Stephen Muggleton is Professor of Machine Learning in the Department of Computing at Imperial College London. He is the Director of UK EPSRC NetworkPlus on Human-Like Computing He was the founder of the field of Inductive Logic Programming to which he has made contributions in theory, implementations and applications and has over 200 publications. He has been Executive Editor of the Machine Intelligence workshop series since 1992 and Editor-in-Chief of the series since 2000. In particular he acted as Programme Chair of the Machine Intelligence 20 and 21 workshop on Human-Like Computing (Cumberland Lodge, 23-25 October 2016; 30 th June-3rd July 2019).

Nick Chater FBA, Professor of Behavioural Science, Warwick Business School
Nick works on the cognitive and social foundations of rationality. He has written over 250 publications and co-written or edited more than a dozen books, has won four national awards for psychological research, and has served as Associate Editor for the journals Cognitive Science, Psychological Review, and Psychological Science. He was elected Fellow of the Cognitive Science Society in 2010, Fellow of the British Academy in 2012, and Fellow of the Association for Psychological Science in 2014. Nick co-founded Decision Technology, a consultancy applying psychology to business; and he is a member of the UK's Committee on Climate Change. His book The Mind is Flat (Penguin/Yale University Press), won the Association of American Publishers PROSE Award for Best Book in Clinical Psychology, 2018.

• PART 1 Human-Like Machine Intelligence


• 1: Human-Compatible Artificial Intelligence


• 2: Alan Turing and Human-Like Intelligence


• 3: Spontaneous communicative conventions through virtual bargaining


• 4: Modelling virtual bargaining using logical representation change"


• PART 2 Human-Like Social Cooperation


• 5: Mining Property-driven Graphical Explanations for Datacentric AI from Argumentation Frameworks


• 6: Explanation in AI systems


• 7: Human-Like Communication


• 8: Too many cooks: Coordinating multi-agent collaboration through inverse planning


• 9: Teaching and explanation: aligning priors between machines and humans


• PART 3 Human-Like Perception and Language


• 10: Human-Like Computer Vision


• 11: Apperception


• 12: Human-Machine Perception of Complex Signal Data


• 13: The sharedworkspace framework for dialogue and other cooperative joint activities


• 14: Beyond robotic speech: mutual benefits to cognitive psychology and artificial intelligence from the joint study of multimodal communication


• PART 4 Human-Like Representation and Learning


• 15: Human-Machine Scientific Discovery


• 16: Fast and slow learning in human-like intelligence


• 17: Interactive Learning with Mutual Explanations in Relational Domains


• 18: Endowing machines with the expert human ability to select representations: why and how


• 19: Human-Machine Collaboration for Democratizing Data Science


• PART 5 Evaluating Human-Like Reasoning


• 20: Automated Commonsense Spatial Reasoning: Still a Huge Challenge


• 21: Sampling as the human approximation to probabilistic inference


• 22: What can the conjunction fallacy tell us about human reasoning?


• 23: Logic-Based Robotics


• 24: Predicting problem difficulty in chess