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Bayes Net Collaborative AI Research Web Tool

Bayes Net Collaborative AI Research Web Tool. What. Bayesian Network = DAG that models a system Node = variable, edge = interrelation Each node has a local distribution. Paths are taken through the network following the flow of edges These paths build up fancy equations

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Bayes Net Collaborative AI Research Web Tool

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  1. Bayes NetCollaborativeAI ResearchWeb Tool

  2. What • Bayesian Network = DAG that models a system • Node = variable, edge = interrelation • Each node has a local distribution • Paths are taken through the network following the flow of edges • These paths build up fancy equations • Solving these equations reveals probabilistic relationships • We need: • A.) A graphical tool to convey and manipulate networks • B.) A way to track network evaluations over time and • C.) A way for multiple users to collaborate over multiple networks

  3. Why • Graphical representations of bayes nets can be used to teach AI • Bayesian networks can be very visual, thus lending to a hands-on experience for students • Online software can allow researchers to collaborate • Different algorithms, networks, and data histories can be shared amongst many

  4. How • Starting from a bayes net GUI tool with core functionality, we can add: • Further modeling features • Extra evaluation tools • A suite of evaluation algorithms • Simulated annealing • Sum-product • Pearl’s algorithm • Etc. • The enhanced GUI tool can be extended into a web service, sitting on top of a database containing: • User profiles • Network evaluation histories • Personalized network datasets

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