• scarcity is a fact of life (land, money, fuel, time, attention)
• rationality = the task to allocate them
• economics as an example to illustrate how outer and inner environment interact
• outer environment: available technologies, behavior of other economicactors, markets or economies (e.g., cost and revenue curves)
• inner environment: system's goals and capabilities for rational adaptive behavior (e.g., profit maximization)

• the choice is:

- optimal decision for an imaginary simplified world (operations research methods)

- decisions that are "good enough" ===> "satisfycing" solutions (heuristic search)

• heuristic search makes much weaker demands on the problem structure than mathematical tools of operations research

- can cope with non-quantifiable variables

- is applicable to non-numerical as well as numerical information

• Examples of combinatorial problems:

traveling salesman problem

warehouse location problem

location of central power stations

• Characteristics of these situations:

the set of available alternatives is "given" in a certain abstract sense ---> i.e. we can define a generator guaranteed to generate all of them eventually

they are not "given" in the sense that it is practically relevant

within practical computational limits we cannot generate all the admissible alternatives

we cannot recognize the best alternative , even if we are fortunate enough to generate it early

• Operations Research:

applied to middle levels of management

example: number of keystrokes needed to do a task

problems with extending operations research methods to ill-defined problems: uncertainty, computational complexity, lack of operationality

• Heuristic Search:

applied to top management decision, involving judgment

applicable to non-quantifiable problems

example: relationship between cognitive effort and physical effort

• things evolve in response to some kind of selective force

• simple scheme of evolution:

- generate ---> produce variety (e.g. genetic mutation)

- test ---> to evaluate the newly generated forms (e.g. natural selection)

- example: see genetic programming in chapter 7

• Problems with Evolution:

- is myopic

- reaches local maxima (instead of global ones)

- moving away from a local maxima implies: going across a valley

• Design different from Evolution:

- guided

- there is a goal (question: can we design without a final goal in mind?) one can look back over a design and "clean it up"

- one can examine failures and see what went wrong

- faster than evolution (guidance, remembering previous successes and failures)

• Is Design the same as Evolution?

- "installed base" problem (Qwerty typewriter, English measurement system, FORTRAN/COBOL, .....)

- standards

- knowledge is cumulative

• see book by Don Norman "Turn Signals are the Facial Expressions of Automobiles"

• why do complex systems evolve (do they?)
• why must knowledge-based systems be able to evolve? (changing requirements - different from algorithms)
• how do they evolve? - end-user modifiability, reuse, redesign, ...
• evolutions and object-oriented architectures

• pattern recognition and decision analysis

• studies of human problem solving in cognitive science

- nature of memory

- learning of categories

- nature of schemas as prototypes

- experiential human knowledge

• powerful reasoning and search methods <---> large amounts of special case knowledge

• "The fundamental problem of understanding intelligence is not the identification of a few powerful techniques, but rather the question of how to represent large amounts of knowledge in a fashion that permits their effective use and interaction." (Goldstein and Papert, 1977)

• expert knowledge is more bounded than common-sense knowledge