2,492 research outputs found

    Cooperative Wide Area Search Algorithm Analysis Using Sub-Region Techniques

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    Recent advances in small Unmmaned Aerial Vehicle (UAV) technology reinvigorates the need for additional research into Wide Area Search (WAS) algorithms for civilian and military applications. But due to the extremely large variability in UAV environments and design, Digital Engineering (DE) is utilized to reduce the time, cost, and energy required to advance this technology. DE also allows rapid design and evaluation of autonomous systems which utilize and support WAS algorithms. Modern WAS algorithms can be broadly classified into decision-based algorithms, statistical algorithms, and Artificial Intelligence (AI)/Machine Learning (ML) algorithms. This research continues on the work by Hatzinger and Gertsman by creating a decision-based algorithm which subdivides the search region into sub-regions known as cells, decides an optimal next cell to search, and distributes the results of the search to other cooperative search assets. Each cooperative search asset would store the following four crucial arrays in order to decide which cell to search: current estimated target density of each cell; the current number of assets in a cell; each cooperative asset’s next cell to search; and the total time any asset has been in a cell. A software-based simulation based environment, Advanced Framework for Simulation, Integration, and Modeling (AFSIM), was utilized to complete the verification process, create the test environment, and the System under Test (SUT). Additionally, the algorithm was tested against threats of various distributions to simulate clustering of targets. Finally, new Measures of Effectiveness (MOEs) are introduced from AI and ML including Precision, Recall, and F-score. The new and the original MOEs from Hatzinger and Gertsman are analyzed using Analysis of Variance (ANOVA) and covariance matrix. The results of this research show the algorithm does not have a significant effect against the original MOEs or the new MOEs which is likely due to a similar spreading of the Networked Collaborative Autonomous Munition (NCAM) as compared to Hatzinger and Gertsman. The results are negatively correlated to a decrease in target distributions standard deviation i.e. target clustering. This second result is more surprising as tighter target distributions could result in less area to search, but the NCAM continue to distribute their locations regardless of clusters identified

    CATCH ME IF YOU CAN!

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    Learning mathematics outside the classroom is not enrichment, it is at the core of empowering an individuals understanding of the subject. The three activities described in this article can all be used outside the classroom in a maths lesson. Teaching mathematical concepts in this way engages and reinforces learning. It puts the ideas learnt into a setting and allows time for those ideas to be developed without any of the maths hang-ups which can occur in the classroom. By taking maths beyond the classroom, we can more clearly illustrate the connections between the real world and what they are studying in school. In so doing students and teachers alike are enthused by the wealth of resources they have all around them in their own environments

    The Cowl - v.79 - n.18 - Feb 26, 2015

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    The Cowl - student newspaper of Providence College. Vol 79 - No. 18 - February 26, 2015. 24 pages

    Pure Monte Carlo Counterfactual Regret Minimization

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    Counterfactual Regret Minimization (CFR) and its variants are the best algorithms so far for solving large-scale incomplete information games. Building upon CFR, this paper proposes a new algorithm named Pure CFR (PCFR) for achieving better performance. PCFR can be seen as a combination of CFR and Fictitious Play (FP), inheriting the concept of counterfactual regret (value) from CFR, and using the best response strategy instead of the regret matching strategy for the next iteration. Our theoretical proof that PCFR can achieve Blackwell approachability enables PCFR's ability to combine with any CFR variant including Monte Carlo CFR (MCCFR). The resultant Pure MCCFR (PMCCFR) can significantly reduce time and space complexity. Particularly, the convergence speed of PMCCFR is at least three times more than that of MCCFR. In addition, since PMCCFR does not pass through the path of strictly dominated strategies, we developed a new warm-start algorithm inspired by the strictly dominated strategies elimination method. Consequently, the PMCCFR with new warm start algorithm can converge by two orders of magnitude faster than the CFR+ algorithm

    Spartan Daily, February 20, 2014

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    Volume 142, Issue 11https://scholarworks.sjsu.edu/spartandaily/1471/thumbnail.jp
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