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

 

2019

  • A. Cropper and S.H. Muggleton. Learning efficient logic programsMachine Learning, 108:1063-1083, 2019.
  • S.H. Muggleton and C. Hocquette. Machine discovery of comprehensible strategies for simple games using meta-interpretive learningNew 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