Coevolutionary Approaches to Generating Robust Build-Orders for Real-Time Strategy Games

We aim to find winning build-orders for Real-Time Strategy games. Real-Time Strategy games provide a variety of challenges, from short-term control to longer term planning. We focus on a longer-term planning problem; which units to build and in what order to produce the units so a player successfully defeats the opponent. Plans which address unit construction scheduling problems in Real-Time Strategy games are called build-orders. A robust build-order defeats many opponents, while a strong build-order defeats opponents quickly. However, no single build-order defeats all other build-orders, and build-orders that defeat many opponents may still lose against a specific opponent. Other researchers have only investigated generating build-orders that defeat a specific opponent, rather than finding robust, strong build-orders. Additionally, previous research has not applied coevolutionary algorithms towards generating build-orders. In contrast, our research has three main contributions towards finding robust, strong build-orders. First, we apply a coevolutionary algorithm towards finding robust build-orders. Compared to exhaustive search, a genetic algorithm finds the strongest build-orders while a coevolutionary algorithm finds more robust build-orders. Second, we show that case-injection enables coevolution to learn from specific opponents while maintaining robustness. Build-orders produced with coevolution and case-injection learn to defeat or play like the injected build-orders. Third, we show that coevolved build-orders benefit from a representation which includes branches and loops. Coevolution will utilize multiple branches and loops to create build-orders that are stronger than build-orders without loops and branches. We believe this work provides evidence that coevolutionary algorithms may be a viable approach to creating robust, strong build-orders for Real-Time Strategy games

Evolving Effective Micro Behaviors for Real-Time Strategy Games

Real-Time Strategy games have become a new frontier of artificial intelligence research. Advances in real-time strategy game AI, like with chess and checkers before, will significantly advance the state of the art in AI research. This thesis aims to investigate using heuristic search algorithms to generate effective micro behaviors in combat scenarios for real-time strategy games. Macro and micro management are two key aspects of real-time strategy games. While good macro helps a player collect more resources and build more units, good micro helps a player win skirmishes against equal numbers of opponent units or win even when outnumbered. In this research, we use influence maps and potential fields as a basis representation to evolve micro behaviors. We first compare genetic algorithms against two types of hill climbers for generating competitive unit micro management. Second, we investigated the use of case-injected genetic algorithms to quickly and reliably generate high quality micro behaviors. Then we compactly encoded micro behaviors including influence maps, potential fields, and reactive control into fourteen parameters and used genetic algorithms to search for a complete micro bot, ECSLBot. We compare the performance of our ECSLBot with two state of the art bots, UAlbertaBot and Nova, on several skirmish scenarios in a popular real-time strategy game StarCraft. The results show that the ECSLBot tuned by genetic algorithms outperforms UAlbertaBot and Nova in kiting efficiency, target selection, and fleeing. In addition, the same approach works to create competitive micro behaviors in another game SeaCraft. Using parallelized genetic algorithms to evolve parameters in SeaCraft we are able to speed up the evolutionary process from twenty one hours to nine minutes. We believe this work provides evidence that genetic algorithms and our representation may be a viable approach to creating effective micro behaviors for winning skirmishes in real-time strategy games

Complexity Theory, Adaptation, and Administrative Law

Recently, commentators have applied insights from complexity theory to legal analysis generally and to administrative law in particular. This Article focuses on one of the central problems that complexity. theory addresses, the importance and mechanisms of adaptation within complex systems. In Part I, the Article uses three features of complex adaptive systems-emergence from self-assembly, nonlinearity, and sensitivity to initial conditions-and explores the extent to which they may add value as a matter of positive analysis to the understanding of change within legal systems. In Part H, the Article focuses on three normative claims in public law scholarship that depend explicitly or implicitly on notions of adaptation: that states offer advantages over the federal government because experimentation can make them more adaptive, that federal agencies should themselves become more experimentalist using the tool of adaptive management, and that administrative agencies shou Id adopt collaborative mechanisms in policymaking. Using two analytic tools found in the complexity literature, the genetic algorithm and evolutionary game theory, the Article tests the extent to which these three normative claims are borne out

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

Ph.DDOCTOR OF PHILOSOPH

Search Based Software Project Management

This thesis investigates the application of Search Based Software Engineering (SBSE) approach in the field of Software Project Management (SPM). With SBSE approaches, a pool of candidate solutions to an SPM problem is automatically generated and gradually evolved to be increasingly more desirable. The thesis is motivated by the observation from industrial practice that it is much more helpful to the project manager to provide insightful knowledge than exact solutions. We investigate whether SBSE approaches can aid the project managers in decision making by not only providing them with desirable solutions, but also illustrating insightful “what-if” scenarios during the phases of project initiation, planning and enactment. SBSE techniques can automatically “evolve” solutions to software requirement elicitation, project staffing and scheduling problems. However, the current state-of- the-art computer-aided software project management tools remain limited in several aspects. First, software requirement engineering is plagued by problems associated with unreliable estimates. The estimations made early are assumed to be accurate, but the projects are estimated and executed in an environment filled with uncertainties that may lead to delay or disruptions. Second, software project scheduling and staffing are two closely related problems that have been studied separately by most published research in the field of computer aided software project management, but software project managers are usually confronted with the complex trade-off and correlations of scheduling and staffing. Last, full attendance of required staff is usually assumed after the staff have been assigned to the project, but the execution of a project is subject to staff absences because of sickness and turnover, for example. This thesis makes the following main contributions: (1) Introducing an automated SBSE approach to Sensitivity Analysis for requirement elicitation, which helps to achieve more accurate estimations by directing extra estimation effort towards those error-sensitive requirements and budgets. (2) Demonstrating that Co-evolutionary approaches can simultaneously co-evolve solutions for both work package sequencing and project team sizing. The proposed approach to these two interrelated problems yields better results than random and single-population evolutionary algorithms. (3) Presenting co-evolutionary approaches that can guide the project manager to anticipate and ameliorate the impact of staff absence. (4) The investigations of seven sets of real world data on software requirement and software project plans reveal general insights as well as exceptions of our approach in practise. (5) The establishment of a tool that implements the above concepts. These contributions support the thesis that automated SBSE tools can be beneficial to solution generation, and most importantly, insightful knowledge for decision making in the practise of software project management

Evolving comprehensible and scalable solvers using CGP for solving some real-world inspired problems

My original contribution to knowledge is the application of Cartesian Genetic Programming to design some scalable and human-understandable metaheuristics automatically; those find some suitable solutions for real-world NP-hard and discrete problems. This technique is thought to possess the ability to raise the generality of a problem-solving process, allowing some supervised machine learning tasks and being able to evolve non-deterministic algorithms. \\ Two extensions of Cartesian Genetic Programming are presented. Iterative My original contribution to knowledge is the application of Cartesian Genetic Programming to design some scalable and human-understandable metaheuristics automatically; those find some suitable solutions for real-world NP-hard and discrete problems. This technique is thought to possess the ability to raise the generality of a problem-solving process, allowing some supervised machine learning tasks and being able to evolve non-deterministic algorithms. \\ Two extensions of Cartesian Genetic Programming are presented. Iterative Cartesian Genetic Programming can encode loops and nested loop with their termination criteria, making susceptible to evolutionary modification the whole programming construct. This newly developed extension and its application to metaheuristics are demonstrated to discover effective solvers for NP-hard and discrete problems. This thesis also extends Cartesian Genetic Programming and Iterative Cartesian Genetic Programming to adapt a hyper-heuristic reproductive operator at the same time of exploring the automatic design space. It is demonstrated the exploration of an automated design space can be improved when specific types of active and non-active genes are mutated. \\ A series of rigorous empirical investigations demonstrate that lowering the comprehension barrier of automatically designed algorithms can help communicating and identifying an effective and ineffective pattern of primitives. The complete evolution of loops and nested loops without imposing a hard limit on the number of recursive calls is shown to broaden the automatic design space. Finally, it is argued the capability of a learning objective function to assess the scalable potential of a generated algorithm can be beneficial to a generative hyper-heuristic

Information Exchange and Conflict Resolution in Particle Swarm Optimization Variants

Single population, biologically-inspired algorithms such as Genetic Algorithm and Particle Swarm Optimization are effective tools for solving a variety of optimization problems. Like many such algorithms, however, they fall victim to the curse of dimensionality. Additionally, these algorithms often suffer from a phenomenon known as hitchhiking where improved solutions are not unequivocally better for all variables. Insofar as individuals within these populations are deemed to be competitive, one solution to both the curse of dimensionality and the problem of hitchhiking has been to introduce more cooperation. These multi-population algorithms cooperate by decomposing a problem into parts and assigning a population to each part. Factored Evolutionary Algorithms (FEA) generalize this decomposition and cooperation to any evolutionary algorithm. A key element of FEA is a global solution that provides missing information to individual populations and coordinates them. This dissertation extends FEA to the distributed case by having individual populations maintain and coordinate local solutions that maintain consensus. This Distributed FEA (DFEA) is demonstrated to perform well on a variety of problems and, sometimes, even if consensus is lost. However, DFEA fails to maintain the same semantics as FEA. To address this issue, we develop an alternative framework to the ``cooperation versus competition'' dichotomy. In this framework, information flows are modeled as a blackboard architecture. Changes in the blackboard are modeled as merge operations that require conflict resolution between existing and candidate values. Conflict resolution is handled using Pareto efficiency, which avoids hitchhiking. We apply this framework to FEA and DFEA and develop revised DFEA, which performs identically to FEA. We then apply our framework to a single population algorithm, Particle Swarm Optimization (PSO), to create Pareto Improving PSO (PI-PSO). We demonstrate that PI-PSO outperforms PSO and sometimes FEA-PSO, often with fewer individuals. Finally, we extend our information based approach by implementing parallel, distributed versions of FEA and DFEA using the Actor model. The Actor model is based on message passing, which accords well with our information-centric framework. We use validation experiments to verify that we have successfully implemented the semantics of the serial versions of FEA and DFEA

Real time tracking using nature-inspired algorithms

This thesis investigates the core difficulties in the tracking field of computer vision. The aim is to develop a suitable tuning free optimisation strategy so that a real time tracking could be achieved. The population and multi-solution based approaches have been applied first to analyse the convergence behaviours in the evolutionary test cases. The aim is to identify the core misconceptions in the manner the search characteristics of particles are defined in the literature. A general perception in the scientific community is that the particle based methods are not suitable for the real time applications. This thesis improves the convergence properties of particles by a novel scale free correlation approach. By altering the fundamental definition of a particle and by avoiding the nostalgic operations the tracking was expedited to a rate of 250 FPS. There is a reasonable amount of similarity between the tracking landscapes and the ones generated by three dimensional evolutionary test cases. Several experimental studies are conducted that compares the performances of the novel optimisation to the ones observed with the swarming methods. It is therefore concluded that the modified particle behaviour outclassed the traditional approaches by huge margins in almost every test scenario

Macroevolution: Explanation, Interpretation and Evidence

info:eu-repo/semantics/publishedVersio