Relational Reinforcement Learning

Cited by (371)

Items 201–300 of 371

1. From Reinforcement Learning Towards Artificial General Intelligence
   Filipe Marinho Rocha, Vı́tor Santos Costa, Luís Paulo Reis · 2020 · Advances in intelligent systems and computing
2. Using Previous Experience for Learning Planning Control Knowledge.
   Susana Fernández, Ricardo Aler, Daniel Borrajo · 2004
3. Reinforcement Learning with a Bilinear Q Function
   Charles Elkan · 2012 · Lecture notes in computer science
4. Optimizing motion primitives to make symbolic models more predictive
   Andreas Orthey, Marc Toussaint, Nikolay Jetchev · 2013 · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings
5. Reinforcement Learning in Nonstationary Environment Navigation Tasks
   Terran D.R. Lane, Martin Ridens, Scott Stevens · 2007 · Lecture notes in computer science
6. Adaptive Windowing for Online Learning from Multiple Inter-related Data Streams
   Elena Ikonomovska, Kurt Driessens, Sašo Džeroski, João Manuel Portela da Gama · 2011 · IEEE ... International Conference on Data Mining workshops
7. An enhancement of relational reinforcement learning
   Renato R. da Silva, Claudio A. Policastro, Roseli Aparecida Francelin Romero · 2008 · International Joint Conference on Neural Networks (IJCNN)
8. The Strategy Entropy of Reinforcement Learning for Mobile Robot Navigation in Complex Environments
   X. Zhuang · 2006 · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings
9. First Order Decision Diagrams for Relational MDPs
   Chenggang Wang, Saket Joshi, Roni Khardon · 2011
10. A causal approach to hierarchical decomposition in reinforcement learning
    Andrew G. Barto, Anders Jönsson · 2006
11. A substrate for accountable layered systems
    Bo Morgan · 2013 · DSpace@MIT (Massachusetts Institute of Technology)
12. Inductive Logic Programming
    Luc De Raedt · 2010 · Encyclopedia of Machine Learning
13. An Artificial Economy of Post Production Systems
    Eric B. Baum, Igor Đurđanović · 2001 · Lecture notes in computer science
14. Learning Goal Hierarchies from Structured Observations and Expert Annotations
    Tolga Könik, John E. Laird · 2004 · Lecture notes in computer science
15. Relational Reinforcement Learning
    Kurt Driessens · 2010 · Encyclopedia of Machine Learning
16. Relational Learning
    Jan Struyf, Hendrik Blockeel · 2010 · Encyclopedia of Machine Learning
17. Integrating Reinforcement Learning and Declarative Programming to Learn Causal Laws in Dynamic Domains
    Mohan Sridharan, Sarah Rainge · 2014 · Lecture notes in computer science
18. Interpretable Imitation Learning with Symbolic Rewards
    Nicolas Bougie, Takashi Onishi, Yoshimasa Tsuruoka · 2023 · ACM Transactions on Intelligent Systems and Technology
19. Finding Memoryless Probabilistic Relational Policies for Inter-task Reuse
    Valdinei Freire, Fernando Augusto Pereira, Anna Helena Reali Costa · 2012 · Communications in computer and information science
20. Should I do that? using relational reinforcement learning and declarative programming to discover domain axioms
    Mohan Sridharan, Ben Meadows · 2016 · IEEE International Conference on Development and Learning (ICDL)
21. Intelligent Business Process Execution using Particle Swarm Optimization
    Markus Kress, Sanaz Mostaghim, Detlef G. Seese · 2010 · IGI Global eBooks
22. Integrating Relational Reinforcement Learning with Reasoning about Actions and Change
    Matthias Nickles · 2012 · Lecture notes in computer science
23. An Alternative Approach to the Revision of Ordinal Conditional Functions in the Context of Multi-Valued Logic
    Klaus Häming, Gabriele Peters · 2010 · Lecture notes in computer science
24. Learning to evaluate conditional partial plans
    Sławomir Nowaczyk, Jacek Malec · 2007 · International Conference on Machine Learning and Applications
25. Performance Comparison of Relational Reinforcement Learning and RBF Neural Networks for Small Mobile Robots
    Roman Neruda, Stanislav Slušný, Petra Vidnerová · 2008 · International Conference on Future Generation Communication and Networking
26. Policy transfer with a relational learning classifier system
    Drew Mellor · 2005 · Genetic and Evolutionary Computation Conference Companion (GECCO Companion)
27. Temporal Pattern Mining in Dynamic Environments
    Andreas D. Lattner · 2007
28. Symbolic Relational Deep Reinforcement Learning based on Graph Neural Networks.
    Jaromír Janisch, Tomáš Pevný, Viliam Lisý · 2020 · arXiv (Cornell University)
29. Faculty of Engineering and Science
    Klaus Bruhn Jensen · 2004
30. Reinforcement Learning
    Amandeep Singh Bhatia, Mandeep Kaur Saggi, Amit Sundas, Jatinder Ashta · 2020
31. Using an induced relational decision tree for rule injection in a learning classifier system
    Jose Ignacio Estevez, Pedro Toledo, Silvia Alayón · 2011 · IEEE Congress on Evolutionary Computation (CEC)
32. Symbol acquisition for task-level planning
    George Konidaris, Leslie Pack Kaelbling, Tomás Lozano‐Pérez · 2013 · DSpace@MIT (Massachusetts Institute of Technology)
33. Machine Discovery of Comprehensible Strategies for Simple Games Using Meta-interpretive Learning
    Stephen Muggleton, Céline Hocquette · 2019 · New Generation Computing
34. Cluster-Based Social Reinforcement Learning
    Mahak Goindani, Jennifer Neville · 2020 · Adaptive Agents and Multi-Agents Systems
35. Inverse reinforcement learning through logic constraint inference
    Mattijs Baert, Sam Leroux, Pieter Simoens · 2023 · Machine Learning
36. Counting-MLNs: Learning Relational Structure for Decision Making
    Aniruddh Nath, Matthew Richardson · 2021 · Proceedings of the AAAI Conference on Artificial Intelligence
37. Automatic Induction of Bellman-Error Features for Probabilistic Planning
    Jia-hong Wu, Robert L. Givan · 2010
38. Agent Learning in Relational Domains based on Logical MDPs with Negation
    Song Zhiwei, Xiaoping Chen, Shuang Cong · 2008 · Journal of Computers
39. Relational reinforcement learning and recurrent neural network with state classification to solve joint attention
    Renato Ramos da Silva, Roseli Aparecida Francelin Romero · 2011 · International Joint Conference on Neural Networks (IJCNN)
40. Graph-based reasoning and reinforcement learning for improving Q/A performance in large knowledge-based systems
    Abhishek B. Sharma, Kenneth D. Forbus · 2010
41. A System for the Use of Answer Set Programming in Reinforcement Learning
    Matthias Nickles · 2012 · Lecture notes in computer science
42. On the definition of a general learning system with user-defined operators
    Fernando Martínez‐Plumed, Cèsar Ferri, José Hernández‐Orallo, Marïa José Ramírez-Quintana · 2013 · arXiv (Cornell University)
43. Planning and learning under uncertainty
    Sergio Jiménez Celorrio · 2011 · LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas)
44. Compact Representation of Knowledge Bases in Inductive Logic Programming
    Jan Struyf, Jan Ramon, Maurice Bruynooghe, Sofie Verbaeten, Hendrik Blockeel · 2004 · Machine Learning
45. Planning and exploration in stochastic relational worlds
    Tobias Lang · 2011 · Universitätsbibliothek der FU Berlin Hochschulschriftenstelle u. Dokumentenserver
46. Cost-sensitive reinforcement learning
    Robby Goetschalckx, Kurt Driessens · 2007 · Lirias
47. Learning to repair plans and schedules using a relational (deictic) representation
    Jorge Andrés Palombarini, Ernesto Carlos Martinez · 2010 · Brazilian Journal of Chemical Engineering
48. Learning Scan Paths for Object Recognition with Relational Reinforcement Learning
    Klaus Häming, Gabriele Peters · 2010 · IASTED International Conference on Signal Processing, Pattern Recognition and Applications / Computer Graphics and Imaging (SPPRA/CGIM)
49. Coevolution of Pattern Generators and Recognizers
    Stewart W. Wilson · 2010 · Lecture notes in computer science
50. Zero-Shot Transfer with Deictic Object-Oriented Representation in Reinforcement Learning
    Ofir Marom, Benjamin Rosman · 2018 · neural information processing systems
51. Statistical Relational Artificial Intelligence: From Distributions through Actions to Optimization
    Kristian Kersting B, Sriraam Natarajan · 2015 · Künstliche Intell.
52. Using k-means for clustering in complex automotive production systems to support a Q-learning-system
    Andre Doring, Wilhelm Dangelmaier, Christoph Danne · 2007 · IEEE International Conference on Cognitive Informatics
53. Rule abstraction and transfer in reinforcement learning by decision tree
    Min Wu, Atsushi Yamashita, Hajime Asama · 2012 · IEEE/SICE International Symposium on System Integration (SII)
54. Thesis: relational reinforcement learning
    Kurt Driessens · 2005 · AI Communications
55. Probabilistic Backward and Forward Reasoning in Stochastic Relational Worlds
    Tobias Lang, Marc Toussaint · 2010
56. Improving Acquisition of Teleoreactive Logic Programs through Representation Change
    Nan Li, David John Stracuzzi, Pat Langley B · 2011
57. Automatic discovery of concepts and actions
    Ana C. Tenorio-González, Eduardo F. Morales · 2017 · Expert Systems with Applications
58. A Direct Policy-Search Algorithm for Relational Reinforcement Learning
    Samuel Sarjant, Bernhard Pfahringer, Kurt Driessens, Tony C. Smith · 2014 · Lecture notes in computer science
59. The Allocation of Time and Location Information to Activity-Travel Sequence Data by Means of Reinforcement Learning
    Wets Janssens · 2008
60. SMART (Stochastic Model Acquisition with ReinforcemenT) Learning Agents: A Preliminary Report
    Chris Child, Kostas Stathis · 2005 · Lecture notes in computer science
61. Policy Search Based Relational Reinforcement Learning using the Cross-Entropy Method
    Samuel Sarjant · 2013 · Research Commons (University of Waikato)
62. Applying probabilistic rules to relational worlds
    Hernandez Gardiol, Natália Natália · 2003 · DSpace@MIT (Massachusetts Institute of Technology)
63. Can I Do That? Discovering Domain Axioms Using Declarative Programming and Relational Reinforcement Learning
    Mohan Sridharan, Prashanth Devarakonda, Rashmica Gupta · 2016 · Lecture notes in computer science
64. Collaborative Decision in Multi-Agent Learning of Action Models
    Christophe Rodrigues, Henry Soldano, Gauvain Bourgne, Céline Rouveirol · 2016 · IEEE International Conference on Tools with Artificial Intelligence (ICTAI)
65. Structural Generalization in Autonomous Cyber Incident Response with Message-Passing Neural Networks and Reinforcement Learning
    Jakob Nyberg, Pontus Johnson · 2024 · IEEE International Conference on Cyber Security and Resilience (CSR)
66. Machine Learning of Plan Robustness Knowledge About Instances
    Sergio Jiménez, Fernando Fernández, Daniel Borrajo · 2005 · Lecture notes in computer science
67. A Reward Optimization Method Based on Action Subrewards in Hierarchical Reinforcement Learning
    Yuchen Fu, Quan Liu, Xionghong Ling, Zhiming Cui · 2014 · The Scientific World JOURNAL
68. Graph Traversal Methods for Reasoning in Large Knowledge-Based Systems
    Abhishek B. Sharma, Kenneth D. Forbus · 2013 · Proceedings of the AAAI Conference on Artificial Intelligence
69. Reinforcement Learning with Markov Logic Networks
    Weiwei Wang, Yang Gao, Xingguo Chen, Shen Ge · 2008 · Lecture notes in computer science
70. IMPROVED REVISION OF RANKING FUNCTIONS FOR THE GENERALIZATION OF BELIEF IN THE CONTEXT OF UNOBSERVED VARIABLES
    Klaus Häming, Gabriele Peters · 2011 · International Conference on Neural Computation Theory and Applications
71. Improving Automated Planning with Machine Learning
    Susana Fernández, Sergio Jiménez Celorrio, Tomás de la Rosa · 2011 · IGI Global eBooks
72. Planning with Qualitative Models for Robotic Domains
    Timothy Wiley, Claude Sammut, Ivan Bratko A · 2015
73. Learning to Repair Plans and Schedules Using a Relational (Deictic) Representation
    Jorge Andrés Palombarini, Ernesto Carlos Martinez · 2009 · Computer-aided chemical engineering/Computer aided chemical engineering
74. Oracle-SAGE: Planning Ahead in Graph-Based Deep Reinforcement Learning
    Andrew Chester, Michael Dann, Fabio Zambetta, John Thangarajah · 2023 · Lecture notes in computer science
75. Relation learning in a neurocomputational architecture supports cross-domain transfer
    Leonidas A. A. Doumas, Guillermo Puebla, Andrea E. Martin, John E. Hummel · 2019 · MPG.PuRe (Max Planck Society)
76. Relational-grid-world: a novel relational reasoning environment and an agent model for relational information extraction
    Faruk Kucuksubasi, Elif Sürer · 2020 · TURKISH JOURNAL OF ELECTRICAL ENGINEERING & COMPUTER SCIENCES
77. Optimising learning with transferable prior information
    Funlade T. Sunmola · 2013 · University of Birmingham Institutional Research Archive (University of Birmingham)
78. Relative relevance of subsets of agent's knowledge
    Sławomir Nowaczyk, Jacek Malec · 2007
79. Algebraic results and bottom-up algorithm for policies generalization in reinforcement learning using concept lattices
    Marc Ricordeau, Michel Liquière · 2007 · Nonlinear Analysis Hybrid Systems
80. Développement autonome des comportements de base d'un agent
    Olivier Buffet, Alain Dutech, François Charpillet · 2005 · Revue d intelligence artificielle
81. Executable product models - The intelligent way
    Markus Kress, Detlef G. Seese · 2007 · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics
82. Learning and Reasoning for Robot Dialog and Navigation Tasks
    Keting Lu, Shiqi Zhang, Peter Stone, Xiaoping Chen · 2020 · arXiv (Cornell University)
83. Relational Learning
    Jan Struyf, Hendrik Blockeel · 2017 · Encyclopedia of Machine Learning and Data Mining
84. On a Reference Architecture to Build Deep‐Q Learning‐Based Intelligent IoT Edge Solutions
    Amit Chakraborty, Ankit Kumar Shaw, Sucharita Samanta · 2022
85. Learning Feature-Based Heuristic Functions
    Marek Petrik, Shlomo Zilberstein B · 2011 · Springer eBooks
86. Machine Learning and Reinforcement Learning
    Maarten van Someren, Stephan ten Hagen · 2005 · Studies in fuzziness and soft computing
87. A Gaussian Processes Reinforcement Learning Method in Large Discrete State Spaces
    Zhou Wen-yun, Quan Liu · 2009 · International Conference on Advanced Computer Control
88. Enhancing Inference in Relational Reinforcement Learning Via Truth Maintenance Systems
    Mandana Hamidi‐Haines, Amir Fijany, Jean‐Guy Fontaine · 2010 · International Conference on Machine Learning and Applications
89. States evolution in Θ(λ)-learning based on logical MDPs with negation
    Song Zhiwei, Chen Xiaoping · 2007 · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics
90. Achieving Human Level Reasoning and Decision-Making for Autonomous Systems: An Agent's Perspective
    Sitanath Biswas, Trilok N. Pandey, Sarada P. Pati · 2011
91. Safe learning agents? Logic-based agents!
    Eduardo Alonso · 2002
92. Biography and future challenges
    Stephen Muggleton, Luc De Raedt, David Poole, Ivan Bratko, Peter A. Flach, Katsumi Inoue, Ashwin Srinivasan · 2011
93. Learning an Optimal Sequence of Questions for the Disambiguation of Queries over Structured Data
    Achim Rettinger, Alexander Hagemann, Matthias Nickles · 2014 · National Conference on Artificial Intelligence
94. Relational Reinforcement Learning Applied to Appearance-Based Object Recognition
    Klaus Häming, Gabriele Peters · 2009 · Communications in computer and information science
95. Aprendizaje de Programas Teleo-Reactivos para Robotica Movil
    Blanca Alicia, Vargas Govea · 2009
96. Algebraic Reinforcement Learning
    Stefanie Neubert, Lenz Belzner, Martin Wirsing · 2015 · Lecture notes in computer science
97. Algebraic Reinforcement Learning - Hypothesis Induction for Relational Reinforcement Learning Using Term Generalization.
    Stefanie Neubert, Lenz Belzner, Martin Wirsing · 2015
98. Collaborative Online Learning of an Action Model
    Christophe Rodrigues, Henry Soldano, Gauvain Bourgne, Céline Rouveirol · 2016 · Lecture notes in computer science
99. Policies Generalization in Reinforcement Learning using Galois Partitions Lattices
    Marc Ricordeau, Michel Liquière · 2007 · HAL
100. Transferable dynamics models for efficient object-oriented reinforcement learning
     Ofir Marom, Benjamin Rosman · 2024 · Artificial Intelligence