II-EN: A compute cluster and software tools for Monte-Carlo methods in artificial intelligence

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The project supports acquisition of a hardware cluster and development of software frameworks to support Monte Carlo methods in articial intelligence, for tasks such as model compilation through machine learning over the results of Monte Carlo simulation, application of Monte Carlo methods to solve single-agent and multi-agent sequential decision making problems, and integration of machine learning methods into Monte Carlo search to improve real-time decision making. These software frameworks will include implementations of baseline and state-of-the-art algorithms for each task, with a goal of release with generic APIs and open-source availability to make it easy for other researchers to add new methods and to connect external simulators to the frameworks. The algorithms and tools have many important applications, including (a) optimization of forest management to jointly minimize the risk of catastrophic fires and maximize biological and economic benefits, (b)design and validation of multiagent control methods for reducing congestion in air traffic control, (c) design and validation of multiagent control methods for micro air vehicles, and (d)modeling spatio-temporal distribution of species to support management of endangered and threatened species. The hardware cluster and software frameworks will be integrated into the undergraduate and graduate curriculum at Oregon State University, as well as various outreach beyond Oregon State.