![]() ![]() We extend FlipIt to a larger action-spaced game with the introduction of a new lower-cost move and generalize the model to multiplayer FlipIt. Our model is a deep neural network combined with Q-learning and is trained to maximize the defender's time of ownership of the resource. We apply our model to FlipIt (1), a two-player game in which both players, the attacker and the defender, compete for ownership of a shared resource and only receive information on the current state upon making a move. We describe a deep learning model that successfully maximizes its score using reinforcement learning in a game with incomplete and imperfect information. However, in most of these games, agents have full knowledge of the environment at all times. Reinforcement learning has shown much success in games such as chess, backgammon, and Go. ![]() International Symposium on Collaborative Technologies & Systems, Technical Committee Member and Session Chair, 2009.45th Annual Conference on Information Sciences and Systems, Session co-Chair on Compressive Sensing, 2011.MILCOM, Session Co-Chair on Compressive Sensing, 2011 & 2012.Workshop on Science of Security at London Institute of Mathematics Sciences, Co-organizer, 2012.Workshop on Event-based Media Integration and Processing (w/ ACM), Co-Chair, 2013.47th Annual Conference on Information Sciences and Systems, Symposium Chair on Deep Learning & Sparse Representation, 2013.SPIE DSS Conference, Co-Chair on Cyber Sensing Conference, 2013–2020.IEEE Global SIP, Co-Chair on Cyber Sensing Conference, 2016–2017.Complex Networks, Program Committee Member, 2018–2022.IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, Multi-Track Co-Chair, 2021.Associate Editor, IEEE Transactions on Computational Social Systems, 2018–current.if you have additional questions about the testing program. The New Hampshire Department of Health and Human Services has also established a public inquiry line. Complete information about this investigation and the testing program can be accessed from this link. At this time, there is limited data on human health effects of exposure to PFCs. Their chemical structure enables them to persist in the environment. ![]() Perfluorochemicals (PFCs) are a group of man made chemicals found in a large number of consumer and industrial products. In June 2015, results of these blood tests are being provided to those tested. This well was removed from the water supply. Routine testing of the wells at this site in 2014 found levels of one type of PFC, perfluorooctane sulfonic acid (PFOS), that were above the provisional health advisory (PHA) level set by the EPA. The NH Department of Health and Human Services (NH DHHS) has provided voluntary blood testing for perfluorochemicals to adults and children who may have consumed water at the Pease Tradeport, a former Air Force Base in the Portsmouth, NH region. Information about Perfluorochemicals (PFCs) Detected in the Pease Tradeport Water System, New Hampshire: New Hampshire Department of Environmental Services on Drinking WaterĪgency for Toxic Substance and Disease Registry Information about Arsenic in New Hampshire Well Water: ![]()
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