The DOI matches its Crossref record; 66 of the 66 references it cites were found in a registry or an index.
Reinforcement Learning: An Introduction
Open accessAbstract
An account of key ideas and algorithms in reinforcement learning. The discussion ranges from the history of the field's intellectual foundations to recent developments and applications. Areas studied include reinforcement learning problems in terms of Markov decision problems and solution methods.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Genetic algorithms in search, optimization, and machine learning
(1989) · Choice Reviews Online · doi:10.5860/choice.27-0936 · Cited by 49,010
Learning Internal Representations by Error Propagation
Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1985) · doi:10.21236/ada164453 · Cited by 16,172
Bellman, R. E. (2010) · doi:10.1515/9781400835386 · Cited by 14,588
(1973) · CERN Document Server (European Organization for Nuclear Research) · Cited by 12,895
Varga, R. S. (2000) · Springer Series in Computational Mathematics · doi:10.1007/978-3-642-05156-2 · Cited by 5,811
Watkins, C. (1989) · OpenGrey (Institut de l'Information Scientifique et Technique) · doi:10.17863/cam.132164 · Cited by 5,297
Parallel and Distributed Computation: Numerical Methods
Bertsekas, D. P., & Tsitsiklis, J. N. (1989) · DSpace@MIT (Massachusetts Institute of Technology) · Cited by 5,293
Learning to predict by the methods of temporal differences
Sutton, R. S. (1988) · Machine Learning · doi:10.1007/bf00115009 · Cited by 5,148
Self-citationSome Studies in Machine Learning Using the Game of Checkers
Samuel, A. L. (1959) · IBM Journal of Research and Development · doi:10.1147/rd.33.0210 · Cited by 4,443
WIDROW, B., & HOFF, M. E. (1960) · doi:10.21236/ad0241531 · Cited by 4,009
Cited by
Items 401–500 of 687
Glavic, M. (2019) · Annual Reviews in Control · doi:10.1016/j.arcontrol.2019.09.008 · Cited by 163
Pezzulo, G., Rigoli, F., & Chersi, F. (2013) · Frontiers in Psychology · doi:10.3389/fpsyg.2013.00092 · Cited by 163
A Self-Learning Call Admission Control Scheme for CDMA Cellular Networks
Liu, D., Zhang, Y., & Zhang, H. (2005) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2005.853408 · Cited by 162
Assessing inter individual differences with task related functional neuroimaging
Lebreton, M., Bavard, S., Daunizeau, J., & Palminteri, S. (2019) · Nature Human Behaviour · doi:10.1038/s41562-019-0681-8 · Cited by 160
A Survey on Policy Search Algorithms for Learning Robot Controllers in a Handful of Trials
Chatzilygeroudis, K., Vassiliades, V., Stulp, F., Calinon, S., & Mouret, J.-B. (2020) · IEEE Transactions on Robotics · doi:10.1109/tro.2019.2958211 · Cited by 160
Deep Reinforcement Learning based Resource Allocation in Low Latency Edge Computing Networks
Yang, T., Hu, Y., Gursoy, M. C., Schmeink, A., & Mathar, R. (2018) · 2018 15th International Symposium on Wireless Communication Systems (ISWCS) · doi:10.1109/iswcs.2018.8491089 · Cited by 156
Learning Radio Resource Management in RANs: Framework, Opportunities, and Challenges
Calabrese, F. D., Wang, L., Ghadimi, E., Peters, G., Hanzo, L., & Soldati, P. (2018) · IEEE Communications Magazine · doi:10.1109/mcom.2018.1701031 · Cited by 154
Reinforcement learning and decision making in monkeys during a competitive game
Lee, D., Conroy, M. L., McGreevy, B. P., & Barraclough, D. J. (2004) · Cognitive Brain Research · doi:10.1016/j.cogbrainres.2004.07.007 · Cited by 156
Behavioural and neural evidence for self-reinforcing expectancy effects on pain
Jepma, M., Koban, L., van Doorn, J., Jones, M., & Wager, T. D. (2018) · Nature Human Behaviour · doi:10.1038/s41562-018-0455-8 · Cited by 155
Bengio, Y. (2017) · arXiv · doi:10.48550/arxiv.1709.08568 · Cited by 155
Dopamine, uncertainty and TD learning
Niv, Y., Duff, M. O., & Dayan, P. (2005) · Behavioral and Brain Functions · doi:10.1186/1744-9081-1-6 · Cited by 153
Programming backgammon using self-teaching neural nets
Tesauro, G. (2002) · Artificial Intelligence · doi:10.1016/s0004-3702(01)00110-2 · Cited by 153
Chu, M., Li, H., Liao, X., & Cui, S. (2019) · IEEE Internet of Things Journal · doi:10.1109/jiot.2018.2872440 · Cited by 153
Safe Exploration in Markov Decision Processes
Moldovan, T. M., & Abbeel, P. (2012) · arXiv · doi:10.48550/arxiv.1205.4810 · Cited by 152
Dynamic Zoom-in Network for Fast Object Detection in Large Images
Gao, M., Yu, R., Li, A., Morariu, V. I., & Davis, L. S. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00724 · Cited by 148
Prediction error, ketamine and psychosis: An updated model
Corlett, P. R., Honey, G. D., & Fletcher, P. C. (2016) · Journal of Psychopharmacology · doi:10.1177/0269881116650087 · Cited by 142
The human Turing machine: a neural framework for mental programs
Zylberberg, A., Dehaene, S., Roelfsema, P. R., & Sigman, M. (2011) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2011.05.007 · Cited by 151
The Rhetoric and Reality of Anthropomorphism in Artificial Intelligence
Watson, D. (2019) · Minds and Machines · doi:10.1007/s11023-019-09506-6 · Cited by 148
Teaching brain-machine interfaces as an alternative paradigm to neuroprosthetics control
Iturrate, I., Chavarriaga, R., Montesano, L., Minguez, J., & Millán, J. D. R. (2015) · Scientific Reports · doi:10.1038/srep13893 · Cited by 149
Learning from Action: Reconsidering Movement Signaling in Midbrain Dopamine Neuron Activity
Coddington, L. T., & Dudman, J. T. (2019) · Neuron · doi:10.1016/j.neuron.2019.08.036 · Cited by 150
Habits as action sequences: hierarchical action control and changes in outcome value
Dezfouli, A., Lingawi, N. W., & Balleine, B. W. (2014) · Philosophical Transactions of the Royal Society B: Biological Sciences · doi:10.1098/rstb.2013.0482 · Cited by 139
Stimulus representation and the timing of reward-prediction errors in models of the dopamine system
Ludvig, E. A., Sutton, R. S., & Kehoe, E. J. (2008) · Neural Computation · doi:10.1162/neco.2008.11-07-654 · Cited by 148
Self-citationDynamic Signals Related to Choices and Outcomes in the Dorsolateral Prefrontal Cortex
Seo, H., Barraclough, D. J., & Lee, D. (2007) · Cerebral Cortex · doi:10.1093/cercor/bhm064 · Cited by 143
Contraction theory for nonlinear stability analysis and learning-based control: A tutorial overview
Tsukamoto, H., Chung, S.-J., & Slotine, J.-J. E. (2021) · Annual Reviews in Control · doi:10.1016/j.arcontrol.2021.10.001 · Cited by 145
Social Cognition 2.0: An Interactive Memory Systems Account
Amodio, D. M. (2019) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2018.10.002 · Cited by 145
Anterior hippocampus and goal-directed spatial decision making
Viard, A., Doeller, C. F., Hartley, T., Bird, C. M., & Burgess, N. (2011) · The Journal of Neuroscience · doi:10.1523/jneurosci.4640-10.2011 · Cited by 144
Troyer, T. W., & Doupe, A. J. (2000) · Journal of Neurophysiology · doi:10.1152/jn.2000.84.3.1204 · Cited by 145
Reinforcement Learning in Autonomic Computing: A Manifesto and Case Studies
Tesauro, G. (2007) · IEEE Internet Computing · doi:10.1109/mic.2007.21 · Cited by 143
Thinking Inside the Box: Controlling and Using an Oracle AI
Armstrong, S., Sandberg, A., & Bostrom, N. (2012) · Minds and Machines · doi:10.1007/s11023-012-9282-2 · Cited by 144
The feeling of grip: novelty, error dynamics, and the predictive brain
Kiverstein, J., Miller, M., & Rietveld, E. (2019) · Synthese · doi:10.1007/s11229-017-1583-9 · Cited by 143
Deep Reinforcement Learning at the Edge of the Statistical Precipice
Agarwal, R., Schwarzer, M., Castro, P. S., Courville, A., & Bellemare, M. G. (2021) · arXiv · doi:10.48550/arxiv.2108.13264 · Cited by 142
Bayesian inferences about the self (and others): a review.
Moutoussis, M., Fearon, P., El-Deredy, W., Dolan, R. J., & Friston, K. J. (2014) · Consciousness and Cognition · doi:10.1016/j.concog.2014.01.009 · Cited by 141
Confidence matching in group decision-making
Bang, D., Aitchison, L., Moran, R., Herce Castanon, S., Rafiee, B., Mahmoodi, A., Lau, J. Y. F., Latham, P. E., Bahrami, B., & Summerfield, C. (2017) · Nature Human Behaviour · doi:10.1038/s41562-017-0117 · Cited by 141
(2021) · UCL Discovery (University College London) · Cited by 141
Agency and the Calibration of Motivated Behavior
Moscarello, J. M., & Hartley, C. A. (2017) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2017.06.008 · Cited by 137
Multi-Agent Safe Policy Learning for Power Management of Networked Microgrids
Zhang, Q., Dehghanpour, K., Wang, Z., Qiu, F., & Zhao, D. (2021) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2020.3034827 · Cited by 137
Adaptive effort investment in cognitive and physical tasks: A neurocomputational model
Verguts, T., Vassena, E., & Silvetti, M. (2015) · Frontiers in Behavioral Neuroscience · doi:10.3389/fnbeh.2015.00057 · Cited by 118
Role of Dopamine D2 Receptors in Human Reinforcement Learning
Eisenegger, C., Naef, M., Linssen, A., Clark, L., Gandamaneni, P. K., Müller, U., & Robbins, T. W. (2014) · Neuropsychopharmacology · doi:10.1038/npp.2014.84 · Cited by 134
Artificial intelligence and the value of transparency
Walmsley, J. (2021) · AI & SOCIETY · doi:10.1007/s00146-020-01066-z · Cited by 131
Zhu, L., Mathewson, K. E., & Hsu, M. (2012) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1116783109 · Cited by 125
Wirth, S., Baraduc, P., Planté, A., Pinède, S., & Duhamel, J.-R. (2017) · PLOS Biology · doi:10.1371/journal.pbio.2001045 · Cited by 129
Markov decision processes with delays and asynchronous cost collection
Katsikopoulos, K. V., & Engelbrecht, S. E. (2003) · IEEE Transactions on Automatic Control · doi:10.1109/tac.2003.809799 · Cited by 129
Neuronal message passing using mean-field, bethe, and marginal approximations
Parr, T., Markovic, D., Kiebel, S. J., & Friston, K. J. (2019) · Scientific Reports · doi:10.1038/s41598-018-38246-3 · Cited by 128
Lateral Intraparietal Cortex and Reinforcement Learning during a Mixed-Strategy Game
Seo, H., Barraclough, D. J., & Lee, D. (2009) · The Journal of Neuroscience · doi:10.1523/jneurosci.1479-09.2009 · Cited by 125
ILP turns 20: Biography and future challenges
Muggleton, S., De Raedt, L., Poole, D., Bratko, I., Flach, P., Inoue, K., & Srinivasan, A. (2012) · Machine Learning · doi:10.1007/s10994-011-5259-2 · Cited by 126
Ziegler, S., Pedersen, M. L., Mowinckel, A. M., & Biele, G. (2016) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2016.09.002 · Cited by 110
Social rewards and social networks in the human brain
Fareri, D. S., & Delgado, M. R. (2014) · The Neuroscientist · doi:10.1177/1073858414521869 · Cited by 124
Minimax Regret Bounds for Reinforcement Learning
Azar, M. G., Osband, I., & Munos, R. (2017) · arXiv · doi:10.48550/arxiv.1703.05449 · Cited by 124
De Novo Design of New Chemical Entities for SARS-CoV-2 Using Artificial Intelligence
Bung, N., Krishnan, S. R., Bulusu, G., & Roy, A. (2021) · Future Medicinal Chemistry · doi:10.4155/fmc-2020-0262 · Cited by 118
A Deep Actor-Critic Reinforcement Learning Framework for Dynamic Multichannel Access
Zhong, C., Lu, Z., Gursoy, M. C., & Velipasalar, S. (2019) · IEEE Transactions on Cognitive Communications and Networking · doi:10.1109/tccn.2019.2952909 · Cited by 123
Human development of the ability to learn from bad news
Moutsiana, C., Garrett, N., Clarke, R. C., Lotto, R. B., Blakemore, S.-J., & Sharot, T. (2013) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1305631110 · Cited by 119
Scalable agent alignment via reward modeling: a research direction
Leike, J., Krueger, D., Everitt, T., Martic, M., Maini, V., & Legg, S. (2018) · arXiv · doi:10.48550/arxiv.1811.07871 · Cited by 121
Distributed Deep Reinforcement Learning for Intelligent Load Scheduling in Residential Smart Grids
Chung, H.-M., Maharjan, S., Zhang, Y., & Eliassen, F. (2021) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2020.3007167 · Cited by 120
Iigaya, K., Fonseca, M. S., Murakami, M., Mainen, Z. F., & Dayan, P. (2018) · Nature Communications · doi:10.1038/s41467-018-04840-2 · Cited by 120
Toward an integration of deep learning and neuroscience
Marblestone, A. H., Wayne, G., & Kording, K. P. (2016) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2016.00094 · Cited by 119
Evolution and impact of bias in human and machine learning algorithm interaction
Sun, W., Nasraoui, O., & Shafto, P. (2020) · PLOS ONE · doi:10.1371/journal.pone.0235502 · Cited by 116
The brain's connective core and its role in animal cognition
Shanahan, M. (2012) · Philosophical Transactions of the Royal Society B: Biological Sciences · doi:10.1098/rstb.2012.0128 · Cited by 115
Predictive maps in rats and humans for spatial navigation
de Cothi, W., Nyberg, N., Griesbauer, E.-M., Ghanamé, C., Zisch, F., Lefort, J. M., Fletcher, L., Newton, C., Renaudineau, S., Bendor, D., et al. (2022) · Current Biology · doi:10.1016/j.cub.2022.06.090 · Cited by 109
The successor representation and temporal context
Gershman, S. J., Moore, C. D., Todd, M. T., Norman, K. A., & Sederberg, P. B. (2012) · Neural Computation · doi:10.1162/neco_a_00282 · Cited by 117
A role for descending auditory cortical projections in songbird vocal learning
Mandelblat-Cerf, Y., Las, L., Denisenko, N., & Fee, M. S. (2014) · eLife · doi:10.7554/elife.02152 · Cited by 117
Reward learning from human preferences and demonstrations in Atari
Ibarz, B., Leike, J., Pohlen, T., Irving, G., Legg, S., & Amodei, D. (2018) · arXiv · doi:10.48550/arxiv.1811.06521 · Cited by 117
TAMER: Training an Agent Manually via Evaluative Reinforcement
Bradley Knox, W., & Stone, P. (2008) · 2008 7th IEEE International Conference on Development and Learning · doi:10.1109/devlrn.2008.4640845 · Cited by 117
Catecholaminergic Regulation of Learning Rate in a Dynamic Environment
Jepma, M., Murphy, P. R., Nassar, M. R., Rangel-Gomez, M., Meeter, M., & Nieuwenhuis, S. (2016) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1005171 · Cited by 91
Haarsma, J., Fletcher, P. C., Griffin, J. D., Taverne, H. J., Ziauddeen, H., Spencer, T. J., Miller, C., Katthagen, T., Goodyer, I., Diederen, K. M. J., et al. (2021) · Molecular Psychiatry · doi:10.1038/s41380-020-0803-8 · Cited by 108
The Anterior Cingulate Gyrus Signals the Net Value of Others' Rewards
Apps, M. A. J., & Ramnani, N. (2014) · The Journal of Neuroscience · doi:10.1523/jneurosci.2701-13.2014 · Cited by 109
Assessing Generalization in Deep Reinforcement Learning
Packer, C., Gao, K., Kos, J., Krähenbühl, P., Koltun, V., & Song, D. (2018) · arXiv · doi:10.48550/arxiv.1810.12282 · Cited by 114
Turel, O., Cavagnaro, D. R., & Meshi, D. (2018) · Psychiatry Research · doi:10.1016/j.psychres.2018.11.017 · Cited by 113
Social rewards: From basic social building blocks to complex social behavior
Tamir, D. I., & Hughes, B. L. (2018) · Perspectives on Psychological Science · doi:10.1177/1745691618776263 · Cited by 109
Brown, V. M., Zhu, L., Solway, A., Wang, J. M., McCurry, K. L., King-Casas, B., & Chiu, P. H. (2021) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2021.1844 · Cited by 112
Optimizing walking controllers for uncertain inputs and environments
Wang, J. M., Fleet, D. J., & Hertzmann, A. (2010) · ACM Transactions on Graphics · doi:10.1145/1778765.1778810 · Cited by 113
Sætra, H. S. (2020) · Technology in Society · doi:10.1016/j.techsoc.2020.101283 · Cited by 111
Automatic ad format selection via contextual bandits
Tang, L., Rosales, R., Singh, A., & Agarwal, D. (2013) · Proceedings of the 22nd ACM international conference on Information & Knowledge Management · doi:10.1145/2505515.2514700 · Cited by 111
Model-based approaches to neuroimaging: combining reinforcement learning theory with fMRI data
Gläscher, J. P., & O'Doherty, J. P. (2010) · WIREs Cognitive Science · doi:10.1002/wcs.57 · Cited by 104
Coordinated energy management for a cluster of buildings through deep reinforcement learning
Pinto, G., Piscitelli, M. S., Vázquez-Canteli, J. R., Nagy, Z., & Capozzoli, A. (2021) · Energy · doi:10.1016/j.energy.2021.120725 · Cited by 108
Gödel Machines: Fully Self-referential Optimal Universal Self-improvers
Schmidhuber, J. (2007) · Cognitive Technologies · doi:10.1007/978-3-540-68677-4_7 · Cited by 110
Design and evaluation of learning algorithms for dynamic resource management in virtual networks
Mijumbi, R., Gorricho, J.-L., Serrat, J., Claeys, M., De Turck, F., & Latre, S. (2014) · 2014 IEEE Network Operations and Management Symposium (NOMS) · doi:10.1109/noms.2014.6838258 · Cited by 109
Adaptive coding of action values in the human rostral cingulate zone
Jocham, G., Neumann, J., Klein, T. A., Danielmeier, C., & Ullsperger, M. (2009) · Journal of Neuroscience · doi:10.1523/jneurosci.0349-09.2009 · Cited by 106
Remani, T., Jasmin, E. A., & Ahamed, T. P. I. (2019) · IEEE Systems Journal · doi:10.1109/jsyst.2018.2855689 · Cited by 106
Overcoming the bootstrap problem in evolutionary robotics using behavioral diversity
Mouret, J.-B., & Doncieux, S. (2009) · 2009 IEEE Congress on Evolutionary Computation · doi:10.1109/cec.2009.4983077 · Cited by 106
Predictive Coding: a Theoretical and Experimental Review
Millidge, B., Seth, A., & Buckley, C. L. (2021) · arXiv · doi:10.48550/arxiv.2107.12979 · Cited by 106
Likmeta, A., Metelli, A. M., Tirinzoni, A., Giol, R., Restelli, M., & Romano, D. (2020) · Robotics and Autonomous Systems · doi:10.1016/j.robot.2020.103568 · Cited by 106
Meta Reinforcement Learning with Latent Variable Gaussian Processes
Sæmundsson, S., Hofmann, K., & Deisenroth, M. P. (2018) · arXiv · doi:10.48550/arxiv.1803.07551 · Cited by 106
Using fMRI to study reward processing in humans: past, present, and future
Wang, K. S., Smith, D. V., & Delgado, M. R. (2016) · Journal of Neurophysiology · doi:10.1152/jn.00333.2015 · Cited by 105
Updating beliefs under perceived threat
Garrett, N., González-Garzón, A. M., Foulkes, L., Levita, L., & Sharot, T. (2018) · The Journal of Neuroscience · doi:10.1523/jneurosci.0716-18.2018 · Cited by 109
Asaad, W. F., & Eskandar, E. N. (2011) · The Journal of Neuroscience · doi:10.1523/jneurosci.3793-11.2011 · Cited by 105
A postdecisional neural marker of confidence predicts information-seeking in decision-making
Desender, K., Murphy, P., Boldt, A., Verguts, T., & Yeung, N. (2019) · The Journal of Neuroscience · doi:10.1523/jneurosci.2620-18.2019 · Cited by 88
Social-ecological interactions, management panaceas, and the future of wild fish populations
van Poorten, B. T., Arlinghaus, R., Daedlow, K., & Haertel-Borer, S. S. (2011) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1013919108 · Cited by 104
Machine learning for spoken dialogue systems
Lemon, O., & Pietquin, O. (2007) · Interspeech 2007 · doi:10.21437/interspeech.2007-705 · Cited by 104
Neurodynamics of mental exploration
Hopfield, J. J. (2010) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0913991107 · Cited by 96
Belief state representation in the dopamine system
Babayan, B. M., Uchida, N., & Gershman, S. J. (2018) · Nature Communications · doi:10.1038/s41467-018-04397-0 · Cited by 101
Koch, K., Schachtzabel, C., Wagner, G., Schikora, J., Schultz, C., Reichenbach, J. R., Sauer, H., & Schlösser, R. G. M. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2009.12.031 · Cited by 101
Cooper, J. C., Dunne, S., Furey, T., & O'Doherty, J. P. (2012) · Journal of Cognitive Neuroscience · doi:10.1162/jocn_a_00114 · Cited by 101
System Design Perspective for Human-Level Agents Using Deep Reinforcement Learning: A Survey
Nguyen, N. D., Nguyen, T., & Nahavandi, S. (2017) · IEEE Access · doi:10.1109/access.2017.2777827 · Cited by 100
A review on the self and dual interactions between machine learning and optimisation
Song, H., Triguero, I., & Özcan, E. (2019) · Progress in Artificial Intelligence · doi:10.1007/s13748-019-00185-z · Cited by 101
Intelligence Through Interaction: Towards a Unified Theory for Learning
Tan, A.-H., Carpenter, G. A., & Grossberg, S. (2007) · Lecture Notes in Computer Science · doi:10.1007/978-3-540-72383-7_128 · Cited by 101
Morales, E. F., Murrieta-Cid, R., Becerra, I., & Esquivel-Basaldua, M. A. (2021) · Intelligent Service Robotics · doi:10.1007/s11370-021-00398-z · Cited by 101
Context, attention, and the switch between habit and goal-direction in behavior
Bouton, M. E. (2021) · Learning & Behavior · doi:10.3758/s13420-021-00488-z · Cited by 100
Enhancing intelligent agents with episodic memory
Nuxoll, A. M., & Laird, J. E. (2012) · Cognitive Systems Research · doi:10.1016/j.cogsys.2011.10.002 · Cited by 99
Levy, I., & Schiller, D. (2021) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2020.11.007 · Cited by 95
(2007) · National Conference on Artificial Intelligence · Cited by 98
Metadata
- Venue
- IEEE Transactions on Neural Networks
- Published
- 2005-01
- Volume
- 16
- Issue
- 1
- Pages
- 285-286
- ISSN
- 1045-9227 (ISSN-L)
- OpenAlex
- W2121863487
- Cited by
- 24,496
- References
- 66
- FWCI
- 971.52
- Type
- journal article
- Language
- en
- Open access
- gold
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
