However, due to the small numbers of goals present in existing systems, goal management Autonomous AI systems should be aware of their own goals is a relatively simple affair. Hanheide et al. (2010) describe and be capable of independently formulating behaviour to a system similar in architecture to our own that manages address them. We would ideally like to provide an agent with just two goals, whereas the one discussed in this paper must a collection of competences that allow it to act in novel situations manage upwards of forty. As the number of goals increases, that may not be predictable at design-time. In particular, the potential for goal conflict grows. This leads to a requirement we are interested in the operation of AI systems in for more sophisticated management processes, such as complex, oversubscribed domains where there may exist a dynamic goal re-prioritisation, allowing agents to alter their variety of ways to address high-level goals by composing behaviour to meet changing operational requirements. In the behaviours to achieve a set of sub-goals taken from a larger oversubscribed problem domains we are interested in, encoding set. Our research focusses how such sub-goals might be chosen all possible operating strategies at design time may (i.e.

KGoldrunner is a puzzle-oriented platform game with dynamic elements. This paper describes Goldspinner, an automatic level generation system for KGoldrunner. Goldspinner has two parts: a genetic algorithm that generates candidate levels, and simulations that use an AI agent to attempt to solve the level from the player's perspective. Our genetic algorithm determines how "good" a candidate level is by examining many different properties of the level, all based on its static aspects. Once the genetic algorithm identifies a good candidate, simulations are performed to evaluate the dynamic aspects of the level. Levels that are statically good may not be dynamically good (or even solvable), making simulation an essential aspect of our level generation system. By carefully optimizing our genetic algorithm and simulation agent we have created an efficient system capable of generating interesting levels in real time.

Adapting game content to a particular player's needs and expertise constitutes an important aspect in game design. Most research in this direction has focused on adapting game difficultyto keep the player engaged in the game. Dynamic difficulty adjustment, however, focuses on one aspect of the gameplay experience by adjusting the content to increase ordecrease perceived challenge. In this paper, we introduce a method for automatic level generation for the platform game Super Mario Bros using grammatical evolution. The grammatical evolution-based level generator is used to generate player-adapted content by employing an adaptation mechanism as a fitness function in grammatical evolution to optimizethe player experience of three emotional states: engagement, frustration and challenge. The fitness functions used are models of player experience constructed in our previous work from crowd-sourced gameplay data collected from over 1500 game sessions.

Efficient Natural Evolution Strategies (eNES) is a novel alternative to conventional evolutionary algorithms, using the natural gradient to adapt the mutation distribution. Unlike previous methods based on natural gradients, eNES uses a fast algorithm to calculate the inverse of the exact Fisher information matrix, thus increasing both robustness and performance of its evolution gradient estimation, even in higher dimensions. Additional novel aspects of eNES include optimal fitness baselines and importance mixing (a procedure for updating the population with very few fitness evaluations). The algorithm yields competitive results on both unimodal and multimodal benchmarks.

Cultural algorithm is a kind of evolutionary algorithm inspired from societal evolution and is composed of a belief space, a population space and a protocol that enables exchange of knowledge between these sources. Knowledge created in the population space is accepted into the belief space while this collective knowledge from these sources is combined to influence the decisions of the individual agents in solving problems. Classification rules comes under descriptive knowledge discovery in data mining and are the most sought out by users since they represent highly comprehensible form of knowledge. The rules have certain properties which make them useful forms of actionable knowledge to users. The rules are evaluated using these properties namely the rule metrics. In the current study a Cultural Algorithm Toolkit for Classification Rule Mining (CAT-CRM) is proposed which allows the user to control three different set of parameters namely the evolutionary parameters, the rule parameters as well as agent parameters and hence can be used for experimenting with an evolutionary system, a rule mining system or an agent based social system. Results of experiments conducted to observe the effect of different number and type of metrics on the performance of the algorithm on bench mark data sets is reported.

The Matrix Bandwidth Minimization Problem (MBMP) seeks for a simultaneous reordering of the rows and the columns of a square matrix such that the nonzero entries are collected within a band of small width close to the main diagonal. The MBMP is a NP-complete problem, with applications in many scientific domains, linear systems, artificial intelligence, and real-life situations in industry, logistics, information recovery. The complex problems are hard to solve, that is why any attempt to improve their solutions is beneficent. Genetic algorithms and ant-based systems are Soft Computing methods used in this paper in order to solve some MBMP instances. Our approach is based on a learning agent-based model involving a local search procedure. The algorithm is compared with the classical Cuthill-McKee algorithm, and with a hybrid genetic algorithm, using several instances from Matrix Market collection. Computational experiments confirm a good performance of the proposed algorithms for the considered set of MBMP instances. On Soft Computing basis, we also propose a new theoretical Reinforcement Learning model for solving the MBMP problem.

Creating and monitoring competitive and cost-effective pay-per-click advertisement campaigns through the web-search channel is a resource demanding task in terms of expertise and effort. Assisting or even automating the work of an advertising specialist will have an unrivaled commercial value. In this paper we propose a methodology, an architecture, and a fully functional framework for semi- and fully- automated creation, monitoring, and optimization of cost-efficient pay-per-click campaigns with budget constraints. The campaign creation module generates automatically keywords based on the content of the web page to be advertised extended with corresponding ad-texts. These keywords are used to create automatically the campaigns fully equipped with the appropriate values set. The campaigns are uploaded to the auctioneer platform and start running. The optimization module focuses on the learning process from existing campaign statistics and also from applied strategies of previous periods in order to invest optimally in the next period. The objective is to maximize the performance (i.e. clicks, actions) under the current budget constraint. The fully functional prototype is experimentally evaluated on real world Google AdWords campaigns and presents a promising behavior with regards to campaign performance statistics as it outperforms systematically the competing manually maintained campaigns.

In aviation industry, free flight is a new concept which implies considering more freedom in the selection and modification of flight paths during flight time. The free flight concept allows pilots choose their own flight paths more efficient, and also plan for their flight with high performance. Although free flight has many advantages such as minimum delays and the reduction of the workload of the air traffic control centers, this concept causes many problems which one of the most important of them are conflicts between different aircrafts. Thus, Conflict Detection and Resolution (CD&R) is a major challenge in air traffic management. In this paper, we presented a model for CD&R between aircrafts in air traffic management using Graph Coloring Problem (GCP) method. In fact, we mapped the congestion area to a corresponding graph, and then addressed to find a reliable and optimal coloring for this graph using one of the new evolutionary algorithms known as Imperialist Competitive Algorithm (ICA) to solve the conflicts. Using ICA for solving GCP is a new method.

We contribute to the theoretical understanding of evolutionary algorithms and carry out a parameterized analysis of evolutionary algorithms for the Euclidean traveling salesperson problem (Euclidean TSP). We exploit structural properties related to the optimization process of evolutionary algorithms for this problem and use them to bound the runtime of evolutionary algorithms. Our analysis studies the runtime in dependence of the number of inner points $k$ and shows that simple evolutionary algorithms solve the Euclidean TSP in expected time O( n k(2 k -1)!).  Moreover, we show that, under reasonable geometric constraints, a locally optimal 2-opt tour can be found by randomized local search in expected time $O( n 2 k k !).

The graphics processing unit (GPU) has emerged as a powerful and cost effective processor for general performance computing. GPUs are capable of an order of magnitude more floating-point operations per second as compared to modern central processing units (CPUs), and thus provide a great deal of promise for computationally intensive statistical applications. Fitting complex statistical models with a large number of parameters and/or for large datasets is often very computationally expensive. In this study, we focus on Gaussian process (GP) models -- statistical models commonly used for emulating expensive computer simulators. We demonstrate that the computational cost of implementing GP models can be significantly reduced by using a CPU+GPU heterogeneous computing system over an analogous implementation on a traditional computing system with no GPU acceleration. Our small study suggests that GP models are fertile ground for further implementation on CPU+GPU systems.