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Real-time Strategy Game Tactical Recommendation Based on Bayesian Network

Yang Zhena, Zhang Wanpeng, Liu Hongfu
2019 Journal of Physics, Conference Series  
Bayesian model is one of the main techniques for the representation of uncertain knowledge in artificial intelligence. It is an excellent tool for dealing with random problems.  ...  This method effectively demonstrates that Bayesian networks can mimic humans to make decisions. Bayesian networks provide a stable, understandable way to generate decision simulations.  ...  Compared with the following advantages: -Bayesian networks can unearth the implications of knowledge.  ... 
doi:10.1088/1742-6596/1168/3/032018 fatcat:7vnp7fx7hbcx7edb3j46m76oey

Using Educational Robotics to Motivate Complete AI Solutions

Lloyd G. Greenwald, Donovan Artz, Yogi Mehta, Babak Shirmohammadi
2006 The AI Magazine  
Acknowledgements We thank Brian Summers for his efforts in testing the Bayesian network exercises. We thank Zachary Dodds and Jerry Weinberg for their feedback on drafts of this article.  ...  We describe here how to take advantage of a low-cost robot platform with inexpensive sensors to motivate and teach the artificial intelligence topics of neural networks and Bayesian networks.  ...  Comparing the naive Bayesian network of figure 2b and the Bayesian network of figure 2c , the students learn that the Bayesian network Localization with Particle Filtering and the RCX Localization  ... 
doi:10.1609/aimag.v27i1.1865 dblp:journals/aim/GreenwaldAMS06 fatcat:j2ysavafo5ezxiavl3i7lwmmjm

Investigating the Structure of Intelligence Using Latent Variable and Psychometric Network Modeling: A Commentary and Reanalysis

Christopher J. Schmank, Sara Anne Goring, Kristof Kovacs, Andrew R. A. Conway
2021 Journal of Intelligence  
Theories of intelligence that posit a general mental ability (general intelligence) are compatible with latent variable models.  ...  More recent approaches, such as mutualism and process overlap theory, reject the notion of general mental ability and are therefore more compatible with network models, which depict the structure of intelligence  ...  completion.  ... 
doi:10.3390/jintelligence9010008 pmid:33562895 fatcat:7p7n73hx75cwxa5wyerwiniwpi

GNGS: An Artificial Intelligent Tool for Generating and Analyzing Gene Networks from Microarray Data [chapter]

Austin H., Ching-Heng Li
2008 Tools in Artificial Intelligence  
The statistical framework of Bayesian learning, since it deals with uncertainly, is designed for domains Tools in Artificial Intelligence 36 with a large number of variables and for  ...  A screen of the system interface after the execution is completed for the K2 algorithm and the Alarm network. GNGS: An Artificial Intelligent Tool for Generating and Analyzing Gene Networks  ...  GNGS: An Artificial Intelligent Tool for Generating and Analyzing Gene Networks from Microarray Data, Tools in Artificial Intelligence, Paula Fritzsche (Ed.), ISBN: 978-953-7619-03-9, InTech, Available  ... 
doi:10.5772/6093 fatcat:kevntekksvf73akcnv5nexhere

A theory of inferred causation [chapter]

Judea Pearl, Thomas S. Verma
1995 Studies in Logic and the Foundations of Mathematics  
Kansas State University Department of Computing and Information Sciences CIS 830: Advanced Topics in Artificial Intelligence Learning Bayesian Networks: Learning Bayesian Networks: Missing Observations  ...  in Artificial Intelligence Bayesian Network Learning: Bayesian Network Learning: Related Fields and References Related Fields and References • ANNs:BBNsas Connectionist Models • GAs: BBN Inference, Learning  ... 
doi:10.1016/s0049-237x(06)80074-1 fatcat:gihsurzxdjebhkbtilxcjlhtqi


Santhanalakshmi L, Sakkaravarthi R
2017 Asian Journal of Pharmaceutical and Clinical Research  
Hence, a complete architecture is proposed to provide complete defense mechanism. This defense mechanism ensures that the threats are blocked before it invades into the cloud environment.  ...  The proficiency of the proposed system has been broke down and different studies have been made in the space of artificial intelligence, AIS, computational intelligence, and Bayesian Network.  ...  Subsequently, it is some of the time lacking to rate the vulnerabilities with settled scores. In this way, we propose dynamic Bayesian network model to include important fleeting components.  ... 
doi:10.22159/ajpcr.2017.v10s1.19602 fatcat:7oib54le6vdvjknuhg7dltwooi

Using Machine Intelligence to Prioritise Code Review Requests

Nishrith Saini, Ricardo Britto
2021 2021 IEEE/ACM 43rd International Conference on Software Engineering: Software Engineering in Practice (ICSE-SEIP)  
This thesis aims to address and solve the above issues by developing a machine intelligence-based code review prioritization tool.  ...  The tool helps to decrease the code review lead-time, along with reducing the workload on a developer while reviewing code changes.  ...  Pineapple is a machine intelligence-based code review prioritisation tool which is built using the concept of Bayesian Networks.  ... 
doi:10.1109/icse-seip52600.2021.00010 fatcat:kbm63jrakneobeinfkk2pfbdxu

Research on Intelligence Service

Dongliang Cui, Ya Feng, Chunying Qin
2020 DEStech Transactions on Engineering and Technology Research  
In order to improve timeliness and pertinence of meteorological service for agriculture, after years of confirmed research into Intelligence service, an intelligent mobile phone application was designed  ...  It is a practical intelligent service platform for agriculture meteorological.  ...  We implemented our RAID server in C, augmented with computationally Bayesian extensions. All software components were hand assembled using AT&T System V's compiler with the help of C.  ... 
doi:10.12783/dtetr/mcaee2020/35018 fatcat:ixtkeybkkzad3o6cdk6tgqbj6e


2003 International Journal of Information Technology and Decision Making  
We illustrate how the proposed hybrid bayesian network-based can be implemented in the distributed computing network environment.  ...  In this paper, we describe an approach for building a hybrid bayesian network based multi-agent system for drug crime knowledge management.  ...  case with the complete information.  ... 
doi:10.1142/s0219622003000872 fatcat:fsnylbsmm5gvbipub6ezzq7fg4

On the Gap between Epidemiological Surveillance and Preparedness [article]

Svetlana Yanushkevich, Vlad Shmerko
2020 arXiv   pre-print
A decision support system (DSS) with Computational Intelligence (CI) tools is required to bridge the gap between epidemiological model of evidence and expert group decision.  ...  These analytics are critical input for pandemics preparedness networks; however, this input is not integrated into a form suitable for decision makers or experts in preparedness.  ...  scenario of COVID-19 was described using a Bayesian network [57] . 2) Development of a complete spectrum of the risk and bias measures, including ES taxonomy updating.  ... 
arXiv:2008.03845v1 fatcat:se6o2ewsxzcbddnwbmc565fsyq

Develop Academic Question Recommender Based on Bayesian Network for Personalizing Student's Practice

Qingsheng Zhang, Di Yang, Pengjun Fang, Nannan Liu, Lu Zhang
2020 International Journal of Emerging Technologies in Learning (iJET)  
In this paper, an academic question recommender based on Bayesian network is developed for personalizing practice question sequence with tracing mastery level of student on knowledge components.  ...  It provides instructor with tools for building knowledge component network and setting question of course. It also makes student personalize practice questions of course.  ...  Next, an overview of intelligent tutoring system based on Bayesian network is discussed.  ... 
doi:10.3991/ijet.v15i18.11594 fatcat:wpt5qzucxfar5pfwzmb4r3krda

Using continuous-time Bayesian networks for standards-based diagnostics and prognostics

Logan Perreault, John Sheppard, Houston King, Liessman Sturlaugson
2014 2014 IEEE AUTOTEST  
Specifically, we introduce the continuous time Bayesian network (CTBN) as an alternative to the previously proposed dynamic Bayesian network to provide an additional model for prognostic reasoning.  ...  As with previous work, we demonstrate the feasibility and necessity of incorporating prognostic capabilities into the standard.  ...  We thank Kihoon Choi and Deepak Haste of Qualtech Systems Inc. for their collaboration with this SBIR.  ... 
doi:10.1109/autest.2014.6935145 fatcat:3hq7iyaxdbbuvgxiocklotmz4u

Efficient Predictive Uncertainty Estimators for Deep Probabilistic Models

Julissa Villanueva Llerena, Denis Deratani Maua
Our approaches shall be evaluated on challenging tasks such as image completion, multilabel classification.  ...  Deep Probabilistic Models (DPM) based on arithmetic circuits representation, such as Sum-Product Networks (SPN) and Probabilistic Sentential Decision Diagrams (PSDD), have shown competitive performance  ...  Acknowledgments This study was financed by the Brazilian Agency Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) -Finance Code 001, CNPq grants no. 303920/2016-5 and 420669/2016-7.  ... 
doi:10.1609/aaai.v34i10.7142 fatcat:lx5rrwxdnberphm6csu6jcylbm

High-throughput bayesian computing machine with reconfigurable hardware

Mingjie Lin, Ilia Lebedev, John Wawrzynek
2010 Proceedings of the 18th annual ACM/SIGDA international symposium on Field programmable gate arrays - FPGA '10  
We use reconfigurable hardware to construct a high throughput Bayesian computing machine (BCM) capable of evaluating probabilistic networks with arbitrary DAG (directed acyclic graph) topology.  ...  A Bayesian computing machine with 16 processing nodes was implemented with a Virtex-5 FPGA (XCV5LX155T-2) on a BEE3 (Berkeley Emulation Engine) platform.  ...  In addition to being high throughput, the BCM architecture is completely reusable and therefore applicable in a wide range of Bayesian network computing problems.  ... 
doi:10.1145/1723112.1723127 dblp:conf/fpga/LinLW10 fatcat:ejmbdbecg5hjzo27j3vdn3jstq

A Bayesian Network Meta-Analysis to Synthesize the Influence of Contexts of Scaffolding Use on Cognitive Outcomes in STEM Education

Brian R. Belland, Andrew E. Walker, Nam Ju Kim
2017 Review of Educational Research  
To address this gap, this study used Bayesian network meta-analysis to synthesize within-subjects (pre-post) differences resulting from scaffolding in 56 studies.  ...  Scaffolding has a consistently strong effect across student populations, STEM (science, technology, engineering, and mathematics) disciplines, and assessment levels, and a strong effect when used with  ...  This can be done with multiple treatment studies and, by extension, Bayesian network meta-analyses.  ... 
doi:10.3102/0034654317723009 pmid:29200508 pmcid:PMC5673014 fatcat:wn7vkapah5cupkdatatrb3stau
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