Co-evolutionary and Reinforcement Learning Techniques Applied to Computer Go players

The objective of this thesis is model some processes from the nature as evolution and co-evolution, and proposing some techniques that can ensure that these learning process really happens and useful to solve some complex problems as Go game. The Go game is ancient and very complex game with simple rules which still is a challenge for the Artificial Intelligence. This dissertation cover some approaches that were applied to solve this problem, proposing solve this problem using competitive and cooperative co-evolutionary learning methods and other techniques proposed by the author. To study, implement and prove these methods were used some neural networks structures, a framework free available and coded many programs. The techniques proposed were coded by the author, performed many experiments to find the best configuration to ensure that co-evolution is progressing and discussed the results. Using co-evolutionary learning processes can be observed some pathologies which could impact co-evolution progress. In this dissertation is introduced some techniques to solve pathologies as loss of gradients, cycling dynamics and forgetting. According to some authors, one solution to solve these co-evolution pathologies is introduce more diversity in populations that are evolving. In this thesis is proposed some techniques to introduce more diversity and some diversity measurements for neural networks structures to monitor diversity during co-evolution. The genotype diversity evolved were analyzed in terms of its impact to global fitness of the strategies evolved and their generalization. Additionally, it was introduced a memory mechanism in the network neural structures to reinforce some strategies in the genes of the neurons evolved with the intention that some good strategies learned are not forgotten. In this dissertation is presented some works from other authors in which cooperative and competitive co-evolution has been applied. The Go board size used in this thesis was 9x9, but can be easily escalated to more bigger boards.The author believe that programs coded and techniques introduced in this dissertation can be used for other domains.

Computational Study of the Fe(CN)(2)CO Cofactor and Its Binding to HypC Protein.

In the intricate maturation process of [NiFe]-hydrogenases, the Fe(CN)2CO cofactor is first assembled in a HypCD complex with iron coordinated by cysteines from both proteins and CO is added after ligation of cyanides. The small accessory protein HypC is known to play a role in delivering the cofactor needed for assembling the hydrogenase active site. However, the chemical nature of the Fe(CN)2CO moiety and the stability of the cofactor–HypC complex are open questions. In this work, we address geometries, properties, and the nature of bonding of all chemical species involved in formation and binding of the cofactor by means of quantum calculations. We also study the influence of environmental effects and binding to cysteines on vibrational frequencies of stretching modes of CO and CN used to detect the presence of Fe(CN)2CO. Carbon monoxide is found to be much more sensitive to sulfur binding and the polarity of the medium than cyanides. The stability of the HypC–cofactor complex is analyzed by means of molecular dynamics simulation of cofactor-free and cofactor-bound forms of HypC. The results show that HypC is stable enough to carry the cofactor, but since its binding cysteine is located at the N-terminal unstructured tail, it presents large motions in solution, which suggests the need for a guiding interaction to achieve delivery of the cofactor.

Conflicts for control of Mapuche-Pehuenche land and natural resources in the Biobío highlands

Análisis de las políticas aplicadas por el Estado chileno y sus efectos en el uso del territorio por parte de las comunidades indígenas de la zona del Alto Bíobio. Se observa que históricamente las comunidades han ido perdiendo el control de sus tierras