Towards Grammars for Cradle-to-Cradle Design

Douglas H. Fisher Vanderbilt University

douglas.h.fisher@vanderbilt.edu

Mary Lou Maher University of Maryland, College Park

marylou.maher@gmail.com

Presentation to the 2011 AAAI Spring Symposium on

Artificial Intelligence and Sustainable Design

Formal Representations

Formal representations of designs and products enable

definition of sustainability-related design and classes of design e.g., the class of reversible designs (inspired by the class of reversible domains, Rich 1991):

α β α, where energy E+ ≈ E-

establishment of community wide standards for design KBs

automated approaches to large-scale design exploration

enablement of machine learning from this exploration

Our goals: new ideas on C2C design and synthesizing across existing and new sustainable design activities

*-*+

E+ E-

Cradle-to-Cradle (C2C) Design

Motivation: Design for very long-term planet sustainability

Full reuse of material from one product life to next, with no degradation in material (eliminate material leakage)

Energy conservation through minimization, repurposing, multi-purposing (minimize energy leakage)

Separation of biological and technological/synthetic material cycles, to avoid monstrous hybrids (McDononough and Braungart)

Toxins not encouraged, but not disallowed per se, so long as completely separable

How do C2C designs map on to design classes? How to facilitate C2C designs through standards? Community KBs? Machine learning?

Product A

Product B

Product C

Product E

Product D

Product A

To achieve full reuse with no degradation in material,

Product families

don’t (simply) rely on post- design recycling opportunism;

but design product families with more efficient reuse cycles, with known and predictable trajectories for reused materialand shared energy.

http://www.preserveproducts.com/

Product families can increase reuse opportunities

http://www.patagonia.com/us/footprint/

Product families can share supply chain resources

Product families

A product family is a cluster of products, with

relatively tight within-family coupling of energy and material sharing (to include processing materials, such as solvents), and

loose across-family coupling

Product Family 1

Product Family 4

Product Family 2

Product Family 3

Design grammars

Shape grammars (Stiny & Gips, 1972; Stiny 1980) a variant on context-free grammars, specify a language/set of designs

Descendents of shape grammar formalism are many, including parametric,

color,

description,

structure and

parallel grammars

Design grammars

A simple material component grammar and leftmost derivation of a product:

T Handle Head. . .Handle Grip BackHead Base BristlesGrip aa Grip ab Grip aba Back bb Back b Base bBristles c. . .

T Handle Head (T Handle Head) Grip Back Head (Handle Grip Back) aa Back Head (Grip aa) aa bb Head (Back bb) aa bb Base Bristles (Head Base Bristles) aa bb b Bristles (Base b) aa bb b c (Bristles c)

Variables T, Handle, Head, Grip, Back, Base, and Bristles represent functional components of a product line

a, b, c are terminal symbols representing materials, with cardinality of each symbol representing amount of that material

T

Handle Head

Grip Back Base Brist

aa bb b c

T

Handle Head

Grip Back Base Brist

aba b b c

T

Handle Head

Grip Back Base Brist

ab bb b c

T

T

Handle Head

T

Body Bristles

T

Handle Head

Grip Back .

Searching the space of designs

Choosing among designs and derivations

using preferences and constraintsT

Handle Head

Grip Back Base Bristles

aa bb b c

T

Handle Head

Grip Back Base Bristles

aba b b c

T

Handle Head

Grip Back Base Bristles

ab bb b c

P1

P2

P3

>

>

Preferences can be based on terminalstrings (designs),

and also on the derivations of thesestrings

Augmenting grammars for better assessments

T

Handle Head

Grip Back Base Bristles

ab bb b c

P2

α β E

where E is energy required of compositionalor disassembly steps corresponding to a transition, …

where P can be a function of energy required by a design (dis)assembly

Backing up preferences and constraints using machine learning

T

Handle Head

Grip Back Base Bristles

ab aa aba bb b b c

T

Handle Head

Grip Back Base Bristles

aa b cb aa Back aa b

Relevant machine learning methods include

grammar induction search control learning clustering

explanation-based learning

Design grammars for product families

Given grammars for product lines (e.g., tooth and hair brushes) with start symbols T and H, form a new grammar with transitions

S SS S [T] S [H]

In principle, a grammar for a product family has the same form as a grammar for a single line of composite product

This is a weak grammar, overly-inclusive, generating many designs that are not desirable by C2C preference criteria

Machine learning methods can be applied to a weak product family grammar, thereby improving it

SSearching the space of product family designs

P2P1

What are desirable formal properties of C2C grammars:

α +*+> β -*-> γ1 α +*+> β -*-> γ2

Reversibility?

Resource constrained, recycling grammars: specified resources (e.g., material terminals) are never exceeded

β1, β2, β3, …, βn ♯α β2, β3, …, βn ♯α+ where α,α+, βi are strings of

terminals

where Ů α,βi = Ů α+,βi (terminals and cardinality preserved)

What are desirable properties of C2C grammars

Gives insight into construction, disassembly, and reuse: with transition, associate energy required, processing materials, expected costs of‘externalities’

α +*+> β -*-> α

This can’t be CFG?

‘aabbbc’

‘addeeef’

‘bcdddee’

‘dhhjjj’

Community architecture to support C2C design

Product and Product Family Design Base

T ….. H …..

Design Grammar Base

S SSS [T] ...

……

Design SpaceExploration

Existing designs

Grammarinduction

and revision

Grammars for product lines, product families, background knowledge (e.g.,

material equivalences)

…

* Machine learning from weak product family grammars to strong grammars* Clustering algorithms: discovery of product families from terminal

strings/designs* Rule induction: Inducing (macro) rules/transitions from derivations

Conclusions/Challenges

• Grammars as a formal models for sustainable design:– Characterizes the principles of sustainability, e.g., C2C design– Provides a standard representation for describing product

designs, e.g. as augmented material component grammars– Identifies characteristics of C2C products and product families,

and grammars for such • Machine learning to advance our understanding of sustainability

– Machine learning methods for mining community knowledge bases

– Machine learning methods for moving from weak, non-C2C grammars, to strong C2C grammars

• More generally – survey and synthesis of existing efforts and formalisms; establishing community standards and infrastructure informed by this analysis; decision making and learning tools to ex