* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
Download The Complexity of Cooperation
Genomic imprinting wikipedia , lookup
Public health genomics wikipedia , lookup
Ridge (biology) wikipedia , lookup
Behavioural genetics wikipedia , lookup
Epigenetics of human development wikipedia , lookup
Minimal genome wikipedia , lookup
Designer baby wikipedia , lookup
Dual inheritance theory wikipedia , lookup
Genome evolution wikipedia , lookup
Gene expression profiling wikipedia , lookup
Genetic drift wikipedia , lookup
Heritability of IQ wikipedia , lookup
Quantitative trait locus wikipedia , lookup
Gene expression programming wikipedia , lookup
Genome (book) wikipedia , lookup
Biology and consumer behaviour wikipedia , lookup
Koinophilia wikipedia , lookup
Population genetics wikipedia , lookup
Evolving New Strategies The Evolution of Strategies in the Prisoner’s Dilemma -By Robert Axelrod Why’s and What’s of Evolution Agents are not always fully aware of there situations. Instead, they must adapt to them. Methods for adapation in nature are a combination of natural selection and mutation. The Steps in a Model of Evolution Specify the Environment in which the evolutionary process can operate. A method for simulating genetics. A test to discovery whether strategies diverge or converge in similar situations. Statisical Analysis of the computer simulation The Simulated Environment An iterated prisoner’s dilemma of known length. A determined number of generations. Adaptive agents that play against eight successful rules of Axelrod’s tournament. The Genetic Algorithm Each Agent has it’s own ‘Chromosome’. The Strategies are deterministic and based on a three turn memory. 64 Corps Genes + 6 Additional Genes to deal with the start of the game. There are 10 to the 21st combinations of C’s and D’s. The Genetic Algorithm Cont’d An initial random population is selected and run. The more successful individuals are chosen to mate at random. Crossover and mutation determine the child’s genes. The new generation replaces the old one. Results A constant population of twenty individuals meeting 151 times with each of the eight rules ~ 24,000 moves per generation. The median adaptive agent was as successful as the best rule in Axelrod’s tournament The adaptive agents act similar to TFT. A Surprising Result In 11 of 50 generations the Agents learned to exploit there opponents. In this case defecting first payed. These new strategies are not robust. Five Important traits for each agent “Don’t Rock the boat (C after RRR).” “Be provocable (D after RRS).” “Acccept an apology (C after TSR).” “Forget (C after SRR).” “Accept a rut (D after PPP).” Where can we go from here? Dock our model with Axelrod’s as a litmus test for its usefulness. Increasingly use our program to run experiments. Data analysis and documentation will be increasingly important Some of Axelrod’s Ideas for Expanding the Model Adaptive Mutation Rates. Several vs. Individual Chromosomes (Coadapation). Dominant and recessive genes. Gradual vs. Rapid Evolution. Population viscosity and mating. Speciation and ecological niches.