1. Intelligence Musical and Otherwise

2.  Huh? Huh?

3. Webster’s definition  Intelligence:  The ability to reason

4. Webster’s definition  Reason  The capacity for intelligence

5. AI class  Learn, consciousness, sense of humor, understanding, love, creativity, think, imagination, analysis, logic, analogy, association, abstraction, reasoning, intuition, dream, adapt, aware of death, memory, skill set, appreciate, sacrifice, control, win, predict, cry, honor, compute, suicide, lie, invent, communication, converse, perceive

6. Our definition.  1. Learn  2. Reason  3. Abstract

7.  Enjoy? Enjoy?

8. GAs

9. Genetic Algorithms  Definition  a computer simulation in which a population of abstract representations (called chromosomes, genotype, or genome) of candidate solutions (called individuals, creatures, or phenotypes) to an optimization problem evolves toward better solutions.  Basics  A genetic representation of the solution domain,  A fitness function to evaluate the solution domain.  Along the way  crossover and mutation  Fitness tests  Until  a solution is found that satisfies minimum criteria

11. Karl Sims  Evolved Virtual Creatures  Not an animation  Evolved objects in motion  Encased in various media (water, air, etc.)  With gravity

12. Evolved Virtual Creatures

13. In lisp  Creating a very simple genetic algorithm for producing melodies of a certain type

14. Step 1  1. program a class of objects that have music melodies as attributes

15. Step 2  2. create a whole bunch, say 100, of instances of this object class (initial population)

16. Step 3  3. give the objects a (fitness test) based on overall interval direction, say 9 [stop when fulfilled]

17. Step 4  4. the ones closest survive (selection), the rest are discarded

18. Step 5  5. the remaining ones (mate)

19. Step 6  6. their offspring inherit (crossover) the general shape of the parents combined melodies

20. Step 7  7. maybe a (mutation) creeps in

21. Step 8  8. return to step 3

22.  1. program a class of objects that have music melodies as attributes  2. create a whole bunch, say 100, of instances of this object class (initial population)  3. give the objects a (fitness test) based on overall interval direction, say 9 [stop when fulfilled]  4. the ones closest survive (selection), the rest are discarded  5. the remaining ones (mate)  6. their offspring inherit (crossover) the general shape of the parents combined melodies  7. maybe a (mutation) creeps in  8. return to step 3

23. Variables  ;;;variables  (defvar *mutation*  (defvar *allowable-deviation*

24. Object  ;;;class of object with one attribute (a melody)  (defclass individual ()  ((melody :initarg :melody :initform nil :accessor melody))  (:documentation "Our objects in the evolutionary potboiler."))

25. Functions  ;;;functions  (defun melody  (defun fitness  (defun mate  (defun inherit  (defun mutation

26. Top level  ;;;top-level  (defun produce-crossover-melodies

27. MGC  ;;;use MGC to quickly turn into a midi file  (mgc "GA-1.mid" :ontimes '() :pitches '() :durations '() :channels '() :dynamics '())

28.  (defvar *mutation*  (defvar *allowable-deviation*  (defclass individual () ((melody :initarg :melody :initform nil :accessor melody)) ((melody :initarg :melody :initform nil :accessor melody)) (:documentation "Our objects in the evolutionary potboiler.")) (:documentation "Our objects in the evolutionary potboiler."))  (setf i-1 (make-instance 'individual))  (defun melody  (defun fitness  (defun mate  (defun inherit  (defun mutation  (defun produce-crossover-melodies  (mgc "GA-1.mid" :ontimes '() :pitches '() :durations '() :channels '() :dynamics '())

29. Interesting Music?  Example Example