Towards Grammars for Cradle -to-Cradle Design Douglas H. Fisher Vanderbilt University

1. 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

2. 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-

3. 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?

4. Product families To achieve full reuse with no degradation in material, don’t (simply) rely on post- design recycling opportunism; Product A but design product families with more efficient reuse cycles, with known and predictable trajectories for reused material and shared energy. Product A Product D Product B Product E Product C

5. Product families can increase reuse opportunities http://www.preserveproducts.com/

6. Product families can share supply chain resources http://www.patagonia.com/us/footprint/

7. 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 Product Family 2 loose across-family coupling Product Family 1 Product Family 4 Product Family 3

8. 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

9. Design grammars A simple material component grammar and leftmost derivation of a product: T  Handle Head . . . Handle  Grip Back Head  Base Bristles Grip  aa Grip  ab Grip  aba Back  bb Back  b Base  b Bristles 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

10. Searching the space of designs T T Handle Head T Body Bristles T Handle Head Grip Back . T Handle Head Grip Back Base Brist T Handle Head Grip Back Base Brist T Handle Head Grip Back Base Brist aba b b c aa bb b c ab bb b c

11. Choosing among designs and derivations using preferences and constraints T Handle Head Grip Back Base Bristles Preferences can be based on terminal strings (designs), T Handle Head Grip Back Base Bristles aa bb b c P1 > T Handle Head Grip Back Base Bristles ab bb b c P2 > and also on the derivations of these strings aba b b c P3

12. Augmenting grammars for better assessments where E is energy required of compositional or disassembly steps corresponding to a transition, … α β E T Handle Head Grip Back Base Bristles ab bb b c P2 where P can be a function of energy required by a design (dis)assembly

13. Backing up preferences and constraints using machine learning T Handle Head Grip Back Base Bristles T Handle Head Grip Back Base Bristles ab aa aba bb b b c aa b c b aa Back  aa b Relevant machine learning methods include grammar induction search control learning clustering explanation-based learning

14. 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

15. Searching the space of product family designs S P1 P2

16. 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’ What are desirable formal properties of C2C grammars: α +*+> β -*-> γ2 α +*+> β -*-> γ1 α +*+> β -*-> α 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) This can’t be CFG?

17. Community architecture to support C2C design Design Grammar Base Grammars for product lines, product families, background knowledge (e.g., material equivalences) T  ….. H  ….. S  SS S  [T] ... Grammar induction and revision Design Space Exploration Product and Product Family Design Base ‘aabbbc’ ‘bcdddee’ Existing designs ‘addeeef’ … ‘dhhjjj’ …… * 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

18. 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