been explored by researchers and a few prototypes and even operational systems have been built (for example, see Barbanente et al, 1995;Borri et al, 1994;Davis and Grant, 1990;Findikaki, 1990;Han and Kim, 1990a;Lee and Wiggins, 1990;Rouhani and Kangari, 1990;Sharpe et al, 1991). However, they generally take a cautious attitude to the use of KBS in this domain (for example, see Barbanente et al, 1995;Borri et al, 1994;Han and Kim, 1990b;Marchand, 1993;Ortolano and Perman, 1990). There are two main obstacles to the successful application of KBS in urban planning-difficulties in building a practical KBS for planning, and the difficulties encountered in "inspiring confidence in the use of it" (Marchand, 1993, page 88).…”

mentioning

confidence: 99%

Applying case-based reasoning to urban planning: a new planning-support system tool

Yeh

Shi

1999

Environ. Plann. B

View full text Add to dashboard Cite

Because of the high complexity, uncertainty, and subjectivity of urban planning, it is a domain from which knowledge is often hard to extract and/or represent by the knowledge-representing methods of knowledge-based systems (KBS) which require explicit, definite, and generalized rules of thumb or causal models, Furthermore it is difficult for a user to understand and interact with the black-box inference process of the system. It is more suitable for solving routine problems but lacks the capability of storing case-dependent knowledge which is needed by planners to generate creative solutions or to deal with exceptional problems. Therefore, in the past, there has been little application of KBS in urban planning. In this paper we discuss the applicability of case-based systems—a methodology in which previous examples (cases) arc used to solve, evaluate, or interpret a new problem—in urban planning which can help overcome the knowledge elicitation and black-box operation problems of rule-based and model-based KBS, Besides pointing out the advantages of case-based reasoning over conventional KBS in knowledge acquisition and user acceptance, we give an overview of its potentials as a planning-support system and examine some of the problems in using this method.

show abstract

Norm: an expert system for development control in underdeveloped operational contexts

Cited by 7 publications

References 0 publications

The integration of case-based systems and GIS in development control

The integration of case-based systems and GIS in development control

Guest Editorial

Applying case-based reasoning to urban planning: a new planning-support system tool

Contact Info

Product

Resources

About