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Article: Principled approach to design of hierarchical abstractions

TitlePrincipled approach to design of hierarchical abstractions
Authors
Issue Date1995
Citation
Journal Of Computing In Civil Engineering, 1995, v. 9 n. 2, p. 177-189 How to Cite?
AbstractThe use of hierarchical abstractions allows processing at an abstract level so that the complexity of low-level processing can be avoided or delayed. The idea of hierarchical abstractions is a simple but powerful one in intelligent engineering systems. It is commonly used in a wide range of applications, such as those for design, planning, learning, and natural language processing. It is also ubiquitous in many general paradigms for intelligent systems, such as heuristic search and blackboard architectures. Despite its usefulness and widespread use, the reduction in complexity achieved through hierarchical abstractions has been only informally understood. This can lead to suboptimal designs of hierarchical abstractions. In the present paper, we derive formulas to show how hierarchical abstractions reduce complexity in intelligent engineering systems. Formalizing the costs and benefits of hierarchical abstractions also allows us to see exactly when what kinds of abstractions are useful. We illustrate the practicality of this approach with IPDOS, a system for designing offshore structures. With an improved understanding of the power of hierarchical abstractions, we hope to stimulate even greater use of such abstractions in computing for engineering.
Persistent Identifierhttp://hdl.handle.net/10722/156422
ISSN
2015 Impact Factor: 1.855
2015 SCImago Journal Rankings: 1.219
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorSoh, CheeKiongen_US
dc.contributor.authorSoh, AiKahen_US
dc.contributor.authorLai, KumYewen_US
dc.date.accessioned2012-08-08T08:42:22Z-
dc.date.available2012-08-08T08:42:22Z-
dc.date.issued1995en_US
dc.identifier.citationJournal Of Computing In Civil Engineering, 1995, v. 9 n. 2, p. 177-189en_US
dc.identifier.issn0887-3801en_US
dc.identifier.urihttp://hdl.handle.net/10722/156422-
dc.description.abstractThe use of hierarchical abstractions allows processing at an abstract level so that the complexity of low-level processing can be avoided or delayed. The idea of hierarchical abstractions is a simple but powerful one in intelligent engineering systems. It is commonly used in a wide range of applications, such as those for design, planning, learning, and natural language processing. It is also ubiquitous in many general paradigms for intelligent systems, such as heuristic search and blackboard architectures. Despite its usefulness and widespread use, the reduction in complexity achieved through hierarchical abstractions has been only informally understood. This can lead to suboptimal designs of hierarchical abstractions. In the present paper, we derive formulas to show how hierarchical abstractions reduce complexity in intelligent engineering systems. Formalizing the costs and benefits of hierarchical abstractions also allows us to see exactly when what kinds of abstractions are useful. We illustrate the practicality of this approach with IPDOS, a system for designing offshore structures. With an improved understanding of the power of hierarchical abstractions, we hope to stimulate even greater use of such abstractions in computing for engineering.en_US
dc.languageengen_US
dc.relation.ispartofJournal of Computing in Civil Engineeringen_US
dc.titlePrincipled approach to design of hierarchical abstractionsen_US
dc.typeArticleen_US
dc.identifier.emailSoh, AiKah:aksoh@hkucc.hku.hken_US
dc.identifier.authoritySoh, AiKah=rp00170en_US
dc.description.naturelink_to_subscribed_fulltexten_US
dc.identifier.doi10.1061/(ASCE)0887-3801(1995)9:2(177)en_US
dc.identifier.scopuseid_2-s2.0-0029289855en_US
dc.identifier.volume9en_US
dc.identifier.issue2en_US
dc.identifier.spage177en_US
dc.identifier.epage189en_US
dc.identifier.isiWOS:A1995QN65800009-
dc.publisher.placeUnited Statesen_US
dc.identifier.scopusauthoridSoh, CheeKiong=7102818722en_US
dc.identifier.scopusauthoridSoh, AiKah=7006795203en_US
dc.identifier.scopusauthoridLai, KumYew=7402135575en_US

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