A 'Neural' Network that Learns to Play Backgammon. - Gerald Tesauro; Terrence J. Sejnowski (NIPS, 1987)

Scaling Relationships in Back-Propagation Learning: Dependence on Training Set Size. - Gerald Tesauro (Complex Syst., 1987)

Connectionist Learning of Expert Backgammon Evaluations. - Gerald Tesauro (ML, 1988)

Scaling and Generalization in Neural Networks: A Case Study. - Subutai Ahmad; Gerald Tesauro (NIPS, 1988)

Connectionist Learning of Expert Preferences by Comparison Training. - Gerald Tesauro (NIPS, 1988)

Scaling Relationships in Back-propagation Learning. - Gerald Tesauro; Bob Janssens (Complex Syst., 1988)

A study of scaling and generalization in neural networks. - Subutai Ahmad; Gerald Tesauro (Neural Networks, 1988)

Asymptotic Convergence of Backpropagation: Numerical Experiments. - Subutai Ahmad; Gerald Tesauro; Yu He (NIPS, 1989)

Neural Network Visualization. - Jakub Wejchert; Gerald Tesauro (NIPS, 1989)

A Parallel Network that Learns to Play Backgammon. - Gerald Tesauro; Terrence J. Sejnowski (Artif. Intell., 1989)

Neurogammon Wins Computer Olympiad. - Gerald Tesauro (Neural Comput., 1989)

Asymptotic Convergence of Backpropagation. - Gerald Tesauro; Yu He; Subutai Ahmad (Neural Comput., 1989)

Neurogammon: a neural-network backgammon program. - Gerald Tesauro (IJCNN, 1990)

Can Neural Networks Do Better Than the Vapnik-Chervonenkis Bounds? - David A. Cohn; Gerald Tesauro (NIPS, 1990)

Visualizing processes in neural networks. - Jakub Wejchert; Gerald Tesauro (IBM J. Res. Dev., 1991)

Temporal Difference Learning of Backgammon Strategy. - Gerald Tesauro (ML, 1992)

Practical Issues in Temporal Difference Learning. - Gerald Tesauro (Mach. Learn., 1992)

How Tight Are the Vapnik-Chervonenkis Bounds? - David A. Cohn; Gerald Tesauro (Neural Comput., 1992)

TD-Gammon, a Self-Teaching Backgammon Program, Achieves Master-Level Play. - Gerald Tesauro (Neural Comput., 1994)

Biologically Inspired Defenses Against Computer Viruses. - Jeffrey O. Kephart; Gregory B. Sorkin; William C. Arnold; David M. Chess; Gerald Tesauro; Steve R. White (IJCAI (1), 1995)

Temporal Difference Learning and TD-Gammon. - Gerald Tesauro (J. Int. Comput. Games Assoc., 1995)

On-line Policy Improvement using Monte-Carlo Search. - Gerald Tesauro; Gregory R. Galperin (NIPS, 1996)

Foresight-based pricing algorithms in an economy of software agents. - Gerald Tesauro; Jeffrey O. Kephart (ICE, 1998)

Comments on "Co-Evolution in the Successful Learning of Backgammon Strategy". - Gerald Tesauro (Mach. Learn., 1998)

Strategic pricebot dynamics. - Amy Greenwald; Jeffrey O. Kephart; Gerald Tesauro (EC, 1999)

Pseudo-convergent Q-Learning by Competitive Pricebots. - Jeffrey O. Kephart; Gerald Tesauro (ICML, 2000)

Multi-agent Q-learning and Regression Trees for Automated Pricing Decisions. - Manu Sridharan; Gerald Tesauro (ICML, 2000)

Foresight-based pricing algorithms in agent economies. - Gerald Tesauro; Jeffrey O. Kephart (Decis. Support Syst., 2000)

Agent-Human Interactions in the Continuous Double Auction. - Rajarshi Das; James E. Hanson; Jeffrey O. Kephart; Gerald Tesauro (IJCAI, 2001)

Pricing in Agent Economies Using Neural Networks and Multi-agent Q-Learning. - Gerald Tesauro (Sequence Learning, 2001)

High-performance bidding agents for the continuous double auction. - Gerald Tesauro; Rajarshi Das (EC, 2001)

Strategic sequential bidding in auctions using dynamic programming. - Gerald Tesauro; Jonathan Bredin (AAMAS, 2002)

Pricing in Agent Economies Using Multi-Agent Q-Learning. - Gerald Tesauro; Jeffrey O. Kephart (Auton. Agents Multi Agent Syst., 2002)

Programming backgammon using self-teaching neural nets. - Gerald Tesauro (Artif. Intell., 2002)

Extending Q-Learning to General Adaptive Multi-Agent Systems. - Gerald Tesauro (NIPS, 2003)

Multi-agent implementation of asymmetric protocol for bilateral negotiations. - James E. Hanson; Gerald Tesauro; Jeffrey O. Kephart; E. C. Snibl (EC, 2003)

A strategic decision model for multi-attribute bilateral negotiation with alternating. - Cuihong Li; Gerald Tesauro (EC, 2003)

Cooperative Negotiation in Autonomic Systems using Incremental Utility Elicitation. - Craig Boutilier; Rajarshi Das; Jeffrey O. Kephart; Gerald Tesauro; William E. Walsh (UAI, 2003)

A Multi-Agent Systems Approach to Autonomic Computing. - Gerald Tesauro; David M. Chess; William E. Walsh; Rajarshi Das; Alla Segal; Ian Whalley; Jeffrey O. Kephart; Steve R. White (AAMAS, 2004)

Utility Functions in Autonomic Systems. - William E. Walsh; Gerald Tesauro; Jeffrey O. Kephart; Rajarshi Das (ICAC, 2004)

New Approaches to Optimization and Utility Elicitation in Autonomic Computing. - Relu Patrascu; Craig Boutilier; Rajarshi Das; Jeffrey O. Kephart; Gerald Tesauro; William E. Walsh (AAAI, 2005)

Online Resource Allocation Using Decompositional Reinforcement Learning. - Gerald Tesauro (AAAI, 2005)

Utility-Function-Driven Resource Allocation in Autonomic Systems. - Gerald Tesauro; Rajarshi Das; William E. Walsh; Jeffrey O. Kephart (ICAC, 2005)

Improvement of Systems Management Policies Using Hybrid Reinforcement Learning. - Gerald Tesauro; Nicholas K. Jong; Rajarshi Das; Mohamed N. Bennani (ECML, 2006)

A Hybrid Reinforcement Learning Approach to Autonomic Resource Allocation. - Gerald Tesauro; Nicholas K. Jong; Rajarshi Das; Mohamed N. Bennani (ICAC, 2006)

Coordinating Multiple Autonomic Managers to Achieve Specified Power-Performance Tradeoffs. - Jeffrey O. Kephart; Hoi Y. Chan; Rajarshi Das; David W. Levine; Gerald Tesauro; Freeman L. Rawson III; Charles Lefurgy (ICAC, 2007)

Estimating End-to-End Performance by Collaborative Prediction with Active Sampling. - Irina Rish; Gerald Tesauro (Integrated Network Management, 2007)

Managing Power Consumption and Performance of Computing Systems Using Reinforcement Learning. - Gerald Tesauro; Rajarshi Das; Hoi Y. Chan; Jeffrey O. Kephart; David W. Levine; Freeman L. Rawson III; Charles Lefurgy (NIPS, 2007)

On the use of hybrid reinforcement learning for autonomic resource allocation. - Gerald Tesauro; Nicholas K. Jong; Rajarshi Das; Mohamed N. Bennani (Clust. Comput., 2007)

Reinforcement Learning in Autonomic Computing: A Manifesto and Case Studies. - Gerald Tesauro (IEEE Internet Comput., 2007)

Metric Learning for Kernel Regression. - Kilian Q. Weinberger; Gerald Tesauro (AISTATS, 2007)

Autonomic multi-agent management of power and performance in data centers. - Rajarshi Das; Jeffrey O. Kephart; Charles Lefurgy; Gerald Tesauro; David W. Levine; Hoi Y. Chan (AAMAS (Industry Track), 2008)

Active Collaborative Prediction with Maximum Margin Matrix Factorization. - Irina Rish; Gerald Tesauro (ISAIM, 2008)

Monte-Carlo simulation balancing. - David Silver; Gerald Tesauro (ICML, 2009)

Bayesian Inference in Monte-Carlo Tree Search. - Gerald Tesauro; V. T. Rajan; Richard B. Segal (UAI, 2010)

Playing repeated Stackelberg games with unknown opponents. - Janusz Marecki; Gerald Tesauro; Richard B. Segal (AAMAS, 2012)

Applying a framework for healthcare incentives simulation. - Joseph P. Bigus; Ching-Hua Chen-Ritzo; Keith Hermiz; Gerald Tesauro; Robert Sorrentino (WSC, 2012)

Simulation, learning, and optimization techniques in Watson's game strategies. - Gerry Tesauro; David Gondek; Jon Lenchner; James Fan; John M. Prager (IBM J. Res. Dev., 2012)

Analysis of Watson's Strategies for Playing Jeopardy! - Gerry Tesauro; David Gondek; Jonathan Lenchner; James Fan; John M. Prager (J. Artif. Intell. Res., 2013)

Budgeted Prediction with Expert Advice. - Kareem Amin 002; Satyen Kale; Gerald Tesauro; Deepak S. Turaga (AAAI, 2015)

Towards Cognitive Automation of Data Science. - Alain Biem; Maria Butrico; Mark Feblowitz; Tim Klinger; Yuri Malitsky; Kenney Ng; Adam Perer; Chandra Reddy; Anton Riabov; Horst Samulowitz; Daby M. Sow; Gerald Tesauro; et al. (AAAI, 2015)

Reports of the AAAI 2014 Conference Workshops. - Stefano V. Albrecht; André da Motta Salles Barreto; Darius Braziunas; David L. Buckeridge; Heriberto Cuayáhuitl; Nina Dethlefs; Markus Endres; Amir-massoud Farahmand; Mark Fox; Lutz Frommberger; Sam Ganzfried; Yolanda Gil; et al. (AI Mag., 2015)

Selecting Near-Optimal Learners via Incremental Data Allocation. - Ashish Sabharwal; Horst Samulowitz; Gerald Tesauro (AAAI, 2016)

Hierarchical Memory Networks. - Sarath Chandar; Sungjin Ahn; Hugo Larochelle; Pascal Vincent; Gerald Tesauro; Yoshua Bengio (CoRR, 2016)

Multiresolution Recurrent Neural Networks: An Application to Dialogue Response Generation. - Iulian Vlad Serban; Tim Klinger; Gerald Tesauro; Kartik Talamadupula; Bowen Zhou 002; Yoshua Bengio; Aaron C. Courville (AAAI, 2017)

Optimal Sequential Drilling for Hydrocarbon Field Development Planning. - Ruben Rodriguez Torrado; Jesus Rios; Gerald Tesauro (AAAI, 2017)

Learning to Query, Reason, and Answer Questions On Ambiguous Texts. - Xiaoxiao Guo; Tim Klinger; Clemens Rosenbaum; Joseph P. Bigus; Murray Campbell; Ban Kawas; Kartik Talamadupula; Gerry Tesauro; Satinder Singh 001 (ICLR (Poster), 2017)

Robust Task Clustering for Deep Many-Task Learning. - Mo Yu; Xiaoxiao Guo; Jinfeng Yi; Shiyu Chang; Saloni Potdar; Gerald Tesauro; Haoyu Wang 002; Bowen Zhou (CoRR, 2017)

R3: Reinforced Reader-Ranker for Open-Domain Question Answering. - Shuohang Wang; Mo Yu; Xiaoxiao Guo; Zhiguo Wang; Tim Klinger; Wei Zhang 057; Shiyu Chang; Gerald Tesauro; Bowen Zhou 002; Jing Jiang 001 (CoRR, 2017)

The Eigenoption-Critic Framework. - Miao Liu 001; Marlos C. Machado; Gerald Tesauro; Murray Campbell (CoRR, 2017)

Cognitive Computing. - Mohan Sridharan; Gerald Tesauro; James A. Hendler (IEEE Intell. Syst., 2017)

R3: Reinforced Ranker-Reader for Open-Domain Question Answering. - Shuohang Wang; Mo Yu; Xiaoxiao Guo; Zhiguo Wang; Tim Klinger; Wei Zhang 057; Shiyu Chang; Gerry Tesauro; Bowen Zhou 002; Jing Jiang 001 (AAAI, 2018)

Eigenoption Discovery through the Deep Successor Representation. - Marlos C. Machado; Clemens Rosenbaum; Xiaoxiao Guo; Miao Liu 001; Gerald Tesauro; Murray Campbell (ICLR (Poster), 2018)

Evidence Aggregation for Answer Re-Ranking in Open-Domain Question Answering. - Shuohang Wang; Mo Yu; Jing Jiang 001; Wei Zhang 057; Xiaoxiao Guo; Shiyu Chang; Zhiguo Wang; Tim Klinger; Gerald Tesauro; Murray Campbell (ICLR (Poster), 2018)

Diverse Few-Shot Text Classification with Multiple Metrics. - Mo Yu; Xiaoxiao Guo; Jinfeng Yi; Shiyu Chang; Saloni Potdar; Yu Cheng 001; Gerald Tesauro; Haoyu Wang 002; Bowen Zhou (NAACL-HLT, 2018)

Dialog-based Interactive Image Retrieval. - Xiaoxiao Guo; Hui Wu 009; Yu Cheng 001; Steven Rennie; Gerald Tesauro; Rogério Schmidt Feris (NeurIPS, 2018)

Learning Abstract Options. - Matthew Riemer; Miao Liu 001; Gerald Tesauro (NeurIPS, 2018)

Introduction to the special issue on deep reinforcement learning: An editorial. - Ron Sun; David Silver; Gerald Tesauro; Guang-Bin Huang (Neural Networks, 2018)

Hybrid Reinforcement Learning with Expert State Sequences. - Xiaoxiao Guo; Shiyu Chang; Mo Yu; Gerald Tesauro; Murray Campbell (AAAI, 2019)

Learning to Teach in Cooperative Multiagent Reinforcement Learning. - Shayegan Omidshafiei; Dong-Ki Kim; Miao Liu 001; Gerald Tesauro; Matthew Riemer; Christopher Amato; Murray Campbell; Jonathan P. How (AAAI, 2019)

Learning to Learn without Forgetting by Maximizing Transfer and Minimizing Interference. - Matthew Riemer; Ignacio Cases; Robert Ajemian; Miao Liu 001; Irina Rish; Yuhai Tu; Gerald Tesauro (ICLR (Poster), 2019)

Learning Hierarchical Teaching in Cooperative Multiagent Reinforcement Learning. - Dong-Ki Kim; Miao Liu 001; Shayegan Omidshafiei; Sebastian Lopez-Cot; Matthew Riemer; Golnaz Habibi; Gerald Tesauro; Sami Mourad; Murray Campbell; Jonathan P. How (CoRR, 2019)

On the Role of Weight Sharing During Deep Option Learning. - Matthew Riemer; Ignacio Cases; Clemens Rosenbaum; Miao Liu 001; Gerald Tesauro (AAAI, 2020)

Learning Hierarchical Teaching Policies for Cooperative Agents. - Dong-Ki Kim; Miao Liu 001; Shayegan Omidshafiei; Sebastian Lopez-Cot; Matthew Riemer; Golnaz Habibi; Gerald Tesauro; Sami Mourad; Murray Campbell; Jonathan P. How (AAMAS, 2020)

Decentralized TD Tracking with Linear Function Approximation and its Finite-Time Analysis. - Gang Wang 014; Songtao Lu; Georgios B. Giannakis; Gerald Tesauro; Jian Sun 003 (NeurIPS, 2020)

Finding Macro-Actions with Disentangled Effects for Efficient Planning with the Goal-Count Heuristic. - Cameron Allen; Tim Klinger; George Konidaris 001; Matthew Riemer; Gerald Tesauro (CoRR, 2020)

Deep RL With Information Constrained Policies: Generalization in Continuous Control. - Tyler Malloy; Chris R. Sims; Tim Klinger; Miao Liu 001; Matthew Riemer; Gerald Tesauro (CoRR, 2020)

Consolidation via Policy Information Regularization in Deep RL for Multi-Agent Games. - Tyler Malloy; Tim Klinger; Miao Liu 001; Matthew Riemer; Gerald Tesauro; Chris R. Sims (CoRR, 2020)

RL Generalization in a Theory of Mind Game Through a Sleep Metaphor (Student Abstract). - Tyler Malloy; Tim Klinger; Miao Liu 001; Gerald Tesauro; Matthew Riemer; Chris R. Sims (AAAI, 2021)

Text-based RL Agents with Commonsense Knowledge: New Challenges, Environments and Baselines. - Keerthiram Murugesan; Mattia Atzeni; Pavan Kapanipathi; Pushkar Shukla; Sadhana Kumaravel; Gerald Tesauro; Kartik Talamadupula; Mrinmaya Sachan; Murray Campbell (AAAI, 2021)

Capacity-Limited Decentralized Actor-Critic for Multi-Agent Games. - Tyler Malloy; Chris R. Sims; Tim Klinger; Miao Liu 001; Matthew Riemer; Gerald Tesauro (CoG, 2021)

Modeling Capacity-Limited Decision Making Using a Variational Autoencoder. - Tyler Malloy; Tim Klinger; Miao Liu 001; Gerald Tesauro; Matthew Riemer; Chris R. Sims (CogSci, 2021)

A Policy Gradient Algorithm for Learning to Learn in Multiagent Reinforcement Learning. - Dong-Ki Kim; Miao Liu 001; Matthew Riemer; Chuangchuang Sun; Marwa Abdulhai; Golnaz Habibi; Sebastian Lopez-Cot; Gerald Tesauro; Jonathan P. How (ICML, 2021)

Efficient Black-Box Planning Using Macro-Actions with Focused Effects. - Cameron Allen; Michael Katz 001; Tim Klinger; George Konidaris 001; Matthew Riemer; Gerald Tesauro (IJCAI, 2021)

Context-Specific Representation Abstraction for Deep Option Learning. - Marwa Abdulhai; Dong-Ki Kim; Matthew Riemer; Miao Liu 001; Gerald Tesauro; Jonathan P. How (AAAI, 2022)

Influencing Long-Term Behavior in Multiagent Reinforcement Learning. - Dong-Ki Kim; Matthew Riemer; Miao Liu 001; Jakob N. Foerster; Michael Everett; Chuangchuang Sun; Gerald Tesauro; Jonathan P. How (NeurIPS, 2022)

AI Planning Annotation for Sample Efficient Reinforcement Learning. - Junkyu Lee 001; Michael Katz 001; Don Joven Agravante; Miao Liu 001; Tim Klinger; Murray Campbell; Shirin Sohrabi; Gerald Tesauro (CoRR, 2022)

Game-Theoretical Perspectives on Active Equilibria: A Preferred Solution Concept over Nash Equilibria. - Dong-Ki Kim; Matthew Riemer; Miao Liu 001; Jakob N. Foerster; Gerald Tesauro; Jonathan P. How (CoRR, 2022)

Learning in Factored Domains with Information-Constrained Visual Representations. - Tyler Malloy; Miao Liu 001; Matthew D. Riemer; Tim Klinger; Gerald Tesauro; Chris R. Sims (CoRR, 2023)