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
 16-36 stories
 6 types of bracings
 2 types of joints between beams and columns
 2 types of ground connections
3 bays

Column
Ground_ connection
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Ontology of Steel Skeleton
Structures for Inventor 2001
Connection_4
OBJECT
Inventor_ ini tial_design
Inventor_ population Building
Logical _ component
Element _ type
Structural_ element

Column
Ground_ connection
Ontology of Steel Skeleton
Structures for Inventor 2001
Connection_4
Building
Low _
Building
M edium_
Building
High_
Building
16_Story_ building
10
16-Story_ building_01
20_Story_ building
24_Story_ bui lding
26_Story_ building
30_Story_ building
32_Story_ building
36_Story_ building
20-Story_ building_01
24-Story_ building_01
26-St ory_ building_01
30-Story_ building_01
32-Story_ building_01
36-Story_ building_01

Column
Ground_ connection
Ontology of Steel Skeleton
Structures for Inventor 2001
Connection_4
Logical_ component
Story Ground Bay
Story_01
Story_02
Story_03 Story_34
Story_35
Story_36
Ground_ connection_01
Left_Bay Middle_Bay Right_Bay
Left_bay_01
Middle_ bay_01
Right_bay_01
Vertical_ truss
Truss
Horizontal_ truss
11

Column
Ground_ connection
Ontology of Steel Skeleton
Structures for Inventor 2001
Connection_4
Structural_ element
Ground_ connection
Beam Diagonal Column
Connection_1 Connection_2 Connection_3 Connection_4
…………
12
………… …………

13
Ontology of Steel Skeleton
Structures for Inventor 2001 column04_left beam05_left beam05_middle column04_middle1 beam05_right column04_middle2 column04_right diagonal04_left beam04_left beam04_middle beam04_right diagonal04_right diagonal04_middle

Column
Ground_ connection
Ontology of Steel Skeleton
Structures for Inventor 2001
Connection_4
Element_ type
Beam_ type
Diagonal_ type
Ground_ connection_ type
Rigid_ connection
Hinged_ connection
Rigid_beam Hinged_beam
No_bracing K_bracing X _bracin g
Left _diagonal
_ bracing
Right_diagonal
_bracing
Simp le_X_ bracing
V_b racing
14

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

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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

20
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)

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
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

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

Results and conclusions
25 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

References
27
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