Using Genetic Programming to Evolve Board Evaluation Functions - Evolutionary Computation, 1995., IEEE International Conference on
نویسنده
چکیده
In this paper, we employ the genetic programming paradigm to enable a computer to learn to play strategies for the ancient Egyptian boardgame Senet by evolving board evaluation functions. Formulating the problem in terms of board evaluation functions made it feasible to evaluate the fitness of game playing strategies by using tournament-style fitness evaluation. The game has elements of both strategy and chance. Our approach learns strategies which enable the computer to play consistently at a reasonably skillful level.
منابع مشابه
Evolutionary Learning of General Fuzzy Rules with Biased Evaluation Functions: Competition and Cooperation
Fuzzy rules cooperate in a Fuzzy Logic Controller (FLC) to produce the best action for a given situation. If we have a population of fuzzy rules controlling a device, and we would like to evolve the population to obtain optimal performance by Reinforcement Learning, rules should compete each other, since we would like to judge their proposals. Therefore, in this approach, cooperation and compet...
متن کاملClassifying Nucleic Acid Sub-Sequences as Introns or Exons Using Genetic Programming
An evolutionary computation technique, genetic programming, created programs that classify messenger RNA sequences into one of two classes: (1) the sequence is expressed as (part of) a protein (an exon), or (2) not expressed as protein (an intron).
متن کاملThe Firefly Machine: Online Evolware - Evolutionary Computation, 1997., IEEE International Conference on
We present the firefly machine, an evolving hardware system, demonstrating that “evolving ware,” evolware, can be attained. The system is based on the cellular programming approach, in which parallel cellular machines evolve to solve computational tasks. The firefly system operates with no reference to an external device, such as a computer that carries out genetic operators, thereby exhibiting...
متن کاملSolving Vehicle Routing Problems with Genetic Algorithms - Evolutionary Computation, 1995., IEEE International Conference on
Many transportation problems, such as the travelling salesman problem, are computationally hard but often soluble quickly, although with less certainty, by heuristic methods. Genetic algorithms fall into this category and generate results with favourable scaling behaviour. We apply a two-level genetic algorithm to an advanced transportation problem, an example of the General Pickup and Delivery...
متن کاملEditorial: Welcome To The IEEE Neural Networks Society
I WANT towelcomeyou toournewly formedsociety.On February 17, 2002, the IEEE Neural Networks Council (NNC), publisher of the IEEE TRANSACTIONS ON NEURAL NETWORKS (TNN), the IEEE TRANSACTIONS ON FUZZY SYSTEMS (TFS), and the IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION (TEC), became the IEEE Neural Networks Society (NNS). This accomplishment was made possible by the relentless efforts of our ExCo...
متن کامل