Russel Greiner

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Motivation: Modern sequencing technology now permits the sequencing of entire genomes, leading to thousands of new gene sequences in need of detailed annotation. It is too time consuming to predict the properties of each protein sequence manually and to organize the results of many prediction tools by hand. The prediction process must be automated so the(More)
Smart decision making at the tactical level is important for Artificial Intelligence (AI) agents to perform well in the domain of real-time strategy (RTS) games. This paper presents a Bayesian model that can be used to predict the outcomes of isolated battles, as well as predict what units are needed to defeat a given army. Model parameters are learned from(More)
This is a pre-print version of a manuscript that has been submitted to a refereed venue. Abstract Naïve Bayes (NB) classifiers are popular tools for predicting the labels of query instances, after being constructed from a training set. However, many training sets contain noisy data, so a user may be reluctant to blindly trust an NB classifier. TCXplain is a(More)
[3] R. Rimey and C. Brown. Control of selective perception using bayes nets and decision theory .ing to act using real-time dynamic programming. [9] Vadim Bulitko and Ilya Levner. Improving learn-ability of adaptive image interpretation systems. An optimal algorithm for approximate nearest neighbor searching fixed dimensions. [17] Dan Pelleg and Andrew(More)
Effective scheduling is a key concern for the execution of performance driven cloud applications. We have analyzed the various characteristics of deadlock, its occurrences and the strategies to overcome it. We have also discussed some of the algorithms used for scheduling and workflow in hybrid and grid computing. To overcome this, we can use(More)
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