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Satinder P. Singh :
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Charles Lee Isbell Jr. , Michael J. Kearns , David P. Kormann , Satinder P. Singh , Peter Stone Cobot in LambdaMOO: A Social Statistics Agent. [Citation Graph (0, 0)][DBLP ] AAAI/IAAI, 2000, pp:36-41 [Conf ] Michael R. James , Satinder P. Singh Planning in Models that Combine Memory with Predictive Representations of State. [Citation Graph (0, 0)][DBLP ] AAAI, 2005, pp:987-992 [Conf ] Michael J. Kearns , Charles Lee Isbell Jr. , Satinder P. Singh , Diane J. Litman , Jessica Howe CobotDS: A Spoken Dialogue System for Chat. [Citation Graph (0, 0)][DBLP ] AAAI/IAAI, 2002, pp:425-430 [Conf ] Satinder P. Singh Reinforcement Learning with a Hierarchy of Abstract Models. [Citation Graph (0, 0)][DBLP ] AAAI, 1992, pp:202-207 [Conf ] Satinder P. Singh Reinforcement Learning Algorithms for Average-Payoff Markovian Decision Processes. [Citation Graph (0, 0)][DBLP ] AAAI, 1994, pp:700-705 [Conf ] Satinder P. Singh , Michael J. Kearns , Diane J. Litman , Marilyn A. Walker Empirical Evaluation of a Reinforcement Learning Spoken Dialogue System. [Citation Graph (0, 0)][DBLP ] AAAI/IAAI, 2000, pp:645-651 [Conf ] Vishal Soni , Satinder P. Singh Using Homomorphisms to Transfer Options across Continuous Reinforcement Learning Domains. [Citation Graph (0, 0)][DBLP ] AAAI, 2006, pp:- [Conf ] David Wingate , Satinder P. Singh Mixtures of Predictive Linear Gaussian Models for Nonlinear, Stochastic Dynamical Systems. [Citation Graph (0, 0)][DBLP ] AAAI, 2006, pp:- [Conf ] Charles Lee Isbell Jr. , Christian R. Shelton , Michael J. Kearns , Satinder P. Singh , Peter Stone A social reinforcement learning agent. [Citation Graph (0, 0)][DBLP ] Agents, 2001, pp:377-384 [Conf ] Peter Stone , Michael L. Littman , Satinder P. Singh , Michael J. Kearns ATTac-2000: an adaptive autonomous bidding agent. [Citation Graph (0, 0)][DBLP ] Agents, 2001, pp:238-245 [Conf ] Christopher Kiekintveld , Michael P. Wellman , Satinder P. Singh , Joshua Estelle , Yevgeniy Vorobeychik , Vishal Soni , Matthew R. Rudary Distributed Feedback Control for Decision Making on Supply Chains. [Citation Graph (0, 0)][DBLP ] ICAPS, 2004, pp:384-392 [Conf ] Joshua Estelle , Yevgeniy Vorobeychik , Michael P. Wellman , Satinder P. Singh , Christopher Kiekintveld , Vishal Soni Strategic Interactions in the TAC 2003 Supply Chain Tournament. [Citation Graph (0, 0)][DBLP ] Computers and Games, 2004, pp:316-331 [Conf ] Diane J. Litman , Michael S. Kearns , Satinder P. Singh , Marilyn A. Walker Automatic Optimization of Dialogue Management. [Citation Graph (0, 0)][DBLP ] COLING, 2000, pp:502-508 [Conf ] Michael J. Kearns , Satinder P. Singh Bias-Variance Error Bounds for Temporal Difference Updates. [Citation Graph (0, 0)][DBLP ] COLT, 2000, pp:142-147 [Conf ] Lawrence K. Saul , Satinder P. Singh Markov Decision Processes in Large State Spaces. [Citation Graph (0, 0)][DBLP ] COLT, 1995, pp:281-288 [Conf ] Lawrence K. Saul , Satinder P. Singh Learning Curve Bounds for a Markov Decision Process with Undiscounted Rewards. [Citation Graph (0, 0)][DBLP ] COLT, 1996, pp:147-156 [Conf ] Doina Precup , Richard S. Sutton , Satinder P. Singh Theoretical Results on Reinforcement Learning with Temporally Abstract Options. [Citation Graph (0, 0)][DBLP ] ECML, 1998, pp:382-393 [Conf ] Michael R. James , Satinder P. Singh Learning and discovery of predictive state representations in dynamical systems with reset. [Citation Graph (0, 0)][DBLP ] ICML, 2004, pp:- [Conf ] Michael J. Kearns , Satinder P. Singh Near-Optimal Reinforcement Learning in Polynominal Time. [Citation Graph (0, 0)][DBLP ] ICML, 1998, pp:260-268 [Conf ] John Loch , Satinder P. Singh Using Eligibility Traces to Find the Best Memoryless Policy in Partially Observable Markov Decision Processes. [Citation Graph (0, 0)][DBLP ] ICML, 1998, pp:323-331 [Conf ] Kary Myers , Michael J. Kearns , Satinder P. Singh , Marilyn A. Walker A Boosting Approach to Topic Spotting on Subdialogues. [Citation Graph (0, 0)][DBLP ] ICML, 2000, pp:655-662 [Conf ] Doina Precup , Richard S. Sutton , Satinder P. Singh Eligibility Traces for Off-Policy Policy Evaluation. [Citation Graph (0, 0)][DBLP ] ICML, 2000, pp:759-766 [Conf ] Matthew R. Rudary , Satinder P. Singh Predictive linear-Gaussian models of controlled stochastic dynamical systems. [Citation Graph (0, 0)][DBLP ] ICML, 2006, pp:777-784 [Conf ] Matthew R. Rudary , Satinder P. Singh , Martha E. Pollack Adaptive cognitive orthotics: combining reinforcement learning and constraint-based temporal reasoning. [Citation Graph (0, 0)][DBLP ] ICML, 2004, pp:- [Conf ] Satinder P. Singh Transfer of Learning Across Compositions of Sequentail Tasks. [Citation Graph (0, 0)][DBLP ] ML, 1991, pp:348-352 [Conf ] Satinder P. Singh Scaling Reinforcement Learning Algorithms by Learning Variable Temporal Resolution Models. [Citation Graph (0, 0)][DBLP ] ML, 1992, pp:406-415 [Conf ] Satinder P. Singh , Tommi Jaakkola , Michael I. Jordan Learning Without State-Estimation in Partially Observable Markovian Decision Processes. [Citation Graph (0, 0)][DBLP ] ICML, 1994, pp:284-292 [Conf ] Satinder P. Singh , Michael L. Littman , Nicholas K. Jong , David Pardoe , Peter Stone Learning Predictive State Representations. [Citation Graph (0, 0)][DBLP ] ICML, 2003, pp:712-719 [Conf ] Richard S. Sutton , Doina Precup , Satinder P. Singh Intra-Option Learning about Temporally Abstract Actions. [Citation Graph (0, 0)][DBLP ] ICML, 1998, pp:556-564 [Conf ] David Wingate , Satinder P. Singh Kernel Predictive Linear Gaussian models for nonlinear stochastic dynamical systems. [Citation Graph (0, 0)][DBLP ] ICML, 2006, pp:1017-1024 [Conf ] Britton Wolfe , Michael R. James , Satinder P. Singh Learning predictive state representations in dynamical systems without reset. [Citation Graph (0, 0)][DBLP ] ICML, 2005, pp:980-987 [Conf ] Britton Wolfe , Satinder P. Singh Predictive state representations with options. [Citation Graph (0, 0)][DBLP ] ICML, 2006, pp:1025-1032 [Conf ] Michael R. James , Britton Wolfe , Satinder P. Singh Combining Memory and Landmarks with Predictive State Representations. [Citation Graph (0, 0)][DBLP ] IJCAI, 2005, pp:734-739 [Conf ] Yevgeniy Vorobeychik , Michael P. Wellman , Satinder P. Singh Learning Payoff Functions in Infinite Games. [Citation Graph (0, 0)][DBLP ] IJCAI, 2005, pp:977-982 [Conf ] David Wingate , Vishal Soni , Britton Wolfe , Satinder P. Singh Relational Knowledge with Predictive State Representations. [Citation Graph (0, 0)][DBLP ] IJCAI, 2007, pp:2035-2040 [Conf ] N. E. Berthier , Satinder P. Singh , Andrew G. Barto , James C. Houk A Cortico-Cerebellar Model that Learns to Generate Distributed Motor Commands to Control a Kinematic Arm. [Citation Graph (0, 0)][DBLP ] NIPS, 1991, pp:611-618 [Conf ] Timothy X. Brown , Hui Tong , Satinder P. Singh Optimizing Admission Control while Ensuring Quality of Service in Multimedia Networks via Reinforcement Learning. [Citation Graph (0, 0)][DBLP ] NIPS, 1998, pp:982-988 [Conf ] David A. Cohn , Satinder P. Singh Predicting Lifetimes in Dynamically Allocated Memory. [Citation Graph (0, 0)][DBLP ] NIPS, 1996, pp:939-945 [Conf ] Peter Dayan , Satinder P. Singh Improving Policies without Measuring Merits. [Citation Graph (0, 0)][DBLP ] NIPS, 1995, pp:1059-1065 [Conf ] Charles Lee Isbell Jr. , Christian R. Shelton , Michael J. Kearns , Satinder P. Singh , Peter Stone Cobot: A Social Reinforcement Learning Agent. [Citation Graph (0, 0)][DBLP ] NIPS, 2001, pp:1393-1400 [Conf ] Tommi Jaakkola , Michael I. Jordan , Satinder P. Singh Convergence of Stochastic Iterative Dynamic Programming Algorithms. [Citation Graph (0, 0)][DBLP ] NIPS, 1993, pp:703-710 [Conf ] Tommi Jaakkola , Satinder P. Singh , Michael I. Jordan Reinforcement Learning Algorithm for Partially Observable Markov Decision Problems. [Citation Graph (0, 0)][DBLP ] NIPS, 1994, pp:345-352 [Conf ] Michael J. Kearns , Satinder P. Singh Finite-Sample Convergence Rates for Q-Learning and Indirect Algorithms. [Citation Graph (0, 0)][DBLP ] NIPS, 1998, pp:996-1002 [Conf ] Michael L. Littman , Michael J. Kearns , Satinder P. Singh An Efficient, Exact Algorithm for Solving Tree-Structured Graphical Games. [Citation Graph (0, 0)][DBLP ] NIPS, 2001, pp:817-823 [Conf ] Michael L. Littman , Richard S. Sutton , Satinder P. Singh Predictive Representations of State. [Citation Graph (0, 0)][DBLP ] NIPS, 2001, pp:1555-1561 [Conf ] David C. Parkes , Satinder P. Singh An MDP-Based Approach to Online Mechanism Design. [Citation Graph (0, 0)][DBLP ] NIPS, 2003, pp:- [Conf ] David C. Parkes , Satinder P. Singh , Dimah Yanovsky Approximately Efficient Online Mechanism Design. [Citation Graph (0, 0)][DBLP ] NIPS, 2004, pp:- [Conf ] Doina Precup , Richard S. Sutton , Cosmin Paduraru , Anna Koop , Satinder P. Singh Off-policy Learning with Options and Recognizers. [Citation Graph (0, 0)][DBLP ] NIPS, 2005, pp:- [Conf ] Matthew R. Rudary , Satinder P. Singh A Nonlinear Predictive State Representation. [Citation Graph (0, 0)][DBLP ] NIPS, 2003, pp:- [Conf ] Satinder P. Singh The Efficient Learning of Multiple Task Sequences. [Citation Graph (0, 0)][DBLP ] NIPS, 1991, pp:251-258 [Conf ] Satinder P. Singh , Dimitri P. Bertsekas Reinforcement Learning for Dynamic Channel Allocation in Cellular Telephone Systems. [Citation Graph (0, 0)][DBLP ] NIPS, 1996, pp:974-980 [Conf ] Satinder P. Singh , Andrew G. Barto , Nuttapong Chentanez Intrinsically Motivated Reinforcement Learning. [Citation Graph (0, 0)][DBLP ] NIPS, 2004, pp:- [Conf ] Satinder P. Singh , Andrew G. Barto , Roderic A. Grupen , Christopher I. Connolly Robust Reinforcement Learning in Motion Planning. [Citation Graph (0, 0)][DBLP ] NIPS, 1993, pp:655-662 [Conf ] Satinder P. Singh , David Cohn How to Dynamically Merge Markov Decision Processes. [Citation Graph (0, 0)][DBLP ] NIPS, 1997, pp:- [Conf ] Satinder P. Singh , Peter Dayan Analytical Mean Squared Error Curves in Temporal Difference Learning. [Citation Graph (0, 0)][DBLP ] NIPS, 1996, pp:1054-1060 [Conf ] Satinder P. Singh , Tommi Jaakkola , Michael I. Jordan Reinforcement Learning with Soft State Aggregation. [Citation Graph (0, 0)][DBLP ] NIPS, 1994, pp:361-368 [Conf ] Satinder P. Singh , Michael J. Kearns , Diane J. Litman , Marilyn A. Walker Reinforcement Learning for Spoken Dialogue Systems. [Citation Graph (0, 0)][DBLP ] NIPS, 1999, pp:956-962 [Conf ] Richard S. Sutton , David A. McAllester , Satinder P. Singh , Yishay Mansour Policy Gradient Methods for Reinforcement Learning with Function Approximation. [Citation Graph (0, 0)][DBLP ] NIPS, 1999, pp:1057-1063 [Conf ] Richard S. Sutton , Satinder P. Singh , Doina Precup , Balaraman Ravindran Improved Switching among Temporally Abstract Actions. [Citation Graph (0, 0)][DBLP ] NIPS, 1998, pp:1066-1072 [Conf ] John K. Williams , Satinder P. Singh Experimental Results on Learning Stochastic Memoryless Policies for Partially Observable Markov Decision Processes. [Citation Graph (0, 0)][DBLP ] NIPS, 1998, pp:1073-1080 [Conf ] Peter Stone , Richard S. Sutton , Satinder P. Singh Reinforcement Learning for 3 vs. 2 Keepaway [Citation Graph (0, 0)][DBLP ] RoboCup, 2000, pp:249-258 [Conf ] Satinder P. Singh , Vishal Soni , Michael P. Wellman Computing approximate bayes-nash equilibria in tree-games of incomplete information. [Citation Graph (0, 0)][DBLP ] ACM Conference on Electronic Commerce, 2004, pp:81-90 [Conf ] Michael J. Kearns , Michael L. Littman , Satinder P. Singh Graphical Models for Game Theory. [Citation Graph (0, 0)][DBLP ] UAI, 2001, pp:253-260 [Conf ] Michael J. Kearns , Yishay Mansour , Satinder P. Singh Fast Planning in Stochastic Games. [Citation Graph (0, 0)][DBLP ] UAI, 2000, pp:309-316 [Conf ] Yishay Mansour , Satinder P. Singh On the Complexity of Policy Iteration. [Citation Graph (0, 0)][DBLP ] UAI, 1999, pp:401-408 [Conf ] David A. McAllester , Satinder P. Singh Approximate Planning for Factored POMDPs using Belief State Simplification. [Citation Graph (0, 0)][DBLP ] UAI, 1999, pp:409-416 [Conf ] Satinder P. Singh , Michael J. Kearns , Yishay Mansour Nash Convergence of Gradient Dynamics in General-Sum Games. [Citation Graph (0, 0)][DBLP ] UAI, 2000, pp:541-548 [Conf ] János A. Csirik , Michael L. Littman , Satinder P. Singh , Peter Stone FAucS : An FCC Spectrum Auction Simulator for Autonomous Bidding Agents. [Citation Graph (0, 0)][DBLP ] WELCOM, 2001, pp:139-151 [Conf ] Charles Lee Isbell Jr. , Michael J. Kearns , Satinder P. Singh , Christian R. Shelton , Peter Stone , David P. Kormann Cobot in LambdaMOO: An Adaptive Social Statistics Agent. [Citation Graph (0, 0)][DBLP ] Autonomous Agents and Multi-Agent Systems, 2006, v:13, n:3, pp:327-354 [Journal ] Andrew G. Barto , Steven J. Bradtke , Satinder P. Singh Learning to Act Using Real-Time Dynamic Programming. [Citation Graph (0, 0)][DBLP ] Artif. Intell., 1995, v:72, n:1-2, pp:81-138 [Journal ] Richard S. Sutton , Doina Precup , Satinder P. Singh Between MDPs and Semi-MDPs: A Framework for Temporal Abstraction in Reinforcement Learning. [Citation Graph (0, 0)][DBLP ] Artif. Intell., 1999, v:112, n:1-2, pp:181-211 [Journal ] Nicholas L. Cassimatis , Sean Luke , Simon D. Levy , Ross Gayler , Pentti Kanerva , Chris Eliasmith , Timothy W. Bickmore , Alan C. Schultz , Randall Davis , James A. Landay , Robert C. Miller , Eric Saund , Thomas F. Stahovich , Michael L. Littman , Satinder P. Singh , Shlomo Argamon , Shlomo Dubnov Reports on the 2004 AAAI Fall Symposia. [Citation Graph (0, 0)][DBLP ] AI Magazine, 2005, v:26, n:1, pp:98-102 [Journal ] Satinder P. Singh , Diane J. Litman , Michael J. Kearns , Marilyn A. Walker Optimizing Dialogue Management with Reinforcement Learning: Experiments with the NJFun System. [Citation Graph (0, 0)][DBLP ] J. Artif. Intell. Res. (JAIR), 2002, v:16, n:, pp:105-133 [Journal ] Peter Stone , Michael L. Littman , Satinder P. Singh , Michael J. Kearns ATTac-2000: An Adaptive Autonomous Bidding Agent. [Citation Graph (0, 0)][DBLP ] J. Artif. Intell. Res. (JAIR), 2001, v:15, n:, pp:189-206 [Journal ] Michael J. Kearns , Satinder P. Singh Near-Optimal Reinforcement Learning in Polynomial Time. [Citation Graph (0, 0)][DBLP ] Machine Learning, 2002, v:49, n:2-3, pp:209-232 [Journal ] Satinder P. Singh Introduction. [Citation Graph (0, 0)][DBLP ] Machine Learning, 2002, v:49, n:2-3, pp:107-109 [Journal ] Satinder P. Singh Transfer of Learning by Composing Solutions of Elemental Sequential Tasks. [Citation Graph (0, 0)][DBLP ] Machine Learning, 1992, v:8, n:, pp:323-339 [Journal ] Satinder P. Singh , Peter Dayan Analytical Mean Squared Error Curves for Temporal Difference Learning. [Citation Graph (0, 0)][DBLP ] Machine Learning, 1998, v:32, n:1, pp:5-40 [Journal ] Satinder P. Singh , Tommi Jaakkola , Michael L. Littman , Csaba Szepesvári Convergence Results for Single-Step On-Policy Reinforcement-Learning Algorithms. [Citation Graph (0, 0)][DBLP ] Machine Learning, 2000, v:38, n:3, pp:287-308 [Journal ] Satinder P. Singh , Richard S. Sutton Reinforcement Learning with Replacing Eligibility Traces. [Citation Graph (0, 0)][DBLP ] Machine Learning, 1996, v:22, n:1-3, pp:123-158 [Journal ] Satinder P. Singh , Richard C. Yee An Upper Bound on the Loss from Approximate Optimal-Value Functions. [Citation Graph (0, 0)][DBLP ] Machine Learning, 1994, v:16, n:3, pp:227-233 [Journal ] Vishal Soni , Satinder P. Singh Abstraction in Predictive State Representations. [Citation Graph (0, 0)][DBLP ] AAAI, 2007, pp:639-644 [Conf ] Satinder P. Singh , Michael R. James , Matthew R. Rudary Predictive State Representations: A New Theory for Modeling Dynamical Systems. [Citation Graph (0, 0)][DBLP ] UAI, 2004, pp:512-518 [Conf ] Matthew R. Rudary , Satinder P. Singh , David Wingate Predictive Linear-Gaussian Models of Stochastic Dynamical Systems. [Citation Graph (0, 0)][DBLP ] UAI, 2005, pp:501-508 [Conf ] Ruggiero Cavallo , David C. Parkes , Satinder P. Singh Optimal Coordinated Planning Amongst Self-Interested Agents with Private State. [Citation Graph (0, 0)][DBLP ] UAI, 2006, pp:- [Conf ] Yevgeniy Vorobeychik , Michael P. Wellman , Satinder P. Singh Learning payoff functions in infinite games. [Citation Graph (0, 0)][DBLP ] Machine Learning, 2007, v:67, n:1-2, pp:145-168 [Journal ] Constraint satisfaction algorithms for graphical games. [Citation Graph (, )][DBLP ] On discovery and learning of models with predictive representations of state for agents with continuous actions and observations. [Citation Graph (, )][DBLP ] Approximate predictive state representations. [Citation Graph (, )][DBLP ] SarsaLandmark: an algorithm for learning in POMDPs with landmarks. [Citation Graph (, )][DBLP ] Transfer via soft homomorphisms. [Citation Graph (, )][DBLP ] Linear options. [Citation Graph (, )][DBLP ] History-dependent graphical multiagent models. [Citation Graph (, )][DBLP ] Efficiently learning linear-linear exponential family predictive representations of state. [Citation Graph (, )][DBLP ] Internal Rewards Mitigate Agent Boundedness. [Citation Graph (, )][DBLP ] Planning with predictive state representations. [Citation Graph (, )][DBLP ] Learning Graphical Game Models. [Citation Graph (, )][DBLP ] Knowledge Combination in Graphical Multiagent Models. [Citation Graph (, )][DBLP ] Strategic Interactions in a Supply Chain Game. [Citation Graph (, )][DBLP ] Search in 0.008secs, Finished in 0.013secs