Intelligent Agent for Designing Steel Skeleton Structures of Tall Buildings Zbigniew Skolicki Rafal Kicinger

2 Outline Intelligent Agents (IAs) Ontologies Inventor 2001 Ontology of steel skeleton structures for Inventor 2001 Disciple and rule learning Results and conclusions

3 Intelligent Agents: Background Advancements in computer power, programming techniques, design paradigms New areas, previously reserved for humans Interaction instead of subordination

4 Intelligent Agents: Characteristics Autonomy and continuity Communication and cooperation Environment and situatedness Perceiving Reasoning (Re-)acting Knowledge and learning

5 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

6 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

7 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

8 Inventor 2001: Design Representation Space Planar transverse designs of steel skeleton structures in tall buildings 3-bay structures stories 6 types of bracings 2 types of joints between beams and columns 2 types of ground connections 3 bays stories

9 Ontology of Steel Skeleton Structures for Inventor 2001

10 Ontology of Steel Skeleton Structures for Inventor 2001

11 Ontology of Steel Skeleton Structures for Inventor 2001

12 Ontology of Steel Skeleton Structures for Inventor 2001 …………

13 Ontology of Steel Skeleton Structures for Inventor 2001

14 Ontology of Steel Skeleton Structures for Inventor 2001

15 Ontology of Steel Skeleton Structures for Inventor 2001

16 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

17 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

18 Rule learning Modeling (natural language) Formalization (structured language) Rule learning (explanations, PVS) Rule refinement (accepting/rejecting examples)

19 Rule learning: Modeling

20 Rule learning: Formalization

21 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)

22 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

23 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 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 Translator

24 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

25 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

26 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

27 References Anumba, C. J., Ugwu, O. O., Newnham, L., and Thorpe, A. (2002). "Collaborative Design of Structures Using Intelligent Agents." Automation in Construction, 11, Murawski, K., Arciszewski, T., and De Jong, K. A. (2001). "Evolutionary Computation in Structural Design." Journal of Engineering with Computers, 16, 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).