Doctoral Consortium@RuleML2015: Genetic Programming for Design Grammar Rule Induction

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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Preliminaries: Functional Decomposition

Black Box Model

(primary function)

Evolved Function Structure

(incl. sub-functions needed for realizing primary function)

Computational support: Graph Rewriting

2


Preliminaries: Graph-Rewriting

G0 Gn-1 Gn

p1 G1 G2

p2 pi …

Black Box

Production Rule pi :

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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

4


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

5


Approach

6


Approach

Black box Desired design graphs

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Approach

Load rules preceding/following the rule to be learned.

8


Approach

Learn a rule that is able to produce one desired design graph.

9


Approach

Check if learned rule is also able to derive other desired design graphs.

10


Approach

If all desired design graphs covered, return set of learned rules.

11


Approach

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Procedure evolve:


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

13


Approach

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Root

Index

Add-Edge

Remove-Non-Terminal Add-Node

Index

Index

Index Which node to add?

Which host graph?


Approach

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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

16


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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Thank you!

Questions? Questions!

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Technology

Doctoral Consortium@RuleML2015: Genetic Programming for Design Grammar Rule Induction