Machine Teaching: An Inverse Problem to Machine Learning and an Approach toward Optimal Education by Xiaojin Zhu, Machine Teaching: A New Paradigm for Building Machine Learning Systems by Patrice Y. Simard, Saleema Amershi, David M. Chickering, Alicia Edelman Pelton, Soroush Ghorashi, Christopher Meek, Gonzalo Ramos, Jina Suh, Johan Verwey, Mo Wang and John Wernsing, An Overview of Machine Teaching by Xiaojin Zhu, Adish Singla, Sandra Zilles and Anna N. Rafferty, Teacher Improves Learning by Selecting a Training Subset by Yuzhe Ma, Robert Nowak, Philippe Rigollet, Xuezhou Zhang and Xiaojin Zhu, An Optimal Control Approach to Sequential Machine Teaching by Laurent Lessard, Xuezhou Zhang and Xiaojin Zhu, Iterative Machine Teaching by Weiyang Liu, Bo Dai, Ahmad Humayun, Charlene Tay, Chen Yu, Linda B. Smith, James M. Rehg and Le Song, Understanding the Role of Adaptivity in Machine Teaching: The Case of Version Space Learners by Yuxin Chen, Adish Singla, Oisin M. Aodha, Pietro Perona and Yisong Yue, Towards Black-box Iterative Machine Teaching by Weiyang Liu, Bo Dai, Xingguo Li, Zhen Liu, James M. Rehg and Le Song and Teaching a Black-box Learner by Sanjoy Dasgupta, Daniel Hsu, Stefanos Poulis and Xiaojin Zhu.
Universal Psychometrics: Measuring Cognitive Abilities in the Machine Kingdom by José Hernández-Orallo, David L. Dowe and M. Victoria Hernández-Lloreda, Can Machine Intelligence Be Measured in the Same Way as Human Intelligence? by Tarek Besold, José Hernández-Orallo and Ute Schmid, The Measure of All Minds: Evaluating Natural and Artificial Intelligence by José Hernández-Orallo, Historical Account of Computer Models Solving IQ Test Problems by Fernando Martınez-Plumed, José Hernández-Orallo, Ute Schmid, Michael Siebers and David L. Dowe, Computer Models Solving Intelligence Test Problems: Progress and Implications by José Hernández-Orallo, Fernando Martínez-Plumed, Ute Schmid, Michael Siebers and David L. Dowe, Verbal IQ of a Four-year Old Achieved by an AI System by Stellan Ohlsson, Robert H. Sloan, György Turán and Aaron Urasky, A Program for the Solution of a Class of Geometric-analogy Intelligence-test Questions by Thomas G. Evans, Solving Raven's IQ-tests: An AI and Cognitive Modeling Approach by Marco Ragni and Stefanie Neubert, Analyzing Raven's Intelligence Test: Cognitive Model, Demand, and Complexity by Marco Ragni and Stefanie Neubert, Measuring Abstract Reasoning in Neural Networks by Adam Santoro, Felix Hill, David Barrett, Ari Morcos and Timothy Lillicrap, DeepIQ: A Human-Inspired AI System for Solving IQ Test Problems by Jacek Mańdziuk and Adam Żychowski, Complexity in Analogy Tasks: An Analysis and Computational Model by Philip Stahl and Marco Ragni, Predicting Numbers: An AI Approach to Solving Number Series by Marco Ragni and Andreas Klein, An Anthropomorphic Method for Number Sequence Problems by Claes Strannegård, Mehrdad Amirghasemi and Simon Ulfsbäcker, Deep Neural Solver for Math Word Problems by Yan Wang, Xiaojiang Liu and Shuming Shi, Analysing Mathematical Reasoning Abilities of Neural Models by David Saxton, Edward Grefenstette, Felix Hill and Pushmeet Kohli, The Winograd Schema Challenge by Hector J. Levesque, Ernest Davis and Leora Morgenstern, Watson: Beyond Jeopardy! by David Ferrucci, Anthony Levas, Sugato Bagchi, David Gondek and Erik T. Mueller, A Study of the Knowledge Base Requirements for Passing an Elementary Science Test by Peter Clark, Philip Harrison and Niranjan Balasubramanian, MCTest: A Challenge Dataset for the Open-domain Machine Comprehension of Text by Matthew Richardson, Christopher J. C. Burges and Erin Renshaw, Recognizing Textual Entailment: Models and Applications by Ido Dagan, Dan Roth, Mark Sammons and Fabio M. Zanzotto, VQA: Visual Question Answering by Stanislaw Antol, Aishwarya Agrawal, Jiasen Lu, Margaret Mitchell, Dhruv Batra, C. Lawrence Zitnick and Devi Parikh, Towards AI-complete Question Answering: A Set of Prerequisite Toy Tasks by Jason Weston, Antoine Bordes, Sumit Chopra and Tomas Mikolov, Combining Retrieval, Statistics, and Inference to Answer Elementary Science Questions by Peter Clark, Oren Etzioni, Tushar Khot, Ashish Sabharwal, Oyvind Tafjord, Peter Turney and Daniel Khashabi, Turing++ Questions: A Test for the Science of (Human) Intelligence by Tomaso Poggio and Ethan Meyers, My Computer Is an Honor Student — but How Intelligent Is It? Standardized Tests as a Measure of AI by Peter Clark and Oren Etzioni, A Dynamic Intelligence Test Framework for Evaluating AI Agents by Nader Chmait, Yuan-Fang Li, David L. Dowe and David G. Green, Using Thought-provoking Children's Questions to Drive Artificial Intelligence Research by Erik T. Mueller and Henry Minsky and How Well Do Machines Perform on IQ Tests: A Comparison Study on a Large-scale Dataset by Yusen Liu, Fangyuan He, Haodi Zhang, Guozheng Rao, Zhiyong Feng and Yi Zhou.
Psychology of Music edited by Diana Deutsch, Oxford Handbook of Music Psychology edited by Susan Hallam, Ian Cross and Michael Thaut, Music, Language, and the Brain by Aniruddh D. Patel, Understanding Music with AI: Perspectives on Music Cognition edited by Mira Balaban, Kemal Ebcioğlu and Otto Laske, Readings in Music and Artificial Intelligence edited by Eduardo R. Miranda and Music, Mind and Machine: Studies in Computer Music, Music Cognition and Artificial Intelligence by Peter Desain and Henkjan Honing.
Emotion and Meaning in Music by Leonard B. Meyer, Emotional Expression in Music Performance: Between the Performer's Intention and the Listener's Experience by Alf Gabrielsson and Patrik N. Juslin, The Effects of Different Types of Music on Mood, Tension, and Mental Clarity by Rollin McCraty, Bob Barrios-Choplin, Michael Atkinson and Dana Tomasino, Emotion Induction through Music: A Review of the Musical Mood Induction Procedure by Daniel Västfjäll, From Everyday Emotions to Aesthetic Emotions: Towards a Unified Theory of Musical Emotions by Patrik N. Juslin, Which Emotions Can be Induced by Music? What are the Underlying Mechanisms? And How Can We Measure Them? by Klaus R. Scherer, Emotional Responses to Music: The Need to Consider Underlying Mechanisms by Patrik N. Juslin and Daniel Västfjäll, Combining Music with Thought to Change Mood by Eric Eich, Joycelin T. W. Ng, Dawn Macaulay, Alexandra D. Percy and Irina Grebneva, Modeling Listeners' Emotional Response to Music by Tuomas Eerola, The Functions of Music for Affect Regulation by Annelies van Goethem and John Sloboda, Emotion Regulation through Listening to Music in Everyday Situations by Myriam V. Thoma, Stefan Ryf, Changiz Mohiyeddini, Ulrike Ehlert and Urs M. Nater and Personalized Affective Music Player by Joris H. Janssen, Egon L. van den Broek and Joyce H. D. M. Westerink.
The Effects of Music on Athletic Performance by Haluk Koç and Turchıan Curtseıt, The Effect of Music on Athletic Cardio-respiratory Responses and Perceived Exertion Rate during Incremental Exercise by Hamed Barzegar, Rahman Soori, Ali Akbarnejad and Elham Vosadi, Effect of Music Tempo on Exercise Performance and Heart Rate among Young Adults by Avinash E. Thakare, Ranjeeta Mehrotra and Ayushi Singh, The Effect of Music Playlist Tempo on Self-paced Running, Mood, and Attentional Focus Tendencies by Kristopher Bly, Psychophysical and Ergogenic Effects of Synchronous Music during Treadmill Walking by Costas I. Karageorghis, Denis A. Mouzourides, David-Lee Priest, Tariq A. Sasso, Daley J. Morrish and Carolyn L. Walley, Effects of Motivational Music on a 1.5 Mile Running Time Trial by Jamie C. Aweau, A Motivational Music and Video Intervention Improves High-intensity Exercise Performance by Martin J. Barwood, Neil J. V. Weston, Richard Thelwell and Jennifer Page, Applying Music in Exercise and Sport by Costas I. Karageorghis and A Personalized Music System for Motivation in Sport Performance by Gertjan Wijnalda, Steffen Pauws, Fabio Vignoli and Heiner Stuckenschmidt.
A Guide to Musical Analysis by Nicholas Cook, A System for the Analysis of Musical Data by Stuart Pullinger, Music Representation: Issues, Techniques, and Systems by Roger B. Dannenberg, The Music Structures Approach to Knowledge Representation for Music Processing by Mira Balaban, A Generative Theory of Tonal Music by Fred Lerdahl and Ray S. Jackendoff, Features for Audio and Music Classification by Martin McKinney and Jeroen Breebaart, The Music Genome Project by Michael Castelluccio, Moving beyond Feature Design: Deep Architectures and Automatic Feature Learning in Music Informatics by Eric J. Humphrey, Juan Pablo Bello and Yann LeCun, Improved Music Feature Learning with Deep Neural Networks by Siddharth Sigtia and Simon Dixon and What is Musical Prosody? by Caroline Palmer and Sean Hutchins.
An Essay on the Psychology of Invention in the Mathematical Field by Jacques Hadamard, The Psychology of Advanced Mathematical Thinking by David Tall, What is Mathematical Thinking by Robert J. Sternberg, Mathematical Thinking and Learning by Herbert P. Ginsburg, Joanna Cannon, Janet Eisenband and Sandra Pappas, Mathematical Problem Solving by Alan H. Schoenfeld, How to Solve It: A New Aspect of Mathematical Method by George Polya, The Computer Modelling of Mathematical Reasoning by Alan Bundy, Modelling the Way Mathematics is Actually Done by Joseph Corneli, Ursula Martin, Dave Murray-Rust, Alison Pease, Raymond Puzio and Gabriela R. Nesin and Proof Assistants: History, Ideas and Future by Herman Geuvers.
Learning from Previous Proof Experience: A Survey by Jörg Denzinger, Matthias Fuchs, Christoph Goller and Stephan Schulz, Automatic Acquisition of Search Control Knowledge from Multiple Proof Attempts by Jörg Denzinger and Stephan Schulz, Reinforcement Learning of Theorem Proving by Cezary Kaliszyk, Josef Urban, Henryk Michalewski and Miroslav Olšák, Learning Heuristics for Automated Reasoning through Deep Reinforcement Learning by Gil Lederman, Markus N. Rabe and Sanjit A. Seshia, DeepMath - Deep Sequence Models for Premise Selection by Geoffrey Irving, Christian Szegedy, Alexander A. Alemi, Niklas Een, François Chollet and Josef Urban, Deep Network Guided Proof Search by Sarah M. Loos, Geoffrey Irving, Christian Szegedy and Cezary Kaliszyk, Learning to Prove with Tactics by Thibault Gauthier, Cezary Kaliszyk, Josef Urban, Ramana Kumar and Michael Norrish and Hierarchical Invention of Theorem Proving Strategies by Jan Jakubův and Josef Urban.
Incremental Interpretation by Fernando C. N. Pereira and Martha E. Pollack, Incremental Interpretation edited by David Milward and Patrick Sturt, What is Incremental Interpretation by Nick Chater, Martin Pickering and David Milward, Computational Models of Incremental Semantic Interpretation by Nicholas J. Haddock, The Information-processing Difficulty of Incremental Parsing by John Hale, Incremental Interpretation and Prediction of Utterance Meaning for Interactive Dialogue by David DeVault, Kenji Sagae and David Traum and Incremental Dialogue Understanding and Feedback for Multiparty, Multimodal Conversation by David Traum, David DeVault, Jina Lee, Zhiyang Wang and Stacy Marsella.
Rationale and Methods for Abductive Reasoning in Natural-language Interpretation by Mark E. Stickel, Interpretation as Abduction by Jerry R. Hobbs, Mark E. Stickel, Douglas E. Appelt and Paul Martin, Abductive Reasoning in Peirce's and Davidson's Account of Interpretation by Uwe Wirth, Layered Abduction for Speech Recognition from Articulation by Richard K. Fox, Abduction in Natural Language Understanding by Jerry R. Hobbs, Abductive Speech Act Recognition by Elizabeth A. Hinhelman, Abduction for Discourse Interpretation: A Probabilistic Framework by Ekaterina Ovchinnikova, Andrew Gordon and Jerry R. Hobbs and Abduction, Belief and Context in Dialogue: Studies in Computational Pragmatics edited by Harry Bunt and William Black.
Interactive Task Learning by John E. Laird, Kevin A. Gluck, John Anderson, Kenneth D. Forbus, Odest C. Jenkins, Christian Lebiere, Dario Salvucci, Matthias Scheutz, Andrea Thomaz, Greg Trafton, Robert E. Wray, Shiwali Mohan and James R. Kirk and Interactive Task Learning: Humans, Robots, and Agents Acquiring New Tasks through Natural Interactions edited by Kevin A. Gluck and John E. Laird.
Multi-agent Reinforcement Learning: Independent vs. Cooperative Agents by Ming Tan, The Dynamics of Reinforcement Learning in Cooperative Multiagent Systems by Caroline Claus and Craig Boutilier, Implicit Imitation in Multiagent Reinforcement Learning by Bob Price and Craig Boutilier, Observational Learning by Reinforcement Learning by Diana Borsa, Nicolas Heess, Bilal Piot, Siqi Liu, Leonard Hasenclever, Remi Munos and Olivier Pietquin, Learning in Multi-agent Systems by Eduardo Alonso, Mark D'inverno, Daniel Kudenko, Michael Luck and Jason Noble and Learning to Teach in Cooperative Multiagent Reinforcement Learning by Shayegan Omidshafiei, Dong-Ki Kim, Miao Liu, Gerald Tesauro, Matthew Riemer, Christopher Amato, Murray Campbell and Jonathan P. How.
Metacontrol for Adaptive Imagination-based Optimization by Jessica B. Hamrick, Andrew J. Ballard, Razvan Pascanu, Oriol Vinyals, Nicolas Heess and Peter W. Battaglia and Imagination-augmented Agents for Deep Reinforcement Learning by Theophane Weber, Sébastien Racanière, David P. Reichert, Lars Buesing, Arthur Guez, Danilo J. Rezende, Adrià P. Badia, Oriol Vinyals, Nicolas Heess, Yujia Li, Razvan Pascanu, Peter W. Battaglia, David Silver and Daan Wierstra.
Machine Learning Methods for Planning edited by Steven Minton, Reinforcement Learning and Automated Planning: A Survey by Ioannis Partalas, Dimitris Vrakas and Ioannis Vlahavas, Combining Reinforcement Learning with Symbolic Planning by Matthew Grounds and Daniel Kudenko, Learning Model-based Planning from Scratch by Razvan Pascanu, Yujia Li, Oriol Vinyals, Nicolas Heess, Lars Buesing, Sebastien Racanière, David Reichert, Théophane Weber, Daan Wierstra and Peter Battaglia, The Predictron: End-to-end Learning and Planning by David Silver, Hado van Hasselt, Matteo Hessel, Tom Schaul, Arthur Guez, Tim Harley, Gabriel Dulac-Arnold, David Reichert, Neil Rabinowitz, Andre Barreto and Thomas Degris and Model-based Planning with Discrete and Continuous Actions by Mikael Henaff, William F. Whitney and Yann LeCun.
A Brief Survey of Deep Reinforcement Learning by Kai Arulkumaran, Marc P. Deisenroth, Miles Brundage and Anil A. Bharath, Deep Reinforcement Learning by Yuxi Li, Human-level Control through Deep Reinforcement Learning by Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Andrei A. Rusu, Joel Veness, Marc G. Bellemare, Alex Graves, Martin Riedmiller, Andreas K. Fidjeland, Georg Ostrovski, Stig Petersen, Charles Beattie, Amir Sadik, Ioannis Antonoglou, Helen King, Dharshan Kumaran, Daan Wierstra, Shane Legg and Demis Hassabis, Massively Parallel Methods for Deep Reinforcement Learning by Arun Nair, Praveen Srinivasan, Sam Blackwell, Cagdas Alcicek, Rory Fearon, Alessandro De Maria, Vedavyas Panneershelvam, Mustafa Suleyman, Charles Beattie, Stig Petersen, Shane Legg, Volodymyr Mnih, Koray Kavukcuoglu and David Silver and IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-learner Architectures by Lasse Espeholt, Hubert Soyer, Remi Munos, Karen Simonyan, Volodymyr Mnih, Tom Ward, Yotam Doron, Vlad Firoiu, Tim Harley, Iain Dunning, Shane Legg and Koray Kavukcuoglu.
Evolutionary Neuroscience edited by Jon H. Kaas, Evolution of the Brain and Intelligence by Harry Jerison, Structure and Evolution of Invertebrate Nervous Systems by Andreas Schmidt-Rhaesa, Steffen Harzsch and Günter Purschke, Brain Organization and the Origin of Insects: An Assessment by Nicholas J. Strausfeld, The Evolution of Brains from Early Mammals to Humans by Jon H. Kaas, The Limbic System in Mammalian Brain Evolution by Roger L. Reep, Barb L. Finlay and Richard B. Darlington, The Anterior Cingulate Cortex: The Evolution of an Interface between Emotion and Cognition by John M. Allman, Atiya Hakeem, Joseph M. Erwin, Esther Nimchinsky and Patrick Hof, Architecture, Neurocytology and Comparative Organization of Monkey and Human Cingulate Cortices by Brent A. Vogt, The Anterior Cingulate Gyrus and the Mechanism of Self-regulation by Michael I. Posner, Mary K. Rothbart, Brad E. Sheese and Yiyuan Tang, Towards a Human Self-regulation System: Common and Distinct Neural Signatures of Emotional and Behavioural Control by Robert Langner, Susanne Leiberg, Felix Hoffstaedter and Simon B. Eickhoff, Evolutionary Developmental Biology Meets the Brain: The Origins of Mammalian Cortex by Harvey J. Karten, The Emergence and Evolution of Mammalian Neocortex by R. Glenn Northcutt and Jon H. Kaas, Evolution of the Neocortex: A Perspective from Developmental Biology by Pasko Rakic, Linked Regularities in the Development and Evolution of Mammalian Brains by Barbara L. Finlay and Richard B. Darlington, Modeling Transformations of Neurodevelopmental Sequences across Mammalian Species by Alan D. Workman, Christine J. Charvet, Barbara Clancy, Richard B. Darlington and Barbara L. Finlay and On the Evolutionary Origins of Executive Functions by Alfredo Ardila.
Embryogenesis of Behavioral Nerve Nets by Roger W. Sperry, An Ethological Approach to the Genetical Study of Human Behavior by Daniel Freedman, Neuro-behavioral Ontogeny: A Synthesis of Ethological and Neurophysiological Concepts by Michael W. Fox, Cellular Mechanisms Underlying Behavior - Neuroethology by Graham Hoyle, The Roles of Experience in the Development of Behavior and the Nervous System by Gilbert Gottlieb, Environmental and Neural Determinants of Behavior in Development by Timothy H. Moran, Structure and Development of Behavior Systems by Jerry A. Hogan, Development of Behavior Systems by Jerry A. Hogan, Behavioral Neurobiology: The Cellular Organization of Natural Behavior by James Park, Blueprints for Behavior: Genetic Specification of Neural Circuitry for Innate Behaviors by Devanand S. Manoli, Geoffrey W. Meissner and Bruce S. Baker and Wired for Behaviors: From Development to Function of Innate Limbic System Circuitry by Katie Sokolowski and Joshua G. Corbin.
Modeling Neural Development edited by Arjen van Ooyen, Using Theoretical Models to Analyse Neural Development by Arjen van Ooyen, Artificial Life Models of Neural Development by Angelo Cangelosi, Stefano Nolfi and Domenico Parisi, Gene Regulation and Biological Development in Neural Networks: An Exploratory Model by Angelo Cangelosi and Jeffrey L. Elman, Artificial Neurogenesis: An Introduction and Selective Review by Taras Kowaliw, Nicolas Bredeche, Sylvain Chevallier and René Doursat, Artificial Development by Simon Harding and Wolfgang Banzhaf, Simulating Evolution with a Computational Model of Embryogeny by Christopher P. Bowers, Using Embryonic Stages to Increase the Evolvability of Development by Diego Federici, Three Ways to Grow Designs: A Comparison of Embryogenies for an Evolutionary Design Problem by Peter Bentley and Sanjeev Kumar and A Taxonomy for Artificial Embryogeny by Kenneth O. Stanley and Risto Miikkulainen.
An Overview of Neuroevolution Techniques by Vincent Hoekstra, Neural Architecture Search: A Survey by Thomas Elsken, Jan H. Metzen and Frank Hutter, Evolving Artificial Neural Networks by Xin Yao, A New Evolutionary System for Evolving Artificial Neural Networks by Xin Yao and Yong Liu, A Genetic Programming Approach to Designing Convolutional Neural Network Architectures by Masanori Suganuma, Shinichi Shirakawa and Tomoharu Nagao, An Evolutionary Algorithm that Constructs Recurrent Neural Networks by Peter J. Angeline, Gregory M. Saunders and Jordan B. Pollack, Evolving Neural Networks through Augmenting Topologies by Kenneth O. Stanley and Risto Miikkulainen, Evolving Deep Neural Networks by Risto Miikkulainen, Jason Liang, Elliot Meyerson, Aditya Rawal, Dan Fink, Olivier Francon, Bala Raju, Hormoz Shahrzad, Arshak Navruzyan, Nigel Duffy and Babak Hodjat, Evolutionary Approach to Machine Learning and Deep Neural Networks - Neuro-evolution and Gene Regulatory Networks by Hitoshi Iba, Neuroevolution: From Architectures to Learning by Dario Floreano, Peter Dürr and Claudio Mattiussi, Efficient Reinforcement Learning through Evolving Neural Network Topologies by Kenneth O. Stanley and Risto Miikkulainen, Hierarchical Representations for Efficient Architecture Search by Hanxiao Liu, Karen Simonyan, Oriol Vinyals, Chrisantha Fernando and Koray Kavukcuoglu and Evolving a Neurocontroller through a Process of Embryogeny by Diego Federici.