Intelligent Agent for Designing Steel Skeleton Structures of Tall Buildings

Zbigniew Skolicki

Rafal Kicinger

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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 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

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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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Ontology of Steel Skeleton Structures for Inventor 2001

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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: Modeling

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Rule learning: Formalization

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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

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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

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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

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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).