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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
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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
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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
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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
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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
3 bays
16
-36 s
tori
es
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Ontology of Steel Skeleton Structures for Inventor 2001
Inventor_initial_design
OBJECT
Inventor_population Building
Logical_component
Element_type
Structural_element
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Ontology of Steel Skeleton Structures for Inventor 2001
Building
Low_Building
Medium_Building
High_Building
16_Story_building
20_Story_building
24_Story_building
26_Story_building
30_Story_building
32_Story_building
36_Story_building
16-Story_building_01
20-Story_building_01
24-Story_building_01
26-Story_building_01
30-Story_building_01
32-Story_building_01
36-Story_building_01
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Ontology of Steel Skeleton Structures for Inventor 2001
Grou nd_connection_01
Logical_component
Story_02Story_01
Story BayGround
Story_03 Story_35Story_34 Story_36 Left_Bay Middle_Bay Right_Bay
Left_bay_01
Vertical_truss
Truss
Horizontal_truss
Middle_bay_01
Right_bay_01
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Ontology of Steel Skeleton Structures for Inventor 2001
Structural_element
Beam DiagonalGround_
connection
Connection_1 Connection_2 Connection_3 Connection_4
Column
………… ………… …………
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Ontology of Steel Skeleton Structures for Inventor 2001
column04_left
beam05_left beam05_middle beam05_right
column04_middle1 column04_middle2 column04_right
diagonal04_left
diagonal04_middle diagonal04_right
beam04_left beam04_middle beam04_right
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Ontology of Steel Skeleton Structures for Inventor 2001
Beam_ typeDiagonal_
type
Ground_connection_
type
Hinged_beamRigid_beam
No_bracing K_bracing X _bracing Left _diagonal_ bracing
Right_diagonal_bracing
Simple_X_bracing
V_bracing
Hinged_connection
Rigid_connection
Element_type
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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
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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
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Rule learning
Modeling (natural language) Formalization (structured language) Rule learning (explanations, PVS) Rule refinement (accepting/rejecting examples)
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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)
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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
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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
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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, 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).