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Intelligent Agent for Designing Steel Skeleton Structures of Tall Buildings . Zbigniew Skolicki Rafal Kicinger. Outline. Intelligent Agents (IAs) Ontologies Inventor 2001 Ontology of steel skeleton structures for Inventor 2001 Disciple and rule learning Results and conclusions.
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Intelligent Agent for Designing Steel Skeleton Structures of Tall Buildings Zbigniew Skolicki Rafal Kicinger
Outline • Intelligent Agents (IAs) • Ontologies • Inventor 2001 • Ontology of steel skeleton structures for Inventor 2001 • Disciple and rule learning • Results and conclusions
Intelligent Agents: Background • Advancements in computer power, programming techniques, design paradigms • New areas, previously reserved for humans • Interaction instead of subordination
Intelligent Agents: Characteristics • Autonomy and continuity • Communication and cooperation • Environment and situatedness • Perceiving • Reasoning • (Re-)acting • Knowledge and learning
Intelligent Agents: Interface Agents • Acting as assistants • Monitoring and suggesting • Being interactive, taking initiative • Possessing knowledge about domain (ontology) • Cooperating with non-expert users • Learning
Ontologies • “Repositories of knowledge”, defining the vocabulary of a domain • Both common and expert knowledge • IAs can “understand” a domain • Supported with inference engines • Formats: OKBC, KIF • Cyc, Ontolingua, Loom, Protégé-2000, Disciple
Inventor 2001: Overview • Evolutionary design and research tool for designing steel skeleton structures in tall buildings • Produces both design concepts and detailed designs • Uses process of evolution to search through the design space
Inventor 2001:Design Representation Space • Planar transverse designs of steel skeleton structures in tall buildings • 3-bay structures • 16-36 stories • 6 types of bracings • 2 types of joints between beams and columns • 2 types of ground connections 16-36 stories 3 bays
Ontology of Steel Skeleton Structures for Inventor 2001 ………… ………… …………
Disciple: Overview • “Learning agent shell” built at GMU • Tool for building ontologies and IAs • Ontology: acyclic graph of concepts, together with instances and relationships • Multi-strategy learning of rules representing expert knowledge
Disciple: Multi-strategy learning • Learning from examples • Modified plausible version space (PVS) learning strategy • Based on generalization and specialization • Learning by analogy • Learning from explanation
Rule learning • Modeling (natural language) • Formalization (structured language) • Rule learning (explanations, PVS) • Rule refinement (accepting/rejecting examples)
Rule learning: Explanations, Plausible Version Space • Rules are generated • Task (question) “IF” part • Answer + explanation “THEN” part • Every variable defined by lower and upper bounds (concepts from the ontology)
Rule learning: Rule refinement • Disciple generates new examples • Expert accepts or rejects them, refines explanations • Rules are refined When the learning phase is finished, Disciple generates solutions
Example of a Modeled Design and a Design Generated by the Agent First_design_01 of 16-Story_building_01 uses Rigid_beam only, and Central_vertical_truss_01 and Top_horizontal_truss_01 and has Rigid_connection as a type of ground connection Translator Translator Third_design_01 of 20-Story_building_01 , which uses Hinged_beam only, and Central_vertical_truss_01 , and uses no horizontal trusses, and has Rigid_connection as a type of ground connections
Results and conclusions • IA was able to learn simple design rules • IA could generalize these rules based on the underlying knowledge stored in the ontology • It was able to generate simple examples of steel skeleton structures • Using user’s evaluation of generated design concept the ruled have been refined by the agent
Results and conclusions but… • It used only a very simple, and restricted domain (very general engineering knowledge was modeled) • Modeling of a designer’s problem solving process was very simplistic • Some underlying assumptions on the problem to be solved are required using Disciple approach – task reduction and decomposition of problems
Further Work • Determining the feasibility of this approach in more complex domains • Building a broader repository of engineering knowledge in a form of large civil engineering ontology • Integration of knowledge-based applications with engineering optimization support tools
References • Anumba, C. J., Ugwu, O. O., Newnham, L., and Thorpe, A. (2002). "Collaborative Design of Structures Using Intelligent Agents." Automation in Construction, 11, 89-103. • Murawski, K., Arciszewski, T., and De Jong, K. A. (2001). "Evolutionary Computation in Structural Design." Journal of Engineering with Computers, 16, 275-286. • Tecuci, G. (1998). Building Intelligent Agents: An Apprenticeship Multistrategy Learning Theory, Methodology, Tool, and Case Studies, Academic Press. • Tecuci, G., Boicu, M., Bowman, M., and Marcu, D. (2001). "An Innovative Application from the Darpa Knowledge Bases Programs: Rapid Development of a High Performance Knowledge Base for Course of Action Critiquing." AI Magazine, 22(2). • Wooldridge, M. J., and Jennings, N. R. (1995). "Intelligent Agents: Theory and Practice." The Knowledge Engineering Review, 10(2).