towards grammars for cradle -to-cradle design douglas h. fisher vanderbilt university
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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 - PowerPoint PPT PresentationTRANSCRIPT
Towards Grammars for Cradle-to-Cradle Design
Douglas H. Fisher Vanderbilt University
Mary Lou Maher University of Maryland, College Park
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 familiesA 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 grammarsShape 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