Artificial life and artificial intelligence

1. Artificial life andartificial intelligence

2. Today’s agenda • Complex Adaptive Systems • Artificial intelligence • Artificial life • Genetic algorithms • Foundation • Genetic operators • GeneIPD

3. Complexity theory Complex adaptive systems exhibit properties that emerge from local interactions among many heterogeneous agents mutually constituting their own environment “Boids” A model of the Internet The Santa Fe Institute

4. Complex Adaptive Systems A CAS is a network exhibiting aggregate properties that emerge from primarily local interaction among many, typically heterogeneous agents mutually constituting their own environment. • Emergent properties • Large numbers of diverse agents • Local and/or selective interaction • Adaptation through selection • Endogenous, non-parametric environment

5. What is the mind? • Marionettes (ancient Greeks) • Hydraulics (Descartes) • Pulleys and gears (Industrial Revolution) • Telephone switchboard (1930’s) • Boolean logic (1940’s) • Digital computer (1960’s) • Hologram (1970’s) • Neural networks (1980’s - ?)

6. Bottom-up vs top-down approach Intelligence Life model this model these

7. Definition of life • No universally agreed definition of life. • Typical features • Self-organization • Emergence • Autonomy • Growth • Development • Reproduction • Adaptation • Responsiveness • Evolution • Metabolism

8. Artificial life • “By the middle of this century, mankind has acquired the power to extinguish life on Earth.” • “By the middle of next century, it will be able to create it. Of the two it is hard to say which places the largest responsibility on our shoulders” Chris Langton

9. Artificial life • Theoretical biology • Artifactual (man-made), not unreal • Bottom up, not top down • Synthesis, not analysis • Leverages emergence • “The ‘artificial’ in Artificial Life refers to the component parts, not the emergent processes. If the component parts are implemented correctly, the processes they support are genuine—every bit as genuine as the natural processes they imitate.” (Langton)

10. Boids • Craig Reynolds (1987) work on flocking behavior • Virtual birds with basic flight capability • 3 rules • (i) collision avoidance – avoid collisions with nearby flock-mates • (ii) velocity matching – attempt to match velocity with nearby flock-mates. • (iii) flock centering – attempt to stay close to nearby flock-mates • Each boid is a basic unit that “sees” only its nearby flock-mates and “flies” according to the 3 rules.

11. Boids (cont.)

12. Boids (cont.) • Result: boids flocked and flew as a cohesive group. When obstacles appeared in their way they spontaneously split into 2 subgroups, without central guidance, and rejoined after clearing obstruction. • Illustrate basic principles of Alife systems • Large number of simple elemental units • Units interacting with nearby neighbors with no central controller • High-level emergent phenomena from low level interactions

13. Novelty

14. Genetic algorithms • Genetic algorithms (GA’s) are an optimization technique • GA’s are based on Darwin’s theory of evolution • Genetic algorithms combine search algorithms with the genetics of nature.   • Invented by John Holland in mid 70’s Charles Darwin 1809-1882 John Holland

15. Biological terminology • Cell nucleus contain the genetic information • Genetic information is stored in the chromosomes • The chromosome is divided in parts: genes • The different settings of the genes for one property is called: allele • Every gene has an unique position on the chromosome: locus

16. Reproduction • During sexual reproduction, crossover occurs • This recombination of chromosomes become the new individual • Hence genetic information is shared between the parents in order to create new offspring • During reproduction “errors” occur: mutation

17. Natural selection • The Origin of Species: “Preservation of favourable variations and rejection of unfavourable variations.” • There are more individuals born than can survive, so there is a continuous struggle for life. • Individuals with an advantage have a greater chance to survive: survival of the fittest.

18. How does it works?

19. Why genetic algorithms? • If nature can do it, why can’t computers? • Power of evolution to solve optimization problems • Particularly well suited for hard problems where little is known about the underlying search space

20. 1/6 = 17% B f(A) = 3 A C f(B) = 1 2/6 = 33% 3/6 = 50% f(C) = 2 Selection • Main idea: better individuals get higher chance • Roulette-wheel sampling gives more fit individuals better chance:

21. 1-point crossover • Sexual reproduction is performed by mixing genes from the parents • With a certain probability, we cross over some couples. • Choose a random point on the two parents • Split parents at this crossover point • Create children by exchanging tails

22. Mutation • Occurs at the isolated gene level within a chromosome • Can result in changes, both positive and negative, in the fitness of an individual • When positive, can help increase individual’s chance of being selected to be a parent of next generation

23. A standard genetic algorithm • Start with a randomly generated population of n chromosomes (genotypes) • Calculate the fitness developed by each chromosomes in the population • Repeat the following steps until n offspring have been created: • Select a pair of parent chromosomesfrom the current population, the probability of selection being an increasing function of fitness • Crossover the pair, with probability pc (crossover rate), at one or two randomly chosen points, to produce two new offspring • Mutate the offspring at each locus with probability pm (mutation rate), and place the resulting chromosomes in the new population • Replace the current population with the new population • Go to step 2 (next generation)

24. Iterated Prisoner’s Dilemma • Cohen, Riolo, and Axelrod. 1999. “The Emergence of Social Organization in the Prisoner's Dilemma” (SFI Working Paper 99-01-002) http://www.santafe.edu/research/publications/wpabstract/199901002 • In The Evolution of Cooperation, Robert Axelrod (1984) created a computer tournament of IPD • cooperation sometimes emerges • Tit For Tat a particularly effective strategy

25. Prisoner’s Dilemma Game Column: C D C 3,3 0,5 Row: D 5,0 1,1

26. One-Step Memory Strategies Strategy = (i, p, q) i = prob. of cooperating at t = 0 p = prob. of cooperating if opponent cooperated q = prob. of cooperating if opponent defected C p Memory: C D q C D D t t-1

27. The Four Strategies

28. D D D D D D TFT meets ALLD Cumulated Payoff p=1; q=0 0 + 1 + 1 + 1 = 3 Row (TFT) i=1 C D C Column (ALLD) D i=0 1 5 + 1 + 1 + = 8 p=0; q=0 0 1 2 3 4 t

29. Payoffs for 4x4 strategies

30. Genetic encoding for IPD model • For each of the possible historical cases – encode a move. History Future Player 1 C D D C D C D D D C C D D C C C D C C Player 2 DD D C D C D D C C C C C C C D C C D Sliding window (memory depth = 3)

31. Genetic encoding for IPD (cont.) Memory depth = 2 Two-rounds memory Second phantommove D C D C D Initialphantommove C D D D C D C C D C D C D C D C D C D C D C D C D 1 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0

32. GeneIPD • Do cooperation emerge in a spatial world? • Do defection emerge in a soup? • Is this always the case? • How does the crossover and mutation rates affect the simulation?

34. Definition of life • What is life? Is “artificial” life possible? • Webster dictionary • 1a. The quality that distinguishes a vital and functional being from a dead body or purely chemical matter. • 1b. The state of a material complex or individual characterized by the capacity to perform certain functional activities including metabolism, growth and reproduction

35. Life versus Intelligence • Life is… Islands of information that persist and reproduce. • Intelligence is… The use of information to persist and reproduce.

36. Alternative Crossover Operators • Performance with 1-point crossover depends on the order that variables occur in the representation • More likely to keep together genes that are near each other • Can never keep together genes from opposite ends of string • This is known as Positional Bias • Can be exploited if we know about the structure of our problem, but this is not usually the case

37. n-point crossover • Choose n random crossover points • Split along those points • Glue parts, alternating between parents • Generalization of 1 point (still some positional bias)

38. Uniform crossover • Assign 'heads' to one parent, 'tails' to the other • Flip a coin for each gene of the first child • Make an inverse copy of the gene for the second child • Inheritance is independent of position

39. Genetic encoding for IPD (cont.) Tit-For-Tat • 1 move remembered: If CC (case 1), then C If CD (case 2), then D If DC (case 3), then C If DD (case 4), then D • Can be encoded as the string “CDCD” or “1010” • For two possible moves remembered -> 16 (4 x 4) possibilities (16 bits string) • For three possible moves remembered -> 64 (4 x 4 x 4) possibilities (64 bits string)

40. Genetic encoding for IPD (cont.) • But this is not enough, as the strategy requires results from previous games • Extra genes to encode hypothetical moves: 1 move memory: 4 + 1 = 5 bits 2 moves memory: 16 + 3 =19 bits 3 moves memory: 64 + 7 = 71 bits • Number of potential strategies for 3 moves history is in the order of 1021