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1.Incentive Design for Adaptive Agents Yiling Chen School of Engineering and Applied Sciences Harvard University Cambridge, MA 02138 USA [email#160;protected] Jerry Kung School of Engineering and Applied Sciences Harvard University Cambridge, MA 02138 USA [email#160;protected] David C Parkes School of Engineering and Applied Sciences Harvard

2.Metric Learning for Reinforcement Learning Agents Matthew E Taylor, Brian Kulis, and Fei Sha Lafayette College, [email#160;protected] University of California, Berkeley, [email#160;protected] University of Southern California, [email#160;protected] ABSTRACT A key component of any reinforcement learning algorithm is the

3.Multirobot Markov Random Fields Short Paper Jesse Buttereld Dept of Computer Science Brown University Providence, RI 029121910 [email#160;protected] Odest Chadwicke Jenkins Dept of Computer Science Brown University Providence, RI 029121910 [email#160;protected] Brian Gerkey Articial Intelligence Center SRI International Menlo Park

4.Multiagent coordination for Multiple Resource Job Scheduling Kagan Tumer Oregon State University [email#160;protected] John Lawson NASA Ames Research Center [email#160;protected] ABSTRACT E cient management of largescale job processing systems is a challenging problem, particularly in the presence of multi

5.Bond, believing his use was a fair use, quickly filed a counternotice and that prompted Serendip to file the lawsuit While the dispute seems straightforward, there are several issues at play including jurisdiction, fair use and more

6.Medieval Institute Publications publishes a series of edited collections, Studies in Medieval and Early Modern Culture, and a sister monograph series, Research in Medieval and Early Modern Culture The series were originally inspired by themes drawn from the annual International Congress on Medieval Studies at Kalamazoo

7.PointBased Policy Generation for Decentralized POMDPs Feng Wu School of Computer Science Univ of Sci Tech of China Hefei, Anhui 230027 China [email#160;protected] Shlomo Zilberstein Department of Computer Science University of Massachusetts Amherst, MA 01003 USA [email#160;protected] Xiaoping Chen School of Computer Science Univ of Sci

8.Merging Example Plans into Generalized Plans for Nondeterministic Environments Siddharth Srivastava Neil Immerman Shlomo Zilberstein Deptartment of Computer Science University of Massachusetts Amherst, MA 01003 {siddharth, immerman, shlomo}@csumassedu ABSTRACT We present a new approach for nding generalized contin

9.called cooperative equilibrium CE, which takes punishment into account It is wellknown that people are willing to punish noncooperators, even at a cost to themselves see, for example, [6, 11, 9] and the references therein CE is de ned only for twoplayer games Intuitively, a strategy pro le sin a twoplayer game is a CE if, for each

10.Usage GuidelinesThe issu abortversion command allows the user to stop the ISSU process at any time before the user commits to completing the process by issuing the issu commitversion command Before any action is taken, a check is performed to ensure that both RPs are either in the run version RV or load version LV state

11.elliptical galaxies at high redshift Perhaps the best way of breaking the agemetallicity degeneracy is by looking back in time, studying galaxies at higher and higher redshifts In the 1990s this was attempted first with 4mclass telescopes, and later, with impressive success, with 810mclass telescopes and HST

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