1. The Science of Design

2. What is Design? Science vs. Engineering – Science teaches about natural things where engineering teaches about artificial things Design vs. Engineering – Engineering used to be more about design but, like many other professional disciplines, became taught more as (applied) math and science Design vs. Art – Teaches process of idea generation and artifact creation Where do you learn about design in Computer Science?

3. Representing Goals Need for imperative logic – Paradoxes of using declarative logic for imperative statements Optimization methods – Command variables represent inner design Quantities of foods – Outer environment represented as probability distributions Prices of foods, nutritional contents – Utility function computes goodness of particular set of command variables for expected context Nutritional requirements, cost of diet

4. Satisficing Some design settings overload computing requirements – Approximation algorithms Many design settings do not enable enumerable goals and environmental variables Consider the problem of being general manager for a basketball team – Extreme simple solution -> taking into account more information Density of acceptable solutions determines how long search will take

5. Finding Alternatives How to decide which potential designs to examine? General Problem Solver (GPS) – Represent differences between current state and desired state – Determine actions likely to reduce that difference – Best-first search Avoiding local maxima – Examine some non-best paths (e.g. simulated annealing) – Combine the features of known good alternatives (e.g. genetic algorithms)

6. Designing Design But defining the problem is the problem – Coevolution of problem and solution spaces Wicked problems (Horst Rittel) – Incomplete, contradictory, and changing requirements Hierarchic resource allocation in design – Progressively design from very general to precise – Each step decides how much resources to put in subdesigns – Can decide how much effort to expend in collecting additional information in human design processes – Estimations of utility and future costs are difficult

7. Hierarchy as Outcome Design decomposition – Semi-independent components – Alternative decompositions How independent are components? Design “style” may reflect process more than goals Variety may be desirable end in itself – Designs are not independent (e.g. in architecture, business, evolution)

8. Representation of Design Alternative representations and equivalance – Chess board problem – Number scrabble and tic-tac-toe Changing the representation may be part of design process – McGuckins example of heaters -> door “solving a problem simply means representing it so as to make the solution transparent” – Does this assume perfect knowledge?

9. Topics in the Theory of Design 1.Evaluating alternatives 2.Optimal/satisficing algorithms 3.Formal logic of design 4.Heuristic search 5.Resource allocation for search 6.Hierarchic organizations 7.Representation of problems “avoid cookbook approaches” – But where is the boundary?