Relational Reinforcement Learning
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[1] Tim Oates,et al. Learning in Worlds with Objects , 2017, Encyclopedia of Machine Learning and Data Mining.
[2] Marcel Abendroth,et al. Data Mining Practical Machine Learning Tools And Techniques With Java Implementations , 2016 .
[3] U. Rieder,et al. Markov Decision Processes , 2010 .
[4] Dimitri P. Bertsekas,et al. Neuro-Dynamic Programming , 2009, Encyclopedia of Optimization.
[5] Jens Vygen,et al. The Book Review Column1 , 2020, SIGACT News.
[6] Abhijit Gosavi,et al. Self-Improving Factory Simulation using Continuous-time Average-Reward Reinforcement Learning , 2007 .
[7] Thomas Gärtner,et al. Graph kernels and Gaussian processes for relational reinforcement learning , 2006, Machine Learning.
[8] David W. Aha,et al. Instance-Based Learning Algorithms , 1991, Machine Learning.
[9] Richard S. Sutton,et al. Reinforcement Learning: An Introduction , 1998, IEEE Trans. Neural Networks.
[10] Saso Dzeroski,et al. Integrating Guidance into Relational Reinforcement Learning , 2004, Machine Learning.
[11] Luc De Raedt,et al. Bellman goes relational , 2004, ICML.
[12] M. van Otterlo. Reinforcement Learning for Relational MDPs , 2004 .
[13] Erik D. Demaine,et al. Tetris is hard, even to approximate , 2002, Int. J. Comput. Geom. Appl..
[14] Hector J. Levesque,et al. On the Semantics of Deliberation in IndiGolog — from Theory to Implementation , 2002, Annals of Mathematics and Artificial Intelligence.
[15] Hannu Toivonen,et al. Discovery of frequent DATALOG patterns , 1999, Data Mining and Knowledge Discovery.
[16] Luc De Raedt,et al. Scaling Up Inductive Logic Programming by Learning from Interpretations , 1999, Data Mining and Knowledge Discovery.
[17] Paul E. Utgoff,et al. Decision Tree Induction Based on Efficient Tree Restructuring , 1997, Machine Learning.
[18] Andrew W. Moore,et al. Locally Weighted Learning , 1997, Artificial Intelligence Review.
[19] Gerald Tesauro,et al. Practical issues in temporal difference learning , 1992, Machine Learning.
[20] DIMITRIOS PIERRAKOS,et al. User Modeling and User-Adapted Interaction , 2004, User Modeling and User-Adapted Interaction.
[21] Longxin Lin. Self-Improving Reactive Agents Based on Reinforcement Learning, Planning and Teaching , 2004, Machine Learning.
[22] Dirk Ormoneit,et al. Kernel-Based Reinforcement Learning , 2004, Machine Learning.
[23] Carl E. Rasmussen,et al. Gaussian Processes in Reinforcement Learning , 2003, NIPS.
[24] Robert Givan,et al. Approximate Policy Iteration with a Policy Language Bias , 2003, NIPS.
[25] Kurt Driessens,et al. Relational Instance Based Regression for Relational Reinforcement Learning , 2003, ICML.
[26] Carlos Guestrin,et al. Generalizing plans to new environments in relational MDPs , 2003, IJCAI 2003.
[27] Thomas Gärtner,et al. A survey of kernels for structured data , 2003, SKDD.
[28] Mike Barley,et al. Intelligent Agents and Multi-Agent Systems , 2003, Lecture Notes in Computer Science.
[29] Sridhar Mahadevan,et al. Recent Advances in Hierarchical Reinforcement Learning , 2003, Discret. Event Dyn. Syst..
[30] Luc De Raedt,et al. Logical Markov Decision Programs , 2003 .
[31] Eduardo F. Morales,et al. Scaling Up Reinforcement Learning with a Relational Representation , 2003 .
[32] Maurice Bruynooghe,et al. Aggregation versus selection bias, and relational neural networks , 2003 .
[33] Thomas Gärtner,et al. On Graph Kernels: Hardness Results and Efficient Alternatives , 2003, COLT.
[34] Thomas Gärtner,et al. Kernels for structured data , 2008, Series in Machine Perception and Artificial Intelligence.
[35] Robert Givan,et al. Inductive Policy Selection for First-Order MDPs , 2002, UAI.
[36] Tim Oates,et al. The Thing that we Tried Didn't Work very Well: Deictic Representation in Reinforcement Learning , 2002, UAI.
[37] van Martijn Otterlo. Relational Representations in Reinforcement Learning: Review and Open Problems , 2002, ICML 2002.
[38] Saso Dzeroski,et al. Integrating Experimentation and Guidance in Relational Reinforcement Learning , 2002, ICML.
[39] Wim Van Laer. From Propositional to First Order Logic in Machine Learning and Data Mining - Induction of first ord , 2002 .
[40] Michail G. Lagoudakis,et al. Least-Squares Methods in Reinforcement Learning for Control , 2002, SETN.
[41] Hisashi Kashima,et al. Kernels for graph classification , 2002 .
[42] Saso Dzeroski,et al. On using guidance in relational reinforcement learning , 2002 .
[43] Jan Ramon,et al. Clustering and instance based learning in first order logic , 2002, AI Communications.
[44] Jeffrey M. Forbes,et al. Representations for learning control policies , 2002 .
[45] Stefan Kramer,et al. Inducing classification and regression trees in first order logic , 2001 .
[46] Kurt Driessens,et al. Learning digger using hierarchical reinforcement learning for concurrent goals , 2001 .
[47] Hendrik Blockeel,et al. From Shell Logs to Shell Scripts , 2001, ILP.
[48] Kurt Driessens,et al. Speeding Up Relational Reinforcement Learning through the Use of an Incremental First Order Decision Tree Learner , 2001, ECML.
[49] Maurice Bruynooghe,et al. A polynomial time computable metric between point sets , 2001, Acta Informatica.
[50] Craig Boutilier,et al. Symbolic Dynamic Programming for First-Order MDPs , 2001, IJCAI.
[51] Ross D. Shachter,et al. Using background knowledge to speed reinforcement learning in physical agents , 2001, AGENTS '01.
[52] Ashwin Srinivasan,et al. Warmr: a data mining tool for chemical data , 2001, J. Comput. Aided Mol. Des..
[53] Michael Collins,et al. Convolution Kernels for Natural Language , 2001, NIPS.
[54] Xin Wang,et al. Batch Value Function Approximation via Support Vectors , 2001, NIPS.
[55] John K. Slaney,et al. Blocks World revisited , 2001, Artif. Intell..
[56] S. Džeroski,et al. Relational Data Mining , 2001, Springer Berlin Heidelberg.
[57] Nello Cristianini,et al. Classification using String Kernels , 2000 .
[58] Bart Demoen,et al. Executing Query Packs in ILP , 2000, ILP.
[59] Leslie Pack Kaelbling,et al. Practical Reinforcement Learning in Continuous Spaces , 2000, ICML.
[60] Stefan Schaal,et al. Real-time robot learning with locally weighted statistical learning , 2000, Proceedings 2000 ICRA. Millennium Conference. IEEE International Conference on Robotics and Automation. Symposia Proceedings (Cat. No.00CH37065).
[61] W. Imrich,et al. Product Graphs: Structure and Recognition , 2000 .
[62] Nello Cristianini,et al. An Introduction to Support Vector Machines and Other Kernel-based Learning Methods , 2000 .
[63] Thomas G. Dietterich. Hierarchical Reinforcement Learning with the MAXQ Value Function Decomposition , 1999, J. Artif. Intell. Res..
[64] K. R. Dixon,et al. Incorporating Prior Knowledge and Previously Learned Information into Reinforcement Learning Agents , 2000 .
[65] Gunnar Rätsch,et al. Engineering Support Vector Machine Kerneis That Recognize Translation Initialion Sites , 2000, German Conference on Bioinformatics.
[66] Doina Precup,et al. Between MDPs and Semi-MDPs: A Framework for Temporal Abstraction in Reinforcement Learning , 1999, Artif. Intell..
[67] Craig Boutilier,et al. Decision-Theoretic Planning: Structural Assumptions and Computational Leverage , 1999, J. Artif. Intell. Res..
[68] Andrew McCallum,et al. Using Reinforcement Learning to Spider the Web Efficiently , 1999, ICML.
[69] Marco Wiering,et al. Explorations in efficient reinforcement learning , 1999 .
[70] Hendrik Blockeel,et al. Top-Down Induction of First Order Logical Decision Trees , 1998, AI Commun..
[71] Luc Dehaspe. Frequent Pattern Discovery in First-Order Logic , 1999, AI Commun..
[72] David Haussler,et al. Convolution kernels on discrete structures , 1999 .
[73] Luc De Raedt,et al. Top-Down Induction of Clustering Trees , 1998, ICML.
[74] Wim Van Laer,et al. A methodology for first order learning: a case study , 1998 .
[75] D. Mackay,et al. Introduction to Gaussian processes , 1998 .
[76] Stuart J. Russell,et al. Reinforcement Learning with Hierarchies of Machines , 1997, NIPS.
[77] Michèle Sebag,et al. Distance Induction in First Order Logic , 1997, ILP.
[78] Russell Greiner,et al. Why Experimentation can be better than "Perfect Guidance" , 1997, ICML.
[79] Thomas G. Dietterich. What is machine learning? , 2020, Archives of Disease in Childhood.
[80] Dietrich Wettschereck,et al. Relational Instance-Based Learning , 1996, ICML.
[81] Ivan Bratko,et al. Learning Models of Control Skills: Phenomena, Results and Problems , 1996 .
[82] Andrew W. Moore,et al. Reinforcement Learning: A Survey , 1996, J. Artif. Intell. Res..
[83] Melanie. Mitchell,et al. An introduction to genetic algorithms [electronic resource] , 1996 .
[84] Andrew McCallum,et al. Reinforcement learning with selective perception and hidden state , 1996 .
[85] Mark Humphrys. W-learning: Competition among selfish Q-learners , 1995 .
[86] Xuemei Wang,et al. Learning by Observation and Practice: An Incremental Approach for Planning Operator Acquisition , 1995, ICML.
[87] Peter Norvig,et al. Artificial Intelligence: A Modern Approach , 1995 .
[88] Andrew G. Barto,et al. Learning to Act Using Real-Time Dynamic Programming , 1995, Artif. Intell..
[89] Pat Langley,et al. Elements of Machine Learning , 1995 .
[90] Luc De Raedt,et al. First-Order jk-Clausal Theories are PAC-Learnable , 1994, Artif. Intell..
[91] Michael I. Jordan,et al. MASSACHUSETTS INSTITUTE OF TECHNOLOGY ARTIFICIAL INTELLIGENCE LABORATORY and CENTER FOR BIOLOGICAL AND COMPUTATIONAL LEARNING DEPARTMENT OF BRAIN AND COGNITIVE SCIENCES , 1996 .
[92] Andrew G. Barto,et al. Monte Carlo Matrix Inversion and Reinforcement Learning , 1993, NIPS.
[93] Leslie Pack Kaelbling,et al. Input Generalization in Delayed Reinforcement Learning: An Algorithm and Performance Comparisons , 1991, IJCAI.
[94] David W. Aha,et al. Instance‐based prediction of real‐valued attributes , 1989, Comput. Intell..
[95] C. Watkins. Learning from delayed rewards , 1989 .
[96] David E. Goldberg,et al. Genetic Algorithms in Search Optimization and Machine Learning , 1988 .
[97] Stephen Barnett,et al. Matrix Methods for Engineers and Scientists , 1982 .
[98] Nils J. Nilsson,et al. Principles of Artificial Intelligence , 1980, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[99] Michael J. Fischer,et al. The String-to-String Correction Problem , 1974, JACM.
[100] Richard Fikes,et al. STRIPS: A New Approach to the Application of Theorem Proving to Problem Solving , 1971, IJCAI.
[101] Donald Michie,et al. Man-Machine Co-operation on a Learning Task , 1969 .
[102] Frank Harary,et al. Graph Theory , 2016 .
[103] Richard Bellman,et al. Adaptive Control Processes: A Guided Tour , 1961, The Mathematical Gazette.
[104] N. Aronszajn. Theory of Reproducing Kernels. , 1950 .