Supporting outdoor inquiry learning (SOIL): Teachers as designers of mobile‐assisted seamless learning von Kali, Yael, Levy, Keren‐Sarah, Levin‐Peled, Rachel, Tal, Tali

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Better environments for better AI von Keren, Sarah

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Reinforcement Learning Model-Based and Model-Free Paradigms for Optimal Control Problems in Power Systems: Comprehensive Review and Future Directions von Ginzburg-Ganz, Elinor, Segev, Itay, Balabanov, Alexander, Segev, Elior, Kaully Naveh, Sivan, Machlev, Ram, Belikov, Juri, Katzir, Liran, Keren, Sarah, Levron, Yoash

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Goal Recognition Design in Deterministic Environments von Keren, Sarah, Gal, Avigdor, Karpas, Erez

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Teachers as Designers of Technology-Enhanced Outdoor Inquiry von Levy, Keren Sarah, Kali, Yael, Tal, Tali

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The Association for the Advancement of Artificial Intelligence 2020 Workshop Program von Bang, Grace, Barash, Guy, Bea, Ryan, Cali, Jacques, Castillo‐Effen, Mauricio, Chen, Xin Cynthia, Chhaya, Niyati, Dhoopar, Rohan, Dumancic, Sebastijan, Espinoza, Huáscar, Farchi, Eitan, Fioretto, Ferdinando, Fuentetaja, Raquel, Galley, Michel, Geib, Christopher, Hernández‐Orallo, José, Huang, Xiaowei, Keren, Sarah, Kim, Seokhwan, Jaidka, Kokil, Liu, Xiaomo, Lu, Tyler, Ma, Zhiqiang, Mallah, Richard, McDermid, John, Michalowski, Martin, Mirsky, Reuth, Héigeartaigh, Seán Ó, Ramachandran, Deepak, Segovia‐Aguas, Javier, Shaban‐Nejad, Arash, Shehory, Onn, Shwartz, Vered, Srivastava, Siddharth, Talamadupula, Kartik, Tang, Jian, Zhang, Dell, Zhang, Jian

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Reports of the Workshops of the 32nd AAAI Conference on Artificial Intelligence von Bouchard, Bruno, Bouchard, Kevin, Brown, Noam, Chhaya, Niyati, Farchi, Eitan, Gaboury, Sebastien, Geib, Christopher, Gyrard, Amelie, Jaidka, Kokil, Keren, Sarah, Khardon, Roni, Kordjamshidi, Parisa, Martinez, David, Mattei, Nicholas, Michalowski, Martin, Mirsky, Reuth, Osborn, Joseph, Sahin, Cem, Shaban‐Nejad, Arash, Shehory, Onn, Sheth, Amit, Shimshoni, Ilan, Shrobe, Howie, Sinha, Arunesh, Sinha, Atanu R., Srivastava, Biplav, Streilein, William, Theocharous, Georgios, Venable, K. Brent, Wagner, Neal, Zamansky, Anna

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Reports of the Workshops Held at the 2019 AAAI Conference on Artificial Intelligence von Barash, Guy, Castillo‐Effen, Mauricio, Chhaya, Niyati, Clark, Peter, Espinoza, Huáscar, Farchi, Eitan, Geib, Christopher, Gundersen, Odd Erik, hÉigeartaig, Seán Ó, Hernández‐Orallo, José, Hori, Chiori, Huang, Xiaowei, Jaidka, Kokil, Kapanipathi, Pavan, Keren, Sarah, Kim, Seokhwan, Lanctot, Marc, Lange, Danny, Martinez, David, Mattar, Marwan, Mausam, McAuley, Julian, Michalowski, Martin, Mirsky, Reuth, Mottaghi, Roozbeh, Osborn, Joseph C., Pérolat, Julien, Schmid, Martin, Shaban‐Nejad, Arash, Shehory, Onn, Srivastava, Biplav, Streilein, William, Talamadupula, Kartik, Togelius, Julian, Yoshino, Koichiro, Zhang, Quanshi, Zitouni, Imed

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Reducing Human-Robot Goal State Divergence with Environment Design von Sikes, Kelsey, Keren, Sarah, Sreedharan, Sarath

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Selectively Sharing Experiences Improves Multi-Agent Reinforcement Learning von Gerstgrasser, Matthias, Danino, Tom, Keren, Sarah

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Value of Assistance for Mobile Agents von Amuzig, Adi, Dovrat, David, Keren, Sarah

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Reports of the Workshops of the 31st AAAI Conference on Artificial Intelligence von Anderson, Monica, Barták, Roman, Brownstein, John S., Buckeridge, David L., Eldardiry, Hoda, Geib, Christopher, Gini, Maria, Isaksen, Aaron, Keren, Sarah, Laddaga, Robert, Lisy, Viliam, Martin, Rodney, Martinez, David, Michalowski, Martin, Michael, Loizos, Mirsky, Reuth, Nguyen, Thanh, Paul, Michael J., Pontelli, Enrico, Sanner, Scott, Shaban‐Nejad, Arash, Sinha, Arunesh, Sohrabi, Shirin, Sricharan, Kumar, Srivastava, Biplav, Stefik, Mark, Streilein, William, Sturtevant, Nathan, Talamadupula, Kartik, Thielscher, Michael, Togelius, Julian, Tran, Son Cao, Tran‐Thanh, Long, Wagner, Neal, Wallace, Byron C., Wilk, Szymon, Zhu, Jichen

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Multi-Agent Reinforcement Learning for Energy Networks: Computational Challenges, Progress and Open Problems von Keren, Sarah, Essayeh, Chaimaa, Albrecht, Stefano V, Morstyn, Thomas

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Value of Assistance for Grasping von Masarwy, Mohammad, Goshen, Yuval, Dovrat, David, Keren, Sarah

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Contextual Pre-planning on Reward Machine Abstractions for Enhanced Transfer in Deep Reinforcement Learning von Azran, Guy, Danesh, Mohamad H, Albrecht, Stefano V, Keren, Sarah

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Data-Driven Goal Recognition Design for General Behavioral Agents von Kasumba, Robert, Yu, Guanghui, Ho, Chien-Ju, Keren, Sarah, Yeoh, William

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Selectively Sharing Experiences Improves Multi-Agent Reinforcement Learning von Gerstgrasser, Matthias, Danino, Tom, Keren, Sarah

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Reducing Human-Robot Goal State Divergence with Environment Design von Sikes, Kelsey, Keren, Sarah, Sreedharan, Sarath

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Collaboration Promotes Group Resilience in Multi-Agent AI von Keren, Sarah, Gerstgrasser, Matthias, Abu, Ofir, Rosenschein, Jeffrey

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Pivotal role of Protease‐activated receptor‐2 in bleomycin‐induced pulmonary fibrosis von Borensztajn, Keren Sarah, Bresser, Paul, Thusen, Jan, Loos, Chris, Blink, Bernt, Bakker, Michael, Daalhuisen, Joost, Groot, Angelique, Peppelenbosch, Maikel, Spek, Arnold

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