Invited Speakers

Professor Ivan Bratko, University of Ljubljana

Topic: "Predicting difficulty of problems for humans"

A question related to Explainable AI is: How can we automatically predict the difficulty of a given problem for humans? The difficulty for a human also depends on how the human would go about solving the problem. The need for predicting difficulty arises in intelligent interaction with a user, including intelligent tutoring systems. In this talk we discuss one approach to automatic prediction of difficulty for humans, of problems that are solved through informed search. We present an experimental study in predicting the difficulty of tactical chess problems, and investigate human problem solving in this domain. In solving tactical problems, humans use pattern-based knowledge to guide their search extremely effectively. Problem solving consists in detecting chess patterns − motifs, and calculation of concrete variations trying to exploit these motifs to the player’s advantage. Our analysis includes players’ comments on how they tackled individual problems, and the recordings of their eye movements during problem solving. The conclusions are indicative of the importance of calculation of variations relative to chess pattern knowledge. Also, the findings suggest how automated assessment of difficulty could be implemented based on the amount of search needed to solve the problem. When the amount of search is estimated by a computer, it is important that the search algorithm takes into account the chess motifs used by a human, which may drastically affect the search complexity.

Short biography:
Professor Ivan Bratko is head of Artificial intelligence Laboratory, Faculty of Computer and Information Sc. of Ljubljana University. He has conducted research in machine learning, knowledge-based systems, qualitative modelling, intelligent robotics, heuristic programming and computer chess. He has published over 200 scientific papers and a number of books, including Prolog Programming for Artificial Intelligence (Addison-Wesley/Pearson Education, third edition, 2001), KARDIO: a Study in Deep and Qualitative Knowledge for Expert Systems (MIT Press, 1989; co-authored by I. Mozetič and N. Lavrač), and Machine Learning and Data Mining: Methods and Applications (Wiley, 1998; co-edited by R.S. Michalski and M. Kubat). He has been member of the editorial boards of a number of scientific journals, including Artificial Intelligence, Machine Learning, Journal of AI Research, Journal of ML Research, and KAIS (Journal of Knowledge and Information Systems). He was one of the founders and the first chairman of SLAIS (Slovenian AI Society) and chairman of ISSEK, International School for the Synthesis of Expert Knowledge, based in Udine, Italy. He is member of SAZU (Slovene Academy of Arts and Sciences) and a Fellow of ECCAI. He has been visiting professor or visiting scientist at various universities, including Edinburgh University, Strathclyde University, Sydney University, University of New South Wales, Polytechnic University of Madrid, University of Klagenfurt, Delft University of Technology.
AlanBundy Professor Alan Bundy, School of Informatics, University of Edinburgh

Topic: "Modelling Repairs to Virtual Bargaining with Reformation"

Research by Nick Chater and his team at Warwick has identified Virtual Bargaining, a technique that collaborating humans have been found to use when they need to coordinate under severe communicative constraints. Each participant imagines themselves into the shoes of the other participant(s): senders decide how receivers would interpret each of the limited range of signals that they can be sent; and receivers use similar reasoning to interpret these signals and, thereby, decide what actions to take. Given the limited range of possible signals, a novel situation sometimes requires an old signal to be reinterpreted in a new way - even to the extent of inverting its meaning. Virtual bargaining isn't perfect. Sometimes receivers misinterpret signals and take the wrong actions. Then, either senders, receivers or both need to learn from these failures and generalise their strategies.  We discuss how Reformation, an algorithm for repairing the concepts in a formal representation, can be used computationally to model the conceptual changes involved in this learning process.

Short biography:
Alan Bundy is Professor of Automated Reasoning in the School of Informatics at the University of Edinburgh.  His research interests include: the automation of mathematical reasoning, with applications to reasoning about the correctness of computer software and hardware; and the automatic construction, analysis and evolution of representations of knowledge. His research combines artificial intelligence with theoretical computer science and applies this to practical problems in the development and maintenance of computing systems.  He is the author of over 300 publications and has held over 60 research grants. He is a fellow of several academic societies, including the Royal Society, the Royal Society of Edinburgh, the Royal Academy of Engineering and the Association for Computing Machinery. His awards include the IJCAI Research Excellence Award (2007), the CADE Herbrand Award (2007) and a CBE (2012). He was: Edinburgh's founding Head of Informatics (1998-2001); founding Convener of UKCRC (2000-05); and a Vice President and Trustee of the British Computer Society with special responsibility for the Academy of Computing (2010-12). He was also a member of: the Hewlett-Packard Research Board (1989-91); the ITEC Foresight Panel (1994-96); both the 2001 and 2008 Computer Science RAE panels (1999-2001, 2005-8); and the Scottish Science Advisory Council (2008-12). 

Professor Nick Chater, Warwick Business School, University of Warwick

Title: Virtual bargaining - A microfoundation for the theory of social interaction

How can people coordinate their actions or make joint decisions? One possibility is that each person attempts to predict the actions of the other(s), and best-responds accordingly. But this can lead to bad outcomes, and sometimes even vicious circularity. An alternative view is that each person attempts to work out what the two or more players would agree to do, if they were to bargain explicitly. If the result of such a "virtual" bargain is "obvious," then the players can simply play their respective roles in that bargain. I suggest that virtual bargaining is essential to genuinely social interaction (rather than viewing other people as instruments), and may even be uniquely human. This approach aims to respect methodological individualism, a key principle in many areas of social science, while explaining how human groups can, in a very real sense, be "greater" than the sum of their individual members.

Short biography:
Nick Chater is Professor of Behavioural Science at Warwick Business School. He works on the cognitive and social foundations of rationality and language. He has published more than 250 papers, co-authored 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 a Fellow of the Cognitive Science Society in 2010 and a Fellow of the British Academy in 2012. Nick is co-founder of the research consultancy Decision Technology and is a member on the UK’s Committee on Climate Change. He is the author of The Mind is Flat (2018).
Professor Luc De Raedt, Department of Computer Science, Katholieke Universiteit Leuven

Topic: "Inductive Modeling for the Automation of Data Science"

A primary goal of artificial intelligence is to develop machines that carry out and automate tasks that require intelligence. This paper focusses on the automation and democratization of data science. Data science, and the related fields of machine learning and data mining, are causing a revolution in both science and society today. But it requires a lot of effort and labor to carry out such data science processes as one needs to select the right subsets of the data, put those data in the right form, determine what the learning tasks will be, select the right algorithms, evaluate the results, ask the experts, etc. The question tackled in the ERC AdG project SYNTH (Synthesising Inductive Data Models) is how humans can be supported in the data science process by a number of tools and techniques that (partly) automate several steps in this process.  We  introduce the SYNTH framework from both the perspective of data scientists and end-users. From an end-user point of view, SYNTH performs the task of autocompletion, that is, given a set of spreadsheets that the user is filling out, SYNTH wants to automatically predict or complete the next value the user will fill out wherever possible. The front-end of SYNTH extends traditional spreadsheet software with facilities for realizing this. These are based on automatically analyzing, wrangling and transforming the data in a format that is amenable for data analysis, the learning of constraints that hold in the data as well as predictive and probabilistic models, and using probabilistic reasoning for automatically computing the most likely target value.  The back-end of SYNTH is the SynthLog language for learning and reasoning, which extends the probabilistic logic programming language ProbLog with inductive database principles, and as such treats learned "inductive" models as first class citizens.  In this way, SyntLog provides support for inductive, deductive and probabilistic reasoning, for constraint solving, as well as for machine learning. For more information about the SYNTH project, and a list of contributors, we refer to synth.cs.kuleuven.be

Short biography:
Professor Luc De Raedt is a full professor and head of the lab for Declarative Languages and Artificial Intelligence at KU Leuven.  He is a former chair of Computer Science of the University of Freiburg in Germany. He received an ERC AdG on automated data science, he is a fellow of EurAI and AAAI, and he chaired several conferences in artificial intelligence and machine learning (such as ICML 05 and ECAI 12). Luc's research interests are in Artificial Intelligence, Machine Learning and Data Mining, as well as their applications. He is well known for his contributions in the areas of  learning and reasoning, in particular, for his contributions to statistical relational learning and inductive programming.
Professor Mike Frank,  Stanford University

Topic: "Variability and Consistency in Early Language Learning: The Wordbank Project"

Every typically developing child learns to talk, but children vary tremendously in how and when they do so. What predicts this variability? And which aspects of early language learning are consistent across the world’s languages and cultures? We use data from tens of thousands of children learning dozens of different languages to create a data-driven picture of universals and variation in early language learning.

Short biography:
Michael C. Frank is David and Lucile Packard Professor of Human Biology at Stanford University. He received his PhD from MIT in Brain and Cognitive Sciences in 2010. He studies language use and language learning, and how these interact with social cognition, focusing especially on early childhood. He is the organizer of the ManyBabies Consortium, a collaborative replication network for infancy research, and has led open-data projects including Wordbank and MetaLab. He has been recognized as a "rising star" by the Association for Psychological Science. His dissertation received the Glushko Prize from the Cognitive Science Society, and he is recipient of the FABBS Early Career Impact award and a Jacobs Advanced Research Fellowship. He has served as Associate Editor for the journal Cognition, member and chair of the Governing Board of the Cognitive Science Society, and was a founding Executive Committee member of the Society for the Improvement of Psychological Science.
Professor Ulrike Hahn, Department of Psychological Sciences, Birkbeck University of London

Topic: "
Explanation for AI systems"

The talk will use recent work seeking to generate natural language explanations for Bayesian Belief Networks (BBN) to motivate a more thorough inquiry into what “good explanations” are in this context. It will draw on analysis of algorithm performance, a case study in human generated explanations of BBN inference, and data from human behavioural experiments. Implications for explanation of machine reasoning and decision-making in general will be discussed.

Short biography:
Professor of Psychology in the Department of Psychological Sciences, has been awarded the Alexander von Humboldt Foundation Anneliese Maier Research Award. This award is presented to world class researchers in the humanities and social sciences with the aim of encouraging collaboration between international researchers in Germany. Winners work on research projects funded for up to five years. Professor Hahn’s research investigates aspects of human cognition including argumentation, decision-making, concept acquisition, and language learning. Her work involves both experimentation and modelling. She is Director of the Centre for Cognition, Computation and Modelling which was launched earlier in 2013.
Prof Patrick Healey, School of Electronic Engineering and Computer Science at Queen Mary, University of London

Topic: "Social Health: Mapping the quality of social interactions in the wild"

Social engagement is an exceptionally strong predictor of long term physical and mental health. Socially isolated people have 2–4-times increased all-cause mortality after adjusting for biomedical risk factors (House, et. al. 1988, Fratiglioni et al., 2004; Holt-Lunstad, 2010).  Our ability to understand and take advantage of these effects is limited by our ability to measure social engagement. Current research relies on coarse-grained, indirect measures such as marital status, group membership, frequency of contact, frequency of media use and retrospective self-report (see e.g., Cohen, 2004; Holt-Lunstad, 2010 for reviews).  These measures are unable to capture the quality or ecology of daily social interactions; stressful, playful, engaging, hostile etcetera. Doing this is an important step toward uncovering opportunities for both health and policy interventions. Wearables provide an obvious opportunity to fill this gap however the primary focus of most health research to date has been on tracking physical activity e.g. walking, sleeping, cycling.  We present data from wrist mounted triaxial accelerators which shows how social activity can be reliably sensed from people's hand movements alone.  We discuss the potential of this approach for unobtrusive tracking of individual and group social health, the challenges for privacy and sharing and the potential applications beyond heath.

Short biography:
Pat Healey is Professor of Human Interaction and leads the Cognitive Science Research Group in the School of Electronic Engineering and Computer Science at Queen Mary, University of London. He also holds a Turing Fellowship.  His research focuses on the mechanisms that underpin human-human interaction, especially the ways in which people detect and recover from misunderstandings.
Dr Mateja Jamnik, Department of Computer Science and Technology, University of Cambridge

Topic: "How to Re(represent) It?"

To achieve efficient human computer collaboration, computers need to be able to represent information in ways that humans can understand. To select representations appropriately, AI systems need to have some underlying theory of the formal and cognitive properties of representations. In this  interdisciplinary project, we are developing the foundations for the analysis of representations for reasoning. Ultimately, the goal is to build AI systems that select representations intelligently, taking users’ preferences and abilities into account.

Short biography:
Dr Mateja Jamnik is a Reader in Artificial Intelligence at the Department of Computer Science and Technology of the University of Cambridge, UK. She is developing AI techniques for human-like computing - she computationally models how people solve problems to enable machines to reason in a similar way to humans. She is essentially trying to humanise computer thinking. She applies this AI technology to medical data to advance personalised cancer medicine, and to education to personalise tutoring systems. Mateja is passionate about bringing science closer to the public and engages frequently with the media and public science events. Her active support of women scientists was recognised by the Royal Society which awarded her the Athena Prize. Mateja has been advising the UK government on policy direction in relation to the impact of AI on society.

Dr Caroline Jay, School of School of Computer Science, University of Manchester

Topic: "Using human vision to automate the interpretation of complex signal data"

Electrocardiograms (ECGs), which capture the electrical activity of the human heart, are widely used in clinical practice, and notoriously difficult to interpret. Whilst there have been attempts to automate their interpretation for several decades, human reading of the data presented visually remains the ‘gold standard’. We demonstrate how a visualisation technique that significantly improves human interpretation of ECG data can be used as a basis for an automated interpretation algorithm that is more accurate than current signal processing techniques, and has the benefit of the human and machine sharing the same representation of the data. We discuss the benefits and limitations of this approach, and compare it with machine learning approaches that are commonly used with medical data, in terms of its accuracy, efficiency, and acceptability in clinical practice

Short biography:
Caroline Jay is a Chartered Psychologist and Computer Scientist at the University of Manchester. Her research examines how people interact with machines, from using apps, to designing algorithms. Caroline is the Research Director of the UKRI Software Sustainability Institute, and a Fellow of the Alan Turing Institute  where she leads the project ‘Understanding the relationship between human health and the environment.' She leads the University of Manchester Arm of the BBC Data Science Partnership, and the Software Engineering Learning Analytics stream at the Institute of Coding.

Dr Max Kleiman-Weiner, Harvard University

Reverse Engineering Human Cooperation

Human cooperation is distinctly powerful. We collaborate with others to accomplish together what none of us could do on our own; we share the benefits of collaboration fairly and trust others to do the same. Even young children cooperate with a scale and sophistication unparalleled in other animal species. I seek to understand these everyday feats of social intelligence in computational terms. What are the cognitive representations and processes that underlie these abilities and what are their origins? How can we apply these cognitive principles to build machines that have the capacity to understand, learn from, and cooperate with people? I will present a formal framework based on the integration of individually rational, hierarchical Bayesian models of learning, together with socially rational multi-agent and game-theoretic models of cooperation. First, I investigate the evolutionary origins of the cognitive structures that enable cooperation and support social learning. I then describe how these structures are used to learn social and moral knowledge rapidly during development. Finally I show how this knowledge is generalized in the moment, across an infinitude of possible situations: inferring the intentions and reputations of others, distinguishing who is friend or foe, and learning a new moral value all from just a few observations of behavior.

Short biography:
Dr. Max Kleiman-Weiner is a fellow of the Data Science Institute and Center for Research on Computation and Society (CRCS) within the computer science and psychology departments at Harvard. He did his PhD in Computational Cognitive Science at MIT advised by Josh Tenenbaum where he was a NSF and Hertz Foundation Fellow. His thesis won the 2019 Robert J. Glushko Prize for Outstanding Doctoral Dissertation in Cognitive Science. He also won best paper at RLDM 2017 for models of human cooperation and the William James Award at SPP for computational work on moral learning. Max serves as Chief Scientist of Diffeo a startup building collaborative machine intelligence. Previously, he was a Fulbright Fellow in Beijing, earned an MSc in Statistics as a Marshall Scholar at Oxford, and did his undergraduate work at Stanford as a Goldwater Scholar.
Dr Kenneth Kwok, Agency for Science, Technology and Research (A*STAR), Singapore

Topic: "Cognitive Human-like Empathetic and Explainable Machine Learning (CHEEM):  A human-centric AI research programme"

AI has made spectacular progress in recent years, achieving and, in some cases, surpassing human-level performance.  Long-standing problems in computer vision and speech have been solved, and AI programs have beaten the best human players in games such as Go, Jeopardy, and even versions of Poker.   Yet, impressive as these feats are, AI still does not understand much of what it does, and certainly does not understand humans and the complexities of the world in which we operate.  For AI to be useful and usable by humans, much more needs to be done to endow AI with abilities that are more human-like.  I will discuss current work at A*STAR that is working to address this gap towards realising AI that understands humans, and that humans can understand.

Short biography:
Dr Kenneth Kwok is Principal Scientist at the Institute of High Performance Computing (IHPC) at the Agency for Science, Technology and Research (A*STAR) and Programme Manager of the A*STAR Artificial Intelligence Programme (A*AI). He heads the Cognitive Systems group within the Social and Cognitive Computing department in IHPC, and is the PI of the Human-Centric AI (CHEEM) Programme under A*AI.  He also co-leads the Collaborative AI for Advanced Manufacturing and Engineering Programme involving more than 50 scientists and researchers from A*STAR, the National University of Singapore, Nanyang Technological University and the Singapore University of Technology and Design.  Prior to joining IHPC, Kenneth was Programme Director for Information Exploitation, and later, Programme Director for Combat Protection and Performance at Singapore’s DSO National Laboratories. Kenneth’s research interests lie at the intersection of Cognitive Psychology and Computing.
DenisMareschal Professor Denis Mareschal, Centre for Brain and Cognitive Development, School of Psychology, Birkbeck College

Topic: "Fast and slow learning across development"

Children are often notoriously slow at learning new skills. Yet there is also evidence that in some circumstances they generalise their knowledge to new exemplars after very few learning trials. In this talk I will review evidence of when rapid learning and slow learning occur to identify the conditions under which or or the other mode operates. These conditions will be illustrated through the use of neural network modelling.

Short biography:
Denis Mareschal obtained his first degree in Physics and Theoretical Physics from Cambridge University. He then completed a Masters in Psychology from McGill University in psychology and AI, before moving on to complete a PhD in psychology at Oxford University. He has received the Marr prize from the Cognitive Science Society (USA), the Young Investigator Award from the International Society on Infant Studies (USA), the Margaret Donaldson Prize from the British Psychological Society, and a Wolfson-Royal Society research merit award from the Royal Society. His research centers on developing mechanistic models of perceptual and cognitive development in infancy and childhood. He is currently Professor and director of the Centre for Brain and Cognitive Development at Birkbeck University of London. Recent books include Neuroconstructivism (2007), The Making of Human Concepts (2010), and Educational Neuroscience (2013).
Professor Peter Millican, Oxford University

Topic: "Turing and Human-Like Intelligence"

The concept of Human-Like Computing became central to visions of Artificial Intelligence through the work of Alan Turing, whose model of computation (1936) was based on the potential operations of a human "computer", and whose famous test for intelligent machinery (1950) focused on indistinguishability from human behaviour.  That test has recently been reconceived by various scholars, and my first aim will be to settle various interpretative controversies as conclusively as possible.  Then I shall go on to consider the force of Turing's arguments for his text and any enduring lessons that can be drawn from his discussion.  My overall conclusion is that his own position is somewhat confused, giving a criterion based on superficial similarity to human performance but at the same time apparently drawing implications about internal causation (notably in his solipsistic response to the objection from consciousness).  Thus Turing overemphasises human-likeness - both externally and internally - even though his overall intention seems to be to provide a criterion for machine intelligence that is objective and blind to internal mechanisms.  The main weaknesses of his test follow: first, its focus on superficial indistinguishability from a human renders it inapplicable to the vast range of possible un-humanlike intelligences, while imposing an irrelevant (and potentially very heavy) demand on any tested system.  Secondly, the test involves a human judge, and thus encourages a focus on methods that can fool such a judge (as in ELIZA-style chatbots) rather than on exhibiting sophisticated information processing.  Both of these objections can be (and often have been) overcome by moving to a general perspective - and, if desired, a style of test - that compares the achievements of human and machine “intelligences” in particular information-processing tasks, focusing more on the quality of their results than on the similarity of their behaviour to our own.
Professor Stephen Muggleton, Department of Computing, Imperial college London

Topic: "Human-Machine Vision"

Statistical machine learning is widely used in image classification. However, most techniques 1) require many images to achieve high accuracy and 2) do not provide support for reasoning below the level of classification, and so are unable to support secondary reasoning, such as the existence and position of light sources and other objects outside the image. In recent work an Inductive Logic Programming approach called Logical Vision (LV) was shown to overcome some of these limitations. LV uses Meta-Interpretive Learning combined with low-level extraction of high-contrast points sampled from the image to learn recursive logic programs describing the image. In published work LV was demonstrated capable of high-accuracy prediction of classes such as regular polygon from a small number examples of images where the compared statistical learning algorithms gave near random prediction given hundreds of instances. LV has so far only been applied to noise-free, artificially generated images. This paper extends LV by using a) richer background knowledge such as light reflection that can itself be learned and used for resolving visual ambiguities, which cannot be easily modeled using statistical approaches, b) a wider class of background models representing classical 2D shapes such as circles and ellipses, c) primitive-level statistical estimators to handle noise in real images, Our results indicate that in real images the new noise-robust version of LV using a single example (ie one-shot LV) converges to an accuracy at least comparable to thirty-shot statistical machine learner on the prediction of hidden light sources. Moreover, we show that the learned theory can be used to identify ambiguities in the convexity/concavity of objects such as craters.

Short biography:
Professor Stephen Muggleton FREng FAAAI is Professor of Machine Learning in the Department of Computing at Imperial College London, Director of the UK's Human-Like Network and is internationally recognised as the founder of the field of Inductive Logic Programming. SM’s career has concentrated on the development of theory, implementations and applications of Machine Learning, particularly in the field of Inductive Logic Programming (ILP) and Probabilistic ILP (PILP). Over the last decade he has collaborated with biological colleagues, such as Prof Mike Sternberg, on applications of Machine Learning to Biological prediction tasks. SM’s group is situated within the Department of Computing and specialises in the development of novel general-purpose machine learning algorithms, and their application to biological prediction tasks. Widely applied software developed by the group includes the ILP system Progol (publication has over 1700 citations on Google Scholar) as well as a family of related systems including ASE-Progol (used in the Robot Scientist project), Metagol and Golem.
Professor Martin Pickering, Department of Psychology, University of Edinburgh

Topic: "Understanding dialogue: Language use and social interaction"

We present a theory of dialogue as a form of cooperative joint activity.  Dialogue is treated as a system involving two interlocutors and a shared workspace that contains their contributions and relevant non-linguistic context.  The interlocutors construct shared plans and use them to “post” contributions to the workspace, to comprehend joint contributions, and to distribute control of the dialogue between them.  A fundamental part of this process is to simulate their partner’s contributions and to use it to predict the upcoming state of the shared workspace.  As a consequence, they align their linguistic representations and their representations of the situation and of the “games” underlying successful communication.  The shared workspace is a highly limited resource, and the interlocutors use their aligned representations to say just enough and to speak in good time.  We end by applying the account beyond the “minimal dyad” to augmented dialogue, multi-party dialogue, and monologue. This talk is based on my forthcoming book with the same title, with Simon Garrod.

Short biography:
Martin Pickering is Professor of the Psychology of Language and Communication at the University of Edinburgh.  His research focuses on the representation and processing of language, and in particular on the interrelation between language production and comprehension in dialogue and monologue.  He has published around 200 papers on topics such as language comprehension during reading, turn-taking in dialogue, the representation of grammatical knowledge, the extent to which bilinguals integrate their languages, and the use of prediction to facilitate comprehension.  He has served as the editor of the Journal of Memory and Language, was recipient of the Experimental Psychology Society mid-career award, and is a Fellow of the Royal Society of Edinburgh.
Professor Stuart Russell, Department Computer Science, University of California, Berkeley

Topic: "Beneficial Artificial Intelligence"

It is reasonable to expect that AI capabilities will eventually exceed those of humans across a range of real-world-decision making scenarios. Should this be a cause for concern, as Elon Musk, Stephen Hawking, and others have suggested?  While some in the mainstream AI community dismiss the issue, I will argue instead that a fundamental reorientation of the field is required. Instead of building systems that optimize arbitrary objectives, we need to learn how to build systems that will, in fact, be beneficial for us.  I will show that it is useful to imbue systems with explicit uncertainty concerning the true objectives of the humans they are designed to help.  This uncertainty causes machine and human behavior to be inextricably (and game-theoretically) linked, while opening up many new avenues for research.

Short biography:
Stuart Russell received his B.A. with first-class honours in physics from Oxford University in 1982 and his Ph.D. in computer science from Stanford in 1986. He then joined the faculty of the University of California at Berkeley, where he is Professor (and formerly Chair) of Electrical Engineering and Computer Sciences, holder of the Smith-Zadeh Chair in Engineering, and Director of the Center for Human-Compatible AI. He has served as an Adjunct Professor of Neurological Surgery at UC San Francisco and as Vice-Chair of the World Economic Forum's Council on AI and Robotics. He is a recipient of the Presidential Young Investigator Award of the National Science Foundation, the IJCAI Computers and Thought Award, the World Technology Award (Policy category), the Mitchell Prize of the American Statistical Association, the Feigenbaum Prize of the Association for the Advancement of Artificial Intelligence, and Outstanding Educator Awards from both ACM and AAAI. From 2012 to 2014 he held the Chaire Blaise Pascal in Paris. He is an Honorary Fellow of Wadham College, Oxford, and Fellow of the American Association for Artificial Intelligence, the Association for Computing Machinery, and the American Association for the Advancement of Science. His book "Artificial Intelligence: A Modern Approach" (with Peter Norvig) is the standard text in AI; it has been translated into 14 languages and is used in over 1400 universities in 128 countries. His research covers a wide range of topics in artificial intelligence including machine learning, probabilistic reasoning, knowledge representation, planning, real-time decision making, multitarget tracking, computer vision, computational physiology, and philosophical foundations. He also works for the United Nations, developing a new global seismic monitoring system for the nuclear-test-ban treaty. His current concerns include the threat of autonomous weapons and the long-term future of artificial intelligence and its relation to humanity.
Dr Katya Tentori, Center for Mind/Brain Sciences (CIMeC), University of Trento, Italy.

Title: "What can the conjunction fallacy tell us about human reasoning?"

In my talk, I will summarize and discuss the main results obtained from more than three decades of studies on the conjunction fallacy. More specifically, I will argue that this striking and widely debated reasoning error is a robust phenomenon, which is not caused by the limitation of cognitive resources but can nonetheless systematically affect lay people’s as much as experts’ probabilistic inferences, with potentially relevant real-life consequences. I will then introduce what is, in my view, the best explanation of the conjunction fallacy and indicate how it allows the reconciliation of some classic probabilistic reasoning errors with the outstanding reasoning performances that humans have been shown capable of. Finally, I will tackle the open issue of the greater accuracy and reliability of evidential impact assessments over those of posterior probability, and outline how further research on this topic might contribute also to the development of effective human-like computing.

Short biography:
I’m a cognitive psychologist working at the Center for Mind/Brain Sciences (CIMeC), University of Trento, Italy.  My research interests are primarily in the fields of inductive reasoning, forecasting, decision biases, and causal cognition, but also extend to various applied problems in medical decision making and legal evidence assessment. In my studies, I combine empirical methods of experimental psychology with theoretical modelling from formal epistemology.
Francesca Toni Professor Francesca Toni, Department of Computing, Imperial College London

Title: "Dialectic Explanations"

The lack of transparency of AI techniques, e.g. machine learning algorithms or recommender systems, is one of the most pressing issues in the field, especially given the ever-increasing integration of AI into everyday systems used by experts and non-experts alike, and the need to explain how and/or why these systems compute outputs, for any or for specific inputs. The need for explainability arises for a number of reasons: an expert may require more transparency to justify outputs of an AI system, especially in safety-critical situations, while a non-expert may place more trust in an AI system providing basic (rather than no) explanations, regarding, for example, films suggested by a recommender system. Explainability is also needed to fulfil the requirements of the forthcoming General Data Protection Regulation (GDPR), effective from May 25 th, 2018. Furthermore, explainability is crucial to guarantee comprehensibility in Human-Like Computing, to support collaboration and communication between machines and human beings. In this talk I will overview recent efforts to use argumentative abstractions for data-centric methods in AI as a basis for generating dialectic explanations. These abstractions are formulated in the spirit of argumentation in AI, amounting to a (family of) symbolic formalism(s) where arguments are seen as nodes in a graph with relations between arguments, e.g. attack and support, as edges. Argumentation allows for conflicts to be managed effectively, an important capability in any AI system tasked with decision-making. It also allows for reasoning to be represented in a human-like manner, and can serve as a basis for a principled theory of explanation supporting human-machine dialectical exchanges.

Short biography:
Francesca Toni is Professor in Computational Logic in the Department of Computing, Imperial College London, UK, and the funder and  leader of the CLArg (Computational Logic and Argumentation) research group. Her research interests  lie within the broad area of Knowledge Representation and Reasoning in Artificial Intelligence, and in particular include Argumentation, Logic-Based Multi-Agent Systems, Logic Programming for Knowledge Representation and  Reasoning, Non-monotonic and Default Reasoning. She graduated, summa cum laude, in Computing at the University of Pisa, Italy, in 1990, and received her PhD in Computing in 1995 from Imperial College London. She has coordinated two EU projects, received funding from EPSRC and the EU, and awarded a Senior Research Fellowship from The Royal Academy of Engineering and the Leverhulme Trust. She is currently Technical Director of  the ROAD2H EPSRC-funded project. She has co-chaired ICLP2015 (the 31st International Conference on Logic Programming) and KR 2018 (the 16th Conference on Principles of Knowledge Representation and Reasoning). She is a member of the steering committe of AT (Agreement Technologies) and KR Inc (Principles of Knowledge Representation and Reasoning, Incorporated), corner editor on Argumentation for the Journal of Logic and Computation , and in the editorial board of the Argument and Computation journal and the AI journal.
Professor Adam Sanborn, University of Warwick

Title: "Bayesian brains without probabilities"

Abstract :
Over the past two decades, a wave of Bayesian explanations has swept through cognitive science, explaining behavior in domains from intuitive physics and causal learning, to perception, motor control and language. Yet people produce stunningly incorrect answers in response to even the simplest questions about probabilities. How can a supposedly Bayesian brain paradoxically reason so poorly with probabilities? Perhaps Bayesian brains do not represent or calculate probabilities at all and are, indeed, poorly adapted to do so. Instead the brain could be approximating Bayesian inference through sampling: drawing samples from its distribution of likely hypotheses over time. Only with infinite samples does a Bayesian sampler conform to the laws of probability, and in this talk I show how reasoning with a finite number of samples systematically generates classic probabilistic reasoning errors in individuals, upending the longstanding consensus on these effects. I then present work testing whether people sample when producing numeric estimates, and discuss what kind of sampling algorithm the brain might be using.

Short biography:
Adam Sanborn is a cognitive psychologist interested in how rational people's behaviour is: whether the biases that people show correspond to normative statistical models and approximations to statistical models. He has studied these ideas in various areas of cognition, including categorization, perception, decision making, learning, reasoning, and intuitive physics. He received his PhD in Cognitive Science and Psychological and Brain Sciences from Indiana University and worked as a postdoc at the Gatsby Computational Neuroscience Unit of University College London. He is currently an Associate Professor at the University of Warwick. In 2019, he was awarded an ERC Consolidator grant to study how sampling algorithms can explain human cognition.

Ute Schmid Professor Ute Schmid, Cognitive System Group, University of Bamberg

Title: "Learning to Delete - Interactive Learning with Mutual Explanations to Get Rid of Digital Clutter"

Abstract :
With the ongoing digitalisation an increasing amount of digital data is being stored on personal and company devices. While the digital storage of data can be used for efficient information retrieval, data analytics and machine learning, we also encounter a growing amount of digital clutter, which is unnecessarily occupying storage space and making it difficult to keep track of relevant files and other digital entities. The interactive companion system Dare2Del is designed as a cognitive companion to support employees in administration and industry by identifying irrelevant digital objects which can be deleted or archived. The application addresses some challenges for human-like computation: Whether a digital object is irrelevant or not is partially dependent on fixed rules and regulations and partially on personal preferences which cannot be predicted and might even change over time. While fixed rules can easily be handcrafted for the system, there is no ground truth available to derive a user‘s personal preferences from. These have to be approximated during an interactive process: The user is presented with a small selection of digital objects that are classified as irrelevant according to the system‘s current classification procedure. He or she can either confirm or reject the system‘s proposal and this feedback will be used for incremental learning. Dare2Del is realized with an inductive logic programming approach. The classification of digital objects as relevant or irrelevant is based on a theory represented as Prolog rules. Fixed rules and regulations can be pre-defined and are combined with rules induced from interactive learning. Which digital objects are presented to the user is determined by the current context. Generating and exploiting explanations is a crucial factor for making Dare2Del a trustworthy companion. For each object assumed to be irrelevant, the user can request an explanation justifying the system‘s decision. Verbal explanations are generated from the learned and predefined Prolog rules which have been instantiated with the current object. The explanations are integrated into the file manager, where the relevant features of the analysed digital objects are highlighted. The user is given the opportunity to reject a certain classification and explicitly state which parts of the explanations are incorrect. This feedback is integrated into the process of learning a revised model in the form of constraints. Besides using verbal explanations, we explore how near-miss examples can support the transparency of Dare2Del.

Short biography:
Ute Schmid holds a diploma in psychology and a diploma in computer science, both from Technical University Berlin (TUB), Germany. She received her doctoral degree (Dr. rer.nat.) in computer science from TUB in 1994 and her habilitation in computer science in 2002. From 1994 to 2001 she was assistant professor (wissenschaftliche Assistentin) at the AI/Machine Learning group, Department of Computer Science, TUB. Afterwards she worked as lecturer (akademische Rätin) for Intelligent Systems at the Department of Mathematics and Computer Science at University Osnabrück. Since 2004 she holds a professorship of Applied Computer Science/Cognitive Systems at the University of Bamberg. Research interests of Ute Schmid are mainly in the domain of comprehensible machine learning, explainable AI, and high-level learning on relational data, especially inductive programming, knowledge level learning from planning, learning structural prototypes, analogical problem solving and learning. Further research is on various applications of machine learning (e.g., classifier learning from medical data and for facial expressions) and empirical and experimental work on high-level cognitive processes. Ute Schmid dedicates a significant amount of her time to measures supporting women in computer science and to promote computer science as a topic in elementary, primary, and secondary education.
Professor Gabriella Vigliocco, Department of Experimental Psychology, University College London

Title: "There is more than Linguistic Information to Language"

Most often, our psychological and/or linguistic theories present a picture of language in which multifaceted phenomena (such as processing a sentence; or processing the meaning of a word) tend to be reduced to linguistic processes without much consideration of the physical and social context in which language is used. However, language is most often used in face-to-face interactions, where ‘linguistic’ information is inexorably intertwined with ‘non-linguistic’ information, relevant to the content of communication (e.g., co-speech gestures or mouth movements). In a similar vein - with little exception - models in CL and NLP also do not take ‘non-linguistic’ information into account. I will present results from behavioural and electro-physiological studies that show how the ‘non-linguistic’ information is used online by humans, thus, calling for human and machine models that take the physical and social context seriously.

Short biography:
Gabriella Vigliocco is Professor of the Psychology of Language in the Department of Experimental Psychology at University College London. She was awarded her PhD from the University of Trieste in 1995 and completed her postdoctoral studies at the University of Arizona before serving as a visiting scientist at the Max Planck Institute for Psycholinguistics between 1999 and 2000. Vigliocco leads a multi-disciplinary team comprising psychologists, linguists, computer scientists and cognitive neuroscientists who share a vision that the integration of multiple levels of analysis and the use of different methodological approaches can lead to a better understanding language and cognition. They seek to understand the relationship between language and other aspects of cognitive function and to use this knowledge to impact education and improve the lives of people with language disorders.