The knowledge engineering effort associated with defining grammar systems can become a barrier for the practical use of such systems. Existing grammar and rule induction algorithms offer rather limited support for discovering context-sensitive graph grammar rules as required by some applications in the domain of engineering design. For this task the present work proposes a rule induction method grounded on Genetic Programming. Specializations regarding the representation and evaluation of rule candidates are discussed. Results from preliminary experiments with a prototype implementation demonstrate the feasibility of the suggested approach.

1. Institut für Rechnergestützte Ingenieursysteme
Prof. Dr. Dieter Roller
Universität Stuttgart
Genetic Programming for Design Grammar Rule Induction
– RuleML 2015 –
Julian R. Eichhoff & Dieter Roller
Institute of Computer-aided Product Development Systems
Universität Stuttgart
Universitätsstraße 38, 70569 Stuttgart
n Preliminaries
n Problem
n Approach
n Results
n Future Work
Overview
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2. Institut für Rechnergestützte Ingenieursysteme
Prof. Dr. Dieter Roller
Universität Stuttgart
Preliminaries: Functional Decomposition
Black Box Model
(primary function)
Evolved Function Structure
(incl. sub-functions needed for realizing primary function)
Computational support:
Graph Rewriting
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3. Institut für Rechnergestützte Ingenieursysteme
Prof. Dr. Dieter Roller
Universität Stuttgart
Preliminaries: Graph-Rewriting
G0 Gn-1 Gn
p1
G1 G2
p2 pi
…
Black Box
Production
Rule pi :
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4. Institut für Rechnergestützte Ingenieursysteme
Prof. Dr. Dieter Roller
Universität Stuttgart
Problem
n Rules represent human design rationale à elicitation is costly
n Reduce effort of knowledge engineering by automatic rule induction
n Learn rules in context of existing rulesets
n Definitions of existing rules are kept secret
n Training resources:
n Samples of expected (positive) behavior:
Black box (Input) – desired design graphs (Output)
n Access to graph-rewriting system to query derivations
G0
p1
G1 G2
p2
…
Black Box
Gi-1 Gi
… Gn-1 Gn
pi pn
Desired Design Graph
Unkown Rule
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5. Institut für Rechnergestützte Ingenieursysteme
Prof. Dr. Dieter Roller
Universität Stuttgart
Approach
n Machine Learning:
Search for optimal hypotheses that best explain the training data
n Greedy grammar induction:
Extend existing rule set by “next best rule”
n Find next best rule by means of evolutionary optimization
à Genetic Programming
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6. Institut für Rechnergestützte Ingenieursysteme
Prof. Dr. Dieter Roller
Universität Stuttgart
Approach
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7. Institut für Rechnergestützte Ingenieursysteme
Prof. Dr. Dieter Roller
Universität Stuttgart
Approach
Black box Desired design graphs
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8. Institut für Rechnergestützte Ingenieursysteme
Prof. Dr. Dieter Roller
Universität Stuttgart
Approach
Load rules preceding/following the rule to be learned.
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9. Institut für Rechnergestützte Ingenieursysteme
Prof. Dr. Dieter Roller
Universität Stuttgart
Approach
Learn a rule that is able to produce one desired design graph.
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10. Institut für Rechnergestützte Ingenieursysteme
Prof. Dr. Dieter Roller
Universität Stuttgart
Approach
Check if learned rule is also able to derive other desired design graphs.
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11. Institut für Rechnergestützte Ingenieursysteme
Prof. Dr. Dieter Roller
Universität Stuttgart
Approach
If all desired design graphs covered, return set of learned rules.
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12. Institut für Rechnergestützte Ingenieursysteme
Prof. Dr. Dieter Roller
Universität Stuttgart
Approach
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Procedure evolve:

13. Institut für Rechnergestützte Ingenieursysteme
Prof. Dr. Dieter Roller
Universität Stuttgart
Approach
G0
p1
G1 G2
p2
…
Black Box
Gi-1 Gi
… Gn-1 Gn
pi pn
Desired Design Graph
Unkown Rule
Monotonic elements must
appear in both graphs
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14. Institut für Rechnergestützte Ingenieursysteme
Prof. Dr. Dieter Roller
Universität Stuttgart
Approach
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Root
Index
Add-Edge
Remove-Non-Terminal Add-Node
Index
Index
Index
Which node to add?
Which host graph?

15. Institut für Rechnergestützte Ingenieursysteme
Prof. Dr. Dieter Roller
Universität Stuttgart
Approach
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16. Institut für Rechnergestützte Ingenieursysteme
Prof. Dr. Dieter Roller
Universität Stuttgart
Results
n Task: Learn rules from an existing hand-crafted ruleset for functional design
Leave one rule out, learn it, and compare the learned rule with original rule
n Comparison with existing rule induction algorithm (Subdue),
which learns a completely new rule set
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17. Institut für Rechnergestützte Ingenieursysteme
Prof. Dr. Dieter Roller
Universität Stuttgart
Future Work
n Fully integrated grammar induction:
See http://ouky.de/accompanying-materials/ruleml-2015/ for a discussion on a
possible iterative application of the proposed approach.
n Repeat experiments with further design grammars
n Approach where rules are allowed to be inspected by the learner
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18. Institut für Rechnergestützte Ingenieursysteme
Prof. Dr. Dieter Roller
Universität Stuttgart
Thank you!
Questions? Questions!
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