Automatic Categorization of Human-coded and Evolved CoreWar Warriors

Abstract. CoreWar is a computer simulation devised in the 1980s where programs loaded into a virtual memory array compete for control over the virtual machine. These programs are written in a special-purpose assembly language called Redcode and referred to as warriors. A great variety of environments and battle strategies have emerged over the years, leading to formation of different warrior types. This paper deals with the problem of automatic warrior categorization, presenting results of classification based on several approaches to warrior representation, and offering insight into ambiguities concerning the identification of strategic classes. Over 600 human-coded warriors were annotated, forming a training set for classification. Several major classifiers were used, SVMs proving to be the most reliable, reaching accuracy of 84%. Classification of an evolved warrior set using the trained classifiers was also conducted. The obtained results proved helpful in outlining the issues with both automatic and manual Redcode program categorization

Evolving Teams of Cooperating Agents for Real-Time Strategy Game

We apply gene expression programing to evolve a player for a real-time strategy (RTS) video game. The paper describes the game, evolutionary encoding of strategies and the technical implementation of experimental framework. In the experimental part, we compare two setups that differ with respect to the used approach of task decomposition. One of the setups turns out to be able to evolve an effective strategy, while the other leads to more sophisticated yet inferior solutions. We discuss both the quantitative results and the behavioral patterns observed in the evolved strategies

Design and Characterisation of a Novel Artificial Life System Incorporating Hierarchical Selection

In this thesis, a minimal artificial chemistry system is presented, which is inspired by the RNA World hypothesis and is loosely based on Holland's Learning Classier Systems. The Molecular Classier System (MCS) takes a bottom-up, individual-based approach to building artificial bio-chemical networks. The MCS has been developed to demonstrate the effects of hierarchical selection. Hierarchical selection appears to have been critical for the evolution of complexity in life as we know it yet, to date, no computational artificial life system has investigated the viability of using hierarchical selection as a mechanism for achieving qualitatively similar results. Hierarchy in MCS is enforced by constraining artificial molecules, which are modeled as individuals, to exist within externally provided containers - protocells. This research is focused on the period of time surrounding the conjectured first Major Transition - from individual replicating molecules to populations of molecules existing within cells. Protocells can be thought of as simplified versions of contemporary biological cells. Molecular replication within these protocells causes them to grow until they undergo a process of binary fission. Darwinian selection is continuously and independently applied at both the molecular level and the protocell level. Experimental results are presented which display the phenomenon of selectional stalemate where the selectional pressures are applied in opposite directions such that they meet in the middle. The work culminates with the presentation of a stable artificial protocell system which is capable of demonstrating ongoing evolution at the protocell level via hierarchical selection of molecular species. Supplementary results are presented in the Appendix material as a set of experiments where selectional pressure is applied at the protocell level in a manner that indirectly favours particular artificial bio-chemical networks at the molecular level. It is shown that a molecular trait which serves no useful purpose to the molecules when they are not contained within protocells is exploited for the benefit of the collective once the molecules are constrained to live together. It is further shown that through the mechanism of hierarchical selection, the second-order effects of this molecular trait can be used by evolution to distinguish between protocells which contain desirable networks, and those that do not. A treatment of the computational potential of such a mechanism is presented with special attention given to the idea that such computation may indeed form the basis for the later evolution of the complicated Cell Signaling Pathways that are exhibited by modern cells

Conocimiento experto y minería de datos sobre reportes de firewall aplicado a la detección de Amenazas Persistentes Avanzadas = Expert knowledge and data mining over firewall reports to detect Advanced Persistent Threats

189 p.En este trabajo se propone una metodología basada en conocimiento experto para construir un sistema inteligente capaz de detectar comportamientos anómalos y clasificar, en su caso, Amenazas Persistentes Avanzadas (APTs). Un experto puede intuir si existe peligro real para una infraestructura TIC a partir de la información contenida en los registros de log del firewall que controla su tráfico de entrada/salida. Este conocimiento experto se puede modelar, y con la ayuda de minería de datos y de sistemas de aprendizaje automático se puede construir una herramienta capaz de identificar tráfico malicioso. Para seleccionar el sistema de aprendizaje automático más adecuado, la información de tráfico real se ha completado con datos sintéticos, a fin de representar diferentes proporciones de actividad anómala en el conjunto de datos de tráfico. Esta metodología se ha aplicado a un entorno de tráfico real, y el sistema inteligente desarrollado muestra unas tasas de comportamiento aceptables en la detección de ataques por APT