2021

  • L. Ai, S.H. Muggleton, C. Hocquette, M. Gromowski, and U. Schmid. . Machine Learning, 2021.
  • S. Patsantzis and S.H. Muggleton. . Machine Learning, 2021.
  • Stuart Russell, Human-Compatible Artificial Intelligence, In S. Muggleton & N. Chater (Eds.), Human-like machine intelligence. Oxford University Press, 2021
  • Peter Millican, Alan Turing and Human-Like Intelligence, In S. Muggleton & N. Chater (Eds.), Human-like machine intelligence. Oxford University Press, 2021
  • Nick Chater and Jennifer Misyak, Spontaneous Communicative Conventions through Virtual Bargaining, In S. Muggleton & N. Chater (Eds.), Human-like machine intelligence. Oxford University Press, 2021
  • Alan Bundy, Eugene Philalithis, and Xue Li, Modelling Virtual Bargaining using Logical Representation Change, In S. Muggleton & N. Chater (Eds.), Human-like machine intelligence. Oxford University Press, 2021
  • Oana Cocarascu, Kristijonas Cyras, Antonio Rago, and Francesca Toni, Mining Property-driven Graphical Explanations for Data-centric AI from Argumentation Frameworks, In S. Muggleton & N. Chater (Eds.), Human-like machine intelligence. Oxford University Press, 2021
  • Marko Tesic and Ulrike Hahn, Explanation in AI systems, In S. Muggleton & N. Chater (Eds.), Human-like machine intelligence. Oxford University Press, 2021
  • Patrick Healey, Human-like Communication, In S. Muggleton & N. Chater (Eds.), Human-like machine intelligence. Oxford University Press, 2021
  • Rose Wang, Sarah Wu, James Evans, David Parkes, Joshua Tenenbaum, and Max Kleiman-Weiner, Too Many cooks: Bayesian inference for coordinating Multi-agent Collaboration, In S. Muggleton & N. Chater (Eds.), Human-like machine intelligence. Oxford University Press, 2021
  • Jose Hernandez-Orallo and Cesar Ferri, Teaching and Explanation: Aligning Priors between Machines and Humans, In S. Muggleton & N. Chater (Eds.), Human-like machine intelligence. Oxford University Press, 2021
  • Stephen Muggleton and Wang-Zhou Dai, Human-like Computer Vision, In S. Muggleton & N. Chater (Eds.), Human-like machine intelligence. Oxford University Press, 2021
  • Richard Evans, Apperception, In S. Muggleton & N. Chater (Eds.), Human-like machine intelligence. Oxford University Press, 2021
  • Alaa Alahmadi, Alan Davies, Markel Vigo, Katherine Dempsey, and Caroline Jay, Human–Machine Perception of Complex Signal Data, In S. Muggleton & N. Chater (Eds.), Human-like machine intelligence. Oxford University Press, 2021
  • Martin Pickering and Simon Garrod, The Shared-Workspace Framework for Dialogue and Other Cooperative Joint Activities, In S. Muggleton & N. Chater (Eds.), Human-like machine intelligence. Oxford University Press, 2021
  • Beata Grzyb and Gabriella Vigliocco, Beyond Robotic Speech Mutual Benefits to Cognitive Psychology and Artificial Intelligence from the Study of Multimodal Communication, In S. Muggleton & N. Chater (Eds.), Human-like machine intelligence. Oxford University Press, 2021
  • Alireza Tamaddoni-Nezhad, David Bohan, Ghazal Afroozi Milani, Alan Raybould, and Stephen Muggleton, Human–Machine Scientific Discovery, In S. Muggleton & N. Chater (Eds.), Human-like machine intelligence. Oxford University Press, 2021
  • Denis Mareschal and Sam Blakeman, Fast and Slow Learning in Human-Like Intelligence, In S. Muggleton & N. Chater (Eds.), Human-like machine intelligence. Oxford University Press, 2021
  • Ute Schmid, Interactive Learning with Mutual Explanations in Relational Domains, In S. Muggleton & N. Chater (Eds.), Human-like machine intelligence. Oxford University Press, 2021
  • Mateja Jamnik and Peter Cheng, Endowing machines with the expert human ability to select representations: why and how, In S. Muggleton & N. Chater (Eds.), Human-like machine intelligence. Oxford University Press, 2021
  • Cle虂Ment Gautrais, Yann Dauxais, Stefano Teso, Samuel Kolb, Gust Verbruggen, and Luc De Raedt, Human–Machine Collaboration for Democratizing Data Science, In S. Muggleton & N. Chater (Eds.), Human-like machine intelligence. Oxford University Press, 2021
  • Brandon Bennett and Anthony Cohn, Automated Common-sense Spatial Reasoning: Still a Huge Challenge, In S. Muggleton & N. Chater (Eds.), Human-like machine intelligence. Oxford University Press, 2021
  • Adam Sanborn, Jian-Qiao Zhu, Jake Spicer, Joakim Sundh, Pablo Leo虂n-Villagra虂, and Nick Chater, Sampling as the Human Approximation to Probabilistic Inference, In S. Muggleton & N. Chater (Eds.), Human-like machine intelligence. Oxford University Press, 2021
  • Katya Tentori, What Can the Conjunction Fallacy Conjunction Tell Us about Human Reasoning?, In S. Muggleton & N. Chater (Eds.), Human-like machine intelligence. Oxford University Press, 2021
  • Evans, R., et al. (2021). “Making sense of sensory input.” Artificial Intelligence 293 (2021): 103438.
  • Claude Sammut, Reza Farid, Handy Wicaksono, and Timothy Wiley, Logic-based Robotics, In S. Muggleton & N. Chater (Eds.), Human-like machine intelligence. Oxford University Press, 2021
  • Ivan Bratko, Dayana Hristova, and Matej Guid, Predicting Problem Difficulty in Chess, In S. Muggleton & N. Chater (Eds.), Human-like machine intelligence. Oxford University Press, 2021
  • Konstantina Spanaki, Erisa Karafili, Stella Despoudi “AI Applications of Data Sharing in Agriculture 4.0: A Framework for Role-based Data Access Control” to appear at the International Journal of Information Management, Elsevier, 2021.

  • Konstantina Spanaki, Erisa Karafili, Uthayasankar Sivarajah, Stella Despoudi, Zahir Irani “Artificial intelligence and food security: swarm intelligence of AgriTech drones for smart AgriFood operations” in Journal of Production Planning & Control, Taylor & Francis, 2021. 

2020

  • A. Cropper, S. Dumancic, and S.H. Muggleton. . In Proceedings of the 34th Conference on Artificial Intelligence (AAAI 2020), pages 13655-13658. AAAI, 2020.
  • A. Cropper, S. Dumancic, and S.H. Muggleton. . In Proceedings of the 29th International Joint Conference Artificial Intelligence (IJCAI 2020), pages 4833-4839. IJCAI, 2020.
  • A. Cropper, R. Morel, and S.H. Muggleton. . Machine Learning, 109:1289-1322, 2020.
  • C. Hocquette and S.H. Muggleton. . In Proceedings of the 29th International Joint Conference Artificial Intelligence (IJCAI 2020), pages 2312-2318. IJCAI, 2020.
  • Varghese D, Tamaddoni-Nezhad A. One-shot rule learning for challenging character recognition. Proc. of the 14th Intl. Rule Challenge (RuleML).2644:10-27, 2020
  • Oaksford, M. and Chater, N. ", Annual Review of Psychology, 71, 1, 305-330, 2020
  • Chater, N., Zhu, J., Spicer, J., Sundh, J., León-Villagrá, P. and Sanborn, A. N. "Probabilistic biases meet the Bayesian brain", Current Directions in Psychological Science, 2020
  • Chater, N. and Vlaev, I. "The fragmentation of vision", Leonardo, 2020
  • Heyes, C., Chater, N. and Dwyer, D. "Sinking in : the peripheral Baldwinisation of human cognition", Trends in Cognitive Sciences, 2020
  • Zhu, J., Sanborn, A. N. and Chater, N. "", Psychological Review, 127, 5, 719-748, 2020
  • Quesada Real, F., ,  &  , Proceedings of the Fifteenth International Workshop on Ontology Matching. CEUR-WS.org, 12 p., 2020
  • Urbonas, M., ,  & Li, X., , Proceedings of the Fortieth SGAI International Conference on Artificial Intelligence (AI 2020). Springer, Cham, 14 p., 2020
  • Numah, K., Bundy, A.,   Proceedings of the 24th European Conference on Artificial Intelligence. 8 p., 2020
  • Regis Riveret, Son Tran and Artur d'Avila Garcez. Neuro-Symbolic Probabilistic Argumentation Machines. In Proc. 17th International Conference on Principles of Knowledge Representation and Reasoning (KR2020), Rhodes, Greece, 2020.
  • Kwun Ho Ngan, Artur d'Avila Garcez, Karen M. Knapp, Andy Appelboam, Constantino Carlos Reyes-Aldasoro. A Machine Learning Approach for Colles Fracture Treatment Diagnosis. In Proceedings of the 24th UK Conference on Medical Image Understanding and Analysis (MIUA'2020), University of Oxford, Oxford, UK, 2020.
  • Luis C. Lamb, Artur d'Avila Garcez, Marco Gori, Marcelo O.R. Prates, Pedro H.C. Avelar and Moshe Y. Vardi. Graph Neural Networks Meet Neural-Symbolic Computing: A Survey and Perspective. In Proc. IJCAI 2020, Yokohama, Japan, 2020.
  • C. Charitou, A. d'Avila Garcez and S. Dragicevic. Semi-supervised GANs for Fraud Detection. In Proc. IEEE International Joint Conference on Neural Networks, IJCNN 2020, Glasgow, UK, 2020.
  • A. White and A. d'Avila Garcez. Measurable Counterfactual Local Explanations for Any Classifier. In Proc. 24th European Conference on Artificial Intelligence (ECAI 2020), Santiago de Compostela, Spain, 2020.
  • Luis C. Lamb, Artur d'Avila Garcez, Marco Gori, Marcelo O.R. Prates, Pedro H.C. Avelar and Moshe Y. Vardi. Graph Neural Networks Meet Neural-Symbolic Computing: A Survey and Perspective. In Proc. IJCAI 2020, Yokohama, Japan, 2020.
  • Ellis, K, Oliver, C, Stefanidou, C, Apperly, I & Moss, J, '', Journal of Autism and Developmental Disorders, 2020
  • Abu-Akel, AM, Apperly, IA, Wood, SJ & Hansen, PC , '', Royal Society of London. Proceedings B. Biological Sciences, vol. 287, no. 1925, 20200244. 2020
  • Ellis, K, Lewington, P, Powis, L, Oliver, C, Waite, J, Heald, M, Apperly, I, Sandhu, P & Crawford, H, '', Journal of Autism and Developmental Disorders. 2020
  • Wang, JJ, Ciranova, N, Woods, B & Apperly, IA , '', PLoS ONE, vol. 15, no. 10, pp. e0240521. 2020
  • Collins, P.J. and Krzyzanowska, K. and Hartmann, S. and Wheeler, G. and Hahn, Ulrike Cognitive Psychology 122 (101329), ISSN 0010-0285, 2020 
  • Collins, P. J. and Hahn, U. (2020). We might be wrong, but we think that hedging doesn’t protect your reputation. Journal of Experimental Psychology. Learning, Memory, and Cognition, 46(7), 1328.
  • Cocarascu,O., Stylianou,A., Cyras, K.et al.(2020). Data-empoweredargumentationfor dialectically explainable predictions, in ECAI 2020—24th European Conference on Artificial Intelligence, Santiago de Compostela, Spain, 10–12 June 2020.
  • Pilditch, T.D. and Hahn, Ulrike and Fenton, N. and Lagnado, D.A.  Cognition 204 (104343), ISSN 0010-0277. 2020
  • Hahn, Ulrike Trends in Cognitive Sciences 24 (5), pp. 363-374. ISSN 1364-6613. 2020
  • Hahn, Ulrike and Hansen, J.U. and Olsson, E.J.  Synthese 197 , pp. 1511-1541. ISSN 0039-7857. 2020
  • Madsen, J.K. and Hahn, Ulrike and Pilditch, T.D.  Journal of Experimental Psychology: Learning, Memory, and Cognition , ISSN 0278-7393. 2020
  • Merdes, C. and von Sydow, M. and Hahn, Ulrike Synthese , ISSN 0039-7857. 2020 

2019

  • A. Cropper and S.H. Muggleton. . Machine Learning, 108:1063-1083, 2019.
  • S.H. Muggleton and C. Hocquette. . New Generation Computing, 37:203-217, 2019.
  • Dai, W.-Z., Xu, Q., Yu, Y. et al. (2019). Bridging machine learning and logical reasoning by abductive learning, in edited by: H. Wallach and H. Larochelle and A. Beygelzimer and F. d’Alche虂-Buc and E. Fox and R. Garnett. Advances in Neural Information Processing Systems 32 (NeurIPS 2019), Curran Associates, Inc., Red Hook, New York.
  • Chater, N., Misyak, J., Ritchie, O., Watson, D. G., Griffiths, N., Xu, Z. and Mouzakitis, A. "", Physics of Life Reviews, 28, 31-33, 2019
  • Ritchie, O. T., Watson, D. G., Griffiths, N., Misyak, J. B., Chater, N., Xu, Z. and Mouzakitis, A. "", Transportation Research Part F: Psychology and Behaviour, 66, 406-418, 2019
  • Son Tran, Artur d'Avila Garcez, Tillman Weyde, Qing Zhang, Mohan Karunanithi, Jie Yin. Sequence Classification Restricted Boltzmann Machines with Gated Units. IEEE Transactions on Neural Networks and Learning Systems, 2019.
  • D. Philps, T. Weyde and A. d'Avila Garcez. Making Good on LSTMs Unfulfilled Promise. Thirty-third Conference on Neural Information Processing Systems (NeurIPS 2019), Workshop on Robust AI in Financial Services: Data, Fairness, Explainability, Trustworthiness, and Privacy, Vancouver, Canada, 2019.
  • S. Odense and A. d'Avila Garcez. Layerwise Knowledge Extraction from Deep Convolutional Networks. Thirty-third Conference on Neural Information Processing Systems (NeurIPS 2019), Knowledge Representation and Reasoning Meets Machine Learning Workshop, Vancouver, Canada, 2019.
  • S. Dragicevic, A. d'Avila Garcez, C. Percy and S. Sarkar. Understanding the Risk Profile of Gambling Behaviour through Machine Learning Predictive Modelling and Explanation. Thirty-third Conference on Neural Information Processing Systems (NeurIPS 2019), Knowledge Representation and Reasoning Meets Machine Learning Workshop, Vancouver, Canada, 2019.
  • Cutting, N, Apperly, I, Chappell, J & Beck, S , '', Cognitive Development, vol. 52, 100811. 2019
  • Apperly, IA , '', Trends in Cognitive Sciences, vol. 23, no. 6, pp. 451-453. 2019
  • Theodorou, L., Healey, P. G. T., and Smeraldi, F. (2019). Engaging with contemporary dance: What can body movements tell us about audience responses? Frontiers in Psychology, 10, 71.
  • Tes虒ic, M. and Hahn, U. (2019). Sequential diagnostic reasoning with independent causes, in Proceedings of the 41th Annual Conference of the Cognitive Science Society. Red Hook, NY: Curran Associates, 2947–53.
  • Cocarascu, O., Rago, A., and Toni, F. (2019). Dialogical Explanations for review aggregations with argumentative dialogical agents, in Proceedings of the 18th International Conference on Autonomous Agents and MultiAgent Systems, 13–17 May, Montreal.
  • Baroni, P., Rago, A., and Toni, F. (2019). From fine-grained properties to broad principles for grad- ual argumentation: a principled spectrum. International Journal of Approximate Reasoning, 105, 252–86.
  • Shum, Michael, Kleiman-Weiner, Max, Littman, Michael L, and Tenenbaum, Joshua B (2019). Theory of minds: Understanding behavior in groups through inverse planning. In Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence (AAAI-19).
  • Telle, J. A., Herna虂ndez-Orallo, J., and Ferri, C. (2019). The teaching size: computable teachers and learners for universal languages. Machine Learning, 108(8–9), 1653–75.
  • Vorms, Marion and Hahn, Ulrike Synthese , ISSN 0039-7857, 2019
  • Collins, P.J. and Hahn, Ulrike Journal of Experimental Psychology: Learning, Memory, and Cognition , ISSN 0278-7393, 2019
  • Skovgaard-Olsen, N. and Kellen, D. and Hahn, Ulrike and Klauer, K.C.  Psychological Review 126 (5), pp. 611-633. ISSN 0033-295X, 2019

2018

  • Celine Hocquette and S.H. Muggleton. . In Fabrizio Riguzzi, Elena Bellodi, and Riccardo Zese, editors, Proceedings of the 28th International Conference on Inductive Logic Programming, pages 38-53, Berlin, 2018
  • S.H. Muggleton, W-Z. Dai, C. Sammut, A. Tamaddoni-Nezhad, J. Wen, and Z-H. Zhou. . Machine Learning, 107:1097-1118, 2018.
  • S.H. Muggleton, U. Schmid, C. Zeller, A. Tamaddoni-Nezhad, and T. Besold. . Machine Learning, 107:1119-1140, 2018.
  • Chater, N., Zeitoun, H. and Melkonyan, T. A. "The social character of moral reasoning - Commentary on : Joshua May : regard for reason in the moral mind", Behavioral and Brain Sciences, 2018
  • Melkonyan, T. A., Zeitoun, H. and Chater, N. "", Management Science, 64, 12, 5599-5609, 2018
  • Chater, N., Misyak, J. B., Watson, D. G., Griffiths, N. and Mouzakitis, A. "", Trends in Cognitive Sciences, 22, 2, 93-95, 2018
  • Zhu, J., Sanborn, A. N. and Chater, N. "", Advances in Neural Information Processing Systems (NIPS 2016), 5753-5764, 2018
  • Chater, N. and Oaksford, M. "", Mind & Language, 33, 5, 525-532, 2018
  • Chater, N. and Christiansen, M. H. "", Current Opinion in Behavioral Sciences, 21, 205-208, 2018
  • Nick Chater "", Trends in Cognitive Sciences, 22, 5, 369-37, 2018
  • Reali, F., Chater, N. and Christiansen, M. H. "", Proceedings of the Royal Society B: Biological Sciences, 285, 1871, 20172586, 2018
  • Jiang B, Li Z, Chen H, Cohn AG. Latent Topic Text Representation Learning on Statistical Manifolds. IEEE Transactions on Neural Networks and Learning Systems. 5643-5654 29.11, 2018
  • Peel H, Luo S, Cohn AG, Fuentes RLocalisation of a mobile robot for bridge bearing inspection. Automation in Construction. 244-256 94, 2018. 
  • Wei L, Magee DR, Cohn AG.  An anomalous event detection and tracking method for a tunnel look-ahead ground prediction system. Automation in Construction. 216-225 9, 2018. 
  • Tayyub J, Hawasly M, Hogg DC, Cohn AG.  Learning Hierarchical Models of Complex Daily Activities from Annotated Videos. 2018 IEEE Winter Conference on Applications of Computer Vision, 2018. 
  • Bilal M, Khan W, Muggleton J, Rustighi E, Jenks H, Pennock SR, Atkins PR, Cohn A.  Inferring the most probable maps of underground utilities using Bayesian mapping model. Journal of Applied Geophysics. 52-66 150, 2018. 
  • D Philps, T. Weyde, A. d'Avila Garcez and R. Batchelor. Investment Decision with Continuous Learning based on Memory-Augmented Neural Networks. In Proc. NIPS 2018 Workshop on Challenges and Opportunities for AI in Financial Services: the Impact of Fairness, Explainability, Accuracy, and Privacy. Workshop at NIPS'18, Montreal, Canada, 2018.
  • A. d'Avila Garcez, M. Gori, L. Lamb, L. Serafini, M. Spranger, S. Tran. Neural-Symbolic Computing: An Effective Methodology for Principled Integration of Machine Learning and Reasoning. In 13th International Workshop on Neural-Symbolic Learning and Reasoning (NeSy18), Human-Level AI Conference, Prague, Czech Republic, 2018
  • Biervoye, A, Meert, G, Apperly, IA & Samson, D, '', PLoS ONE, vol. 13, no. 1, e0190295. 2018
  • Upthegrove, R, Abu-Akel, A, Chisholm, K, Lin, A, Zahid, S, Pelton, M, Apperly, I, Hansen, PC & Wood, SJ , '', Schizophrenia Research, vol. 195, pp. 80-85. 2018
  • Abu-Akel, A, Apperly, I, Muller Spaniol, M, Geng, J & Mevorach, C , '', Scientific Reports, vol. 8, 8478. 2018
  • Zhao, L, Wang, JJ & Apperly, I , '', Journal of Experimental Child Psychology, vol. 174, pp. 130-149. 2018
  • Skovgaard-Olsen, N. and Collins, Peter J and Krzy偶anowska, K. and Hahn, Ulrike and Klauer, K.C.  Cognitive Psychology 108 , pp. 42-71. ISSN 0010-0285, 2018
  • Hahn, Ulrike and von Sydow, M. and Merdes, C.  Topics in Cognitive Science 11 (1), pp. 194-206. ISSN 1756-8765, 2018
  • Hahn, Ulrike and Merdes, C. and von Sydow, M. Topics in Cognitive Science 10 (4), pp. 660-678. ISSN 1756-8765, 2018
  • Harris, A.J.L. and Sildmäe, O. and Speekenbrink, M. and Hahn, Ulrike Journal of Risk Research 22 (5), pp. 593-609. ISSN 1366-9877, 2018
  • Collins, P.J. and Hahn, Ulrike and von Gerber, Y. and Olsson, E.J.  Frontiers in Psychology 9, p. 18. ISSN 1664-1078., 2018
  • von Sydow, M. and Braus, N. and Hahn, Ulrike Journal of Experimental Psychology: Applied , ISSN 1076-898X. 2018