2021

• L. Ai, S.H. Muggleton, C. Hocquette, M. Gromowski, and U. Schmid. Beneficial and harmful explanatory machine learning. Machine Learning, 2021.
• S. Patsantzis and S.H. Muggleton. Top program construction and reduction for polynomial time meta-interpretive learning. 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
• CléMent 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 León-Villagrá, 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. Learning higher-order programs through predicate invention. In Proceedings of the 34th Conference on Artificial Intelligence (AAAI 2020), pages 13655-13658. AAAI, 2020.
• A. Cropper, S. Dumancic, and S.H. Muggleton. Turning 30: New ideas in inductive logic programming. 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. Learning higher-order logic programs. Machine Learning, 109:1289-1322, 2020.
• C. Hocquette and S.H. Muggleton. Complete bottom-up predicate invention in meta-interpretive learning. 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. New paradigms in the psychology of reasoning", 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. "The Bayesian sampler : generic Bayesian inference causes incoherence in human probability ", Psychological Review, 127, 5, 719-748, 2020
• Quesada Real, F., Bella, G., Mcneill, F. & Bundy, A. Using Domain Lexicon and Grammar for Ontology Matching, Proceedings of the Fifteenth International Workshop on Ontology Matching. CEUR-WS.org, 12 p., 2020
• Urbonas, M., Bundy, A., Casanova, J. & Li, X., The Use of Max-Sat for Optimal Choice of Automated Theory Repairs, Proceedings of the Fortieth SGAI International Conference on Artificial Intelligence (AI 2020). Springer, Cham, 14 p., 2020
• Numah, K., Bundy, A.,  Explainable Inference in the FRANK Query Answering System 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, 'An Observational Study of Social Interaction Skills and Behaviors in Cornelia de Lange, Fragile X and Rubinstein-Taybi Syndromes', Journal of Autism and Developmental Disorders, 2020
• Abu-Akel, AM, Apperly, IA, Wood, SJ & Hansen, PC , 'Re-imaging the intentional stance', 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, 'Scaling of Early Social Cognitive Skills in Typically Developing Infants and Children with Autism Spectrum Disorder', Journal of Autism and Developmental Disorders. 2020
• Wang, JJ, Ciranova, N, Woods, B & Apperly, IA , 'Why are listeners sometimes (but not always) egocentric? Making inferences about using others' perspective in referential communication', PLoS ONE, vol. 15, no. 10, pp. e0240521. 2020
• Collins, P.J. and Krzyzanowska, K. and Hartmann, S. and Wheeler, G. and Hahn, Ulrike Conditionals and Testimony. 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.  Dependencies in evidential reports: the case for informational advantages. Cognition 204 (104343), ISSN 0010-0277. 2020
• Hahn, Ulrike Argument quality in Real-World Argumentation. Trends in Cognitive Sciences 24 (5), pp. 363-374. ISSN 1364-6613. 2020
• Hahn, Ulrike and Hansen, J.U. and Olsson, E.J.  Truth tracking performance of social networks: how connectivity and clustering can make groups less competent. Synthese 197 , pp. 1511-1541. ISSN 0039-7857. 2020
• Madsen, J.K. and Hahn, Ulrike and Pilditch, T.D.  The impact of partial source dependence on belief and reliability revision. Journal of Experimental Psychology: Learning, Memory, and Cognition , ISSN 0278-7393. 2020
• Merdes, C. and von Sydow, M. and Hahn, Ulrike Formal models of source reliability. Synthese , ISSN 0039-7857. 2020 

2019

• A. Cropper and S.H. Muggleton. Learning efficient logic programs. Machine Learning, 108:1063-1083, 2019.
• S.H. Muggleton and C. Hocquette. Machine discovery of comprehensible strategies for simple games using meta-interpretive learning. 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’Alché-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. "Sensorimotor communication beyond the body: The case of driving. Comment on “The body talks: sensorimotor communication and its brain and kinematic signatures” by G. Pezzulo et al.", 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. "How should autonomous vehicles overtake other drivers?", 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 , 'Is tool modification more difficult than innovation?', Cognitive Development, vol. 52, 100811. 2019
• Apperly, IA , 'The benefit of seeing in company', 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.
• Tešic, 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., Hernández-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 In the space of reasonable doubt. Synthese , ISSN 0039-7857, 2019
• Collins, P.J. and Hahn, Ulrike We might be wrong, but we think that hedging doesn't protect your reputation. 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.  Norm conflicts and conditionals. Psychological Review 126 (5), pp. 611-633. ISSN 0033-295X, 2019

2018

• Celine Hocquette and S.H. Muggleton. How much can experimental cost be reduced in active learning of agent strategies?. 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. Meta-interpretive learning from noisy images. Machine Learning, 107:1097-1118, 2018.
• S.H. Muggleton, U. Schmid, C. Zeller, A. Tamaddoni-Nezhad, and T. Besold. Ultra-strong machine learning - comprehensibility of programs learned with ILP. 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. "Collusion in Bertrand versus Cournot competition : a virtual bargaining approach", Management Science, 64, 12, 5599-5609, 2018
• Chater, N., Misyak, J. B., Watson, D. G., Griffiths, N. and Mouzakitis, A. "Negotiating the traffic : can cognitive science help make autonomous vehicles a reality?", Trends in Cognitive Sciences, 22, 2, 93-95, 2018
• Zhu, J., Sanborn, A. N. and Chater, N. "Mental sampling in multimodal representations ", Advances in Neural Information Processing Systems (NIPS 2016), 5753-5764, 2018
• Chater, N. and Oaksford, M. "The enigma is not entirely dispelled: A review of Mercier and Sperber's The Enigma of Reason ", Mind & Language, 33, 5, 525-532, 2018
• Chater, N. and Christiansen, M. H. "Language acquisition as skill learning", Current Opinion in Behavioral Sciences, 21, 205-208, 2018
• Nick Chater "Is the Type 1/Type 2 Distinction Important for Behavioral Policy?", Trends in Cognitive Sciences, 22, 5, 369-37, 2018
• Reali, F., Chater, N. and Christiansen, M. H. "Simpler grammar, larger vocabulary : how population size affects language", 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, 'Assessing the integrity of the cognitive processes involved in belief reasoning by means of two nonverbal tasks: rationale, normative data collection and illustration with brain-damaged patients', 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 , 'Autism and psychosis: Clinical implications for depression and suicide', Schizophrenia Research, vol. 195, pp. 80-85. 2018
• Abu-Akel, A, Apperly, I, Muller Spaniol, M, Geng, J & Mevorach, C , 'Diametric effects of autism tendencies and psychosis proneness on attention control irrespective of task demands', Scientific Reports, vol. 8, 8478. 2018
• Zhao, L, Wang, JJ & Apperly, I , 'The cognitive demands of remembering a speaker's perspective and managing common ground size modulate 8- and 10-year-olds' perspective-taking abilities', Journal of Experimental Child Psychology, vol. 174, pp. 130-149. 2018
• Skovgaard-Olsen, N. and Collins, Peter J and Krzyżanowska, K. and Hahn, Ulrike and Klauer, K.C.  Cancellation, negation, and rejection. Cognitive Psychology 108 , pp. 42-71. ISSN 0010-0285, 2018
• Hahn, Ulrike and von Sydow, M. and Merdes, C.  How communication can make voters choose less well. Topics in Cognitive Science 11 (1), pp. 194-206. ISSN 1756-8765, 2018
• Hahn, Ulrike and Merdes, C. and von Sydow, M. How good is your evidence and how would you know? 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 The potential power of experience in communications of expert consensus levels. 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.  The bi-directional relationship between source characteristics and message content. Frontiers in Psychology 9, p. 18. ISSN 1664-1078., 2018
• von Sydow, M. and Braus, N. and Hahn, Ulrike On the ignorance of group-level effects – The tragedy of personnel evaluation? Journal of Experimental Psychology: Applied , ISSN 1076-898X. 2018