We evolve tactical control for entity groups in a naval real-time strategy game. Since tactical maneuvering involves spatial reasoning, our evolutionary algorithm evolves a set of influence maps that help specify an e...
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We evolve tactical control for entity groups in a naval real-time strategy game. Since tactical maneuvering involves spatial reasoning, our evolutionary algorithm evolves a set of influence maps that help specify an entity's spatial objectives. The entity then uses the A* route finding algorithm to generate waypoints according to the influence map, and follows them to achieve spatial objectives. Using this representation, our evolutionary algorithm quickly evolves increasingly better capture-the-flag tactics on three increasingly difficult maps. These preliminary results indicate (1) the usefulness of our particular influence map encoding for representing spatially resolved tactics and (2) the potential for using co-evolution to generate increasingly complex and competent tactics in our game. More generally, this work represents another step in our ongoing effort to investigate the co-evolution of competent game players in a real-time, continuous, environment that does not assume complete knowledge of the game state.
The Fourth IET International Conference on Intelligent Environments was held July 21- 22 at the University of Washington campus in Seattle, Washington. The general chairs were Diane Cook of Washington State University...
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Most of the online courses nowadays are offered in asynchronous mode, enabling students with the freedom of time and distance. Nonetheless, the drop-out rate for online courses is high. With online synchronous instruc...
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In this article, we survey the history of studies of computational creativity, following the development of the International Conference on Computational Creativity pom its beginnings, a decade ago, in two parallel wo...
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Metaschedulers can distribute parts of a bag-of-tasks (BoT) application among various resource providers in order to speed up its execution. When providers cannot disclose private information such as their load and co...
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Metaschedulers can distribute parts of a bag-of-tasks (BoT) application among various resource providers in order to speed up its execution. When providers cannot disclose private information such as their load and computing power, which are usually heterogeneous, the metascheduler needs to make blind scheduling decisions. We propose three policies for composing resource offers to schedule deadline-constrained BoT applications. Offers act as a mechanism in which resource providers expose their interest in executing an entire BoT or only part of it without revealing their load and total computing power. We also evaluate the amount of information resource providers need to expose to the metascheduler and its impact on the scheduling. Our main findings are: (i) offer-based scheduling produces less delay for jobs that cannot meet deadlines in comparison to scheduling based on load availability (i.e. free time slots); thus it is possible to keep providers' load private when scheduling multi-site BoTs; and (ii) if providers publish their total computing power they can have more local jobs meeting deadlines.
Scientific computing often requires the availability of a massive number of computers for performing large scale experiments. Traditionally, these needs have been addressed by using high-performance computing solution...
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Scientific computing often requires the availability of a massive number of computers for performing large scale experiments. Traditionally, these needs have been addressed by using high-performance computing solutions and installed facilities such as clusters and super computers, which are difficult to setup, maintain, and operate. Cloud computing provides scientists with a completely new model of utilizing the computing infrastructure. Compute resources, storage resources, as well as applications, can be dynamically provisioned (and integrated within the existing infrastructure) on a pay per use basis. These resources can be released when they are no more needed. Such services are often offered within the context of a service level agreement (SLA), which ensure the desired quality of service (QoS). Aneka, an enterprise cloud computing solution, harnesses the power of compute resources by relying on private and public clouds and delivers to users the desired QoS. Its flexible and service based infrastructure supports multiple programming paradigms that make Aneka address a variety of different scenarios: from finance applications to computational science. As examples of scientific computing in the cloud, we present a preliminary case study on using Aneka for the classification of gene expression data and the execution of fMRI brain imaging workflow.
A robust and fast method to assess the validity of a motor unit potential train (MUPT) obtained by decomposing a needle-detected EMG signal is proposed. This method determines whether a MUPT represents the firings of ...
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A robust and fast method to assess the validity of a motor unit potential train (MUPT) obtained by decomposing a needle-detected EMG signal is proposed. This method determines whether a MUPT represents the firings of a single motor unit (MU) or the merged activity of more than one MU, and if is a single train it identifies whether the estimated levels of missed and false classification errors in the MUPT are acceptable. Two supervised classifiers, the Single/Merged classifier (SMC) and the Error Rate classifier (ERC), and a linear model for estimating the level of missed classification error have been developed for this objective. Experimental results using simulated data show that the accuracy of the SMC and the ERC in correctly categorizing a train is 99% and %84 respectively.
In this paper,the rank stability radius problem is proposed for a real matrix under structured scalar perturbations and some interesting results are achieved based on polynomial *** addition,a computable formula and a...
In this paper,the rank stability radius problem is proposed for a real matrix under structured scalar perturbations and some interesting results are achieved based on polynomial *** addition,a computable formula and a two-step procedure are obtained which nicely solves the problem in this simple ***,these results on rank stability radius are used to estimate the stability robustness of descriptor systems,and for a special class of symmetric descriptor systems,the rank stability radius is proved to be equal to the system stability radius.
The main objective of this experience lies on the development of positive learning aptitudes on engineering students. The experience presented in this paper has been carried out with the help of the problem-based lear...
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The main objective of this experience lies on the development of positive learning aptitudes on engineering students. The experience presented in this paper has been carried out with the help of the problem-based learning (PBL) methodology. Solutions of medium-high complexity problems by students make them work on the development of different skills. The teaching model turns into a significant and autonomous learning model where students are conscious of their compromise with this process (learning). The obtained results are a starting point to other authors willing to use active learning methodologies within the framework of engineering degrees. Both lecturers and students involved in the experience have done a positive assessment of the methodology.
In this paper, a reputation-based Grid workflow scheduling algorithm is proposed to counter the effect of inherent unreliability and temporal characteristics of computing resources in large scale, decentralized Grid o...
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In this paper, a reputation-based Grid workflow scheduling algorithm is proposed to counter the effect of inherent unreliability and temporal characteristics of computing resources in large scale, decentralized Grid overlays. The proposed approach builds upon structured peer-to-peer indexing and overlay networking techniques to create a scalable wide-area networking of Grid sites for supporting dependable scheduling of applications. The scheduling algorithm considers reliability of a Grid resource as a statistical property, which is globally computed in the decentralized Grid overlay based on dynamic feedbacks or reputation scores assigned by individual service consumers (Grid Resource Brokers). The proposed algorithm can dynamically adapt to changing resource conditions and offer significant performance gains as compared to traditional approaches in the event of unsuccessful job execution or resource failure. We evaluate and demonstrate the feasibility of our approach through an extensive trace driven simulation. The results show that our scheduling technique can reduce the makespan up to 50% and successfully isolate the failure-prone resources from the system.
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