Analysis of game playing agents with fingerprints

Evolutionary computation (EC) can create a vast number of strategies for playing simple games in a short time. Analysis of these strategies is typically more time-consuming than their production. As a result, analysis of strategies produced by an EC system is often lacking or restricted to the extraction of superficial summary Statistics and Probability; This thesis presents a technique for extracting a functional signature from evolved agents that play games. This signature can be used as a visualization of agent behavior in games with two moves and also provides a numerical target for clustering and other forms of automatic analysis. The fingerprint can be used to induce a similarity measure on the space of game strategies. This thesis develops fingerprints in the context of the iterated prisoner\u27s dilemma; we note that they can be computed for any two player simultaneous game with a finite set of moves. When using a clustering algorithm, the results are strongly influenced by the choice of the measure used to find the distance between or to compare the similarity of the data being clustered. The Euclidean metric, for example, rates a convex polytope as the most compact type of object and builds clusters that are contained in compact polytopes. Presented here is a general method, called multi-clustering, that compensates for the intrinsic shape of a metric or similarity measure. The method is tested on synthetic data sets that are natural for the Euclidean metric and on data sets designed to defeat k-means clustering with the Euclidean metric. Multi-clustering successfully discovers the designed cluster structure of all the synthetic data sets used with a minimum of parameter tuning. We then use multi-clustering and filtration on fingerprint data. Cellular representation is the practice of evolving a set of instructions for constructing a desired structure. This thesis presents a cellular encoding for finite state machines and specializes it to play the iterated prisoner\u27s dilemma. The impact on the character and behavior of finite state agents of using the cellular representation is investigated. For the cellular representation resented a statistically significant drop in the level of cooperation is found. Other differences in the character of the automaton generated with a direct and cellular representation are reported

INVESTIGATIONS INTO THE COGNITIVE ABILITIES OF ALTERNATE LEARNING CLASSIFIER SYSTEM ARCHITECTURES

The Learning Classifier System (LCS) and its descendant, XCS, are promising paradigms for machine learning design and implementation. Whereas LCS allows classifier payoff predictions to guide system performance, XCS focuses on payoff-prediction accuracy instead, allowing it to evolve optimal classifier sets in particular applications requiring rational thought. This research examines LCS and XCS performance in artificial situations with broad social/commercial parallels, created using the non-Markov Iterated Prisoner\u27s Dilemma (IPD) game-playing scenario, where the setting is sometimes asymmetric and where irrationality sometimes pays. This research systematically perturbs a conventional IPD-playing LCS-based agent until it results in a full-fledged XCS-based agent, contrasting the simulated behavior of each LCS variant in terms of a number of performance measures. The intent is to examine the XCS paradigm to understand how it better copes with a given situation (if it does) than the LCS perturbations studied.Experiment results indicate that the majority of the architectural differences do have a significant effect on the agents\u27 performance with respect to the performance measures used in this research. The results of these competitions indicate that while each architectural difference significantly affected its agent\u27s performance, no single architectural difference could be credited as causing XCS\u27s demonstrated superiority in evolving optimal populations. Instead, the data suggests that XCS\u27s ability to evolve optimal populations in the multiplexer and IPD problem domains result from the combined and synergistic effects of multiple architectural differences.In addition, it is demonstrated that XCS is able to reliably evolve the Optimal Population [O] against the TFT opponent. This result supports Kovacs\u27 Optimality Hypothesis in the IPD environment and is significant because it is the first demonstrated occurrence of this ability in an environment other than the multiplexer and Woods problem domains.It is therefore apparent that while XCS performs better than its LCS-based counterparts, its demonstrated superiority may not be attributed to a single architectural characteristic. Instead, XCS\u27s ability to evolve optimal classifier populations in the multiplexer problem domain and in the IPD problem domain studied in this research results from the combined and synergistic effects of multiple architectural differences

Meta-Stability of Interacting Adaptive Agents

The adaptive process can be considered as being driven by two fundamental forces: exploitation and exploration. While the explorative process may be deterministic, the resultant effect may be stochastic. Stochastic effects may also exist in the expoitative process. This thesis considers the effects of stochastic fluctuations inherent in the adaptive process on the behavioural dynamics of a population of interacting agents. It is hypothesied that in such systems, one or more attractors in the population space exist; and that transitions between these attractors can occur; either as a result of internal shocks (sampling fluctuations) or external shocks (environmental changes). It is further postulated that such transitions in the (microscopic) population space may be observable as phase transitions in the behaviour of macroscopic observables. A simple model of a stock market, driven by asexual reproduction (selection plus mutation) is put forward as a testbed. A statistical dynamics analysis of the behaviour of this market is then developed. Fixed points in the space of agent behaviours are located, and market dynamics are compared to the analytic predictions. Additionally, an analysis of the relative importance of internal shocks(sampling fluctuations) and external shocks( the stock dividend sequence) across varying population size is presented

Game theoretic modeling and analysis : A co-evolutionary, agent-based approach

Ph.DDOCTOR OF PHILOSOPH

An exploration and validation of computer modeling of evolution, natural selection, and evolutionary biology with cellular automata for secondary students.

The Evolutionary Tool Kit, a new software package, is the prototype of a concept simulator providing an environment for students to create microworlds of populations of artificial organisms. Its function is to model processes, concepts and arguments in natural selection and evolutionary biology, using either Mendelian asexual or sexual reproduction, or counterfactual systems such as \u27paint pot\u27 or blending inheritance. In this environment students can explore a conceptual What if? in evolutionary biology, test misconceptions and deepen understanding of inheritance and changes in populations. Populations can be defined either with typological, or with populational thinking, to inquire into the role and necessity of variation in natural selection. The approach is generative not tutorial. The interface is highly graphic with twenty traits set as icons that are moved onto the \u27phenotypes\u27. Activities include investigations of evolutionary theory of aging, reproductive advantage, sexual selection and mimicry. Design of the activities incorporates Howard Gardner\u27s Theory of Multiple Intelligences. Draft of a teacher and student manual are included

Co-operation, paternal care and the evolution of hominid social groups.

Humans are social animals. Human societies emerge from vast networks of cooperative interactions between many different individuals. In this respect, humans are similar to most other primates. However, human societies are unusual among primates in the number of different types of cooperative relationships that are involved. In humans, males and females form strong pair bonds within large multimale, multi-female societies in which many other cooperative relationships are also important. How and when did human social systems arise? Do males and females use different types of cooperative strategies? Under what conditions does paternal care evolve? Do males and females have different constraints, and how do these affect the types of social strategies they employ? How do factors such as environment quality and seasonality modify these strategies? This thesis seeks answers to these questions using computer simulations based on the iterated Prisoner's Dilemma. The hypotheses generated by these models are tested using data from living primates. They are then used to investigate the kinds of societies that our hominid ancestors may have lived in. The theoretical and empirical evidence presented in this thesis suggests that sex differences in the energetic cost of reproduction determine the cooperative strategies, and ultimately the types of social groups, that evolve. It is proposed that during hominid evolution female energetic costs increased greatly, in comparison to male energetic costs, due to changes in body size dimorphism, diet and brain size. A two-stage model of hominid social structure is developed. The first stage, at the transition from the australopithecines to Homo erectus, would have involved an increase in female cooperation, especially food sharing. The second stage, occurring between 500,000 and 100,000 years ago, would have involved male care giving, the formation of pair bonds and the sexual division of labour within the context of a wider cooperative network

Public good games with incentives

Public good games dienen als Modell für den Konflikt zwischen Allgemeinwohl und individuellem Vorteil: Während der Erfolg eines gemeinschaftlichen Projekts oft vom Einsatz aller Beteiligten abhängt, kann für den Einzelnen der Anreiz zum Trittbrettfahren bestehen. In dieser Dissertation untersuche ich das Zusammenspiel von Kooperation und Anreizsystemen mit Hilfe der evolutionären Spieltheorie. Es wird gezeigt, dass Belohnungen zwar individuelle Kooperation anstoßen können, dass aber Bestrafungsmöglichkeiten notwendig sind um die Zusammenarbeit aufrechtzuerhalten. Dabei liefert die individuelle Reputation der Spieler einen Anreiz, die Einhaltung von Normen zu überwachen und Abweichungen zu sanktionieren. Im Gegensatz zu früheren Studien werden Bestrafungsmechanismen jedoch nicht zur Stabilisierung von beliebigen Normen und Verhaltensvorschriften verwendet. Stattdessen werden Sanktionen gezielt dazu eingesetzt um die soziale Wohlfahrt zu verbessern. In dieser Dissertation stelle ich auch einige mathematischeWerkzeuge und methodische Konzepte vor, die bei der Untersuchung von Public good games hilfreich sind. Dazu wird die Theorie der Rollenspiele erweitert und eine modifizierte Replikatorgleichung eingeführt. Unter dieser lokalen Replikatordynamik können sich selbst dominierte Strategien durchsetzen, falls diese zu einem relativen Vorteil führen.Public good games reflect the common conflict between group interest and self interest: While collaborative projects depend on joint efforts of all participants, each individual performs best by free-riding on the others’ contributions. In this thesis I use evolutionary game dynamics to study the interplay of cooperation and incentives. I demonstrate that rewards may act as a catalyst for individual contributions, while punishment is needed to maintain mutual cooperation. In this process, reputation plays a key role: It helps to mitigate the second-order free-rider problem and prevents subjects from being spiteful. In contrast to previous studies, I do not find that punishment can promote any behaviour (as long as deviations from that norm are punished). Instead, sanctions are targeted at noncooperators only, and lead to stable cooperation. Furthermore, this thesis provides some mathematical tools for the study of public good games with incentives. It extends the theory of role games and it introduces a modified replicator dynamcis that allows to investigate the consequences of local competition. Under this local replicator dynamics, even dominated strategies may prevail if they lead to a relative payoff advantage – which can be considered as a basic model for the evolution of spite

Combating Fake News: A Gravity Well Simulation to Model Echo Chamber Formation In Social Media

Fake news has become a serious concern as distributing misinformation has become easier and more impactful. A solution is critically required. One solution is to ban fake news, but that approach could create more problems than it solves, and would also be problematic from the beginning, as it must first be identified to be banned. We initially propose a method to automatically recognize suspected fake news, and to provide news consumers with more information as to its veracity. We suggest that fake news is comprised of two components: premises and misleading content. Fake news can be condensed down to a collection of premises, which may or may not be true, and to various forms of misleading material, including biased arguments and language, misdirection, and manipulation. Misleading content can then be exposed. While valuable, this framework’s utility may be limited by artificial intelligence, which can be used to alter fake news strategies at a rate exceeding the ability to update the framework. Therefore, we propose a model for identifying echo chambers, which are widely reported to be havens for fake news producers and consumers. We simulate a social media interest group as a gravity well, through which we identify the online groups postured to become echo chambers, and thus a source for fake news consumption and replication. This echo chamber model is supported by three pillars related to the social media group: technology employed, topic explored, and confirmation bias of group members. The model is validated by modeling and analyzing 19 subreddits on the Reddit social media platform. Contributions include a working definition for fake news, a framework for recognizing fake news, a generic model for social media echo chambers including three pillars central to echo chamber formation, and a gravity well simulation for social media groups, implemented for 19 subreddits

Improving Inter-service bandwidth fairness in Wireless Mesh Networks

Includes bibliographical references.We are currently experiencing many technological advances and as a result, a lot of applications and services are developed for use in homes, offices and out in the field. In order to attract users and customers, most applications and / or services are loaded with graphics, pictures and movie clips. This unfortunately means most of these next generation services put a lot of strain on networking resources, namely bandwidth. Efficient management of bandwidth in next generation wireless network is therefore important for ensuring fairness in bandwidth allocation amongst multiple services with diverse quality of service needs. A number of algorithms have been proposed for fairness in bandwidth allocation in wireless networks, and some researchers have used game theory to model the different aspects of fairness. However, most of the existing algorithms only ensure fairness for individual requests and disregard fairness among the classes of services while some other algorithms ensure fairness for the classes of services and disregard fairness among individual requests