Research and Development

Machine Teaching


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.

Psychometric Artificial Intelligence

Natural Intelligence

Handbook of Intelligence edited by Robert J. Sternberg, The Cambridge Handbook of Intelligence edited by Robert J. Sternberg and Scott Barry Kaufman, The Neuroscience of Intelligence by Richard J. Haier, Intelligence and Cognitive Abilities as Competencies in Development by Damian P. Birney and Robert J. Sternberg and A Computational Analysis of General Intelligence Tests for Evaluating Cognitive Development by Fernando Martínez-Plumed, César Ferri, José Hernández-Orallo and María J. Ramírez-Quintana.

Artificial Intelligence

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.

Artificial Wisdom


Wisdom: Its Nature, Origins, and Development edited by Robert J. Sternberg, People Nominated as Wise: A Comparative Study of Wisdom-related Knowledge by Paul B. Baltes, Ursula M. Staudinger, Andreas Maercker and Jacqui Smith, Defining and Assessing Wisdom: A Review of the Literature by Katherine J. Bangen, Thomas W. Meeks and Dilip V. Jeste, What is Wisdom? Cross-cultural and Cross-disciplinary Syntheses by Roger Walsh and The Cambridge Handbook of Wisdom edited by Robert J. Sternberg and Judith Glück.

Conversational Search and Recommendation

A Theoretical Framework for Conversational Search by Filip Radlinski and Nick Craswell, Towards Conversational Recommender Systems by Konstantina Christakopoulou, Filip Radlinski and Katja Hofmann and Conversational Recommender System by Yueming Sun and Yi Zhang.


Anecdote Recognition and Recommendation by Wei Song, Ruiji Fu, Lizhen Liu, Hanshi Wang and Ting Liu.


Proverbs: A Handbook by Wolfgang Mieder, A Proverb in Mind: The Cognitive Science of Proverbial Wit and Wisdom by Richard P. Honeck, Comprehension and the Interpretation of Proverbs by Susan Kemper and Introduction to Paremiology: A Comprehensive Guide to Proverb Studies edited by Hrisztalina Hrisztova-Gotthardt and Melita Aleksa Varga.


Words to Live By: Scholarly Quotations as Proverbial Theory by Corey Anton, Learning to Recommend Quotes for Writing by Jiwei Tan, Xiaojun Wan and Jianguo Xiao and Quote Recommendation for Dialogs and Writings by Yeonchan Ahn, Hanbit Lee, Heesik Jeon, Seungdo Ha and Sang-goo Lee.


Music Search Engines: Specifications and Challenges by Alexandros Nanopoulos, Dimitrios Rafailidis, Maria M. Ruxanda and Yannis Manolopoulos, Music Recommender Systems by Markus Schedl, Peter Knees, Brian McFee, Dmitry Bogdanov and Marius Kaminskas and Current Challenges and Visions in Music Recommender Systems Research by Markus Schedl, Hamed Zamani, Ching-Wei Chen, Yashar Deldjoo and Mehdi Elahi.


An Introduction to Poetry by X. J. Kennedy and Dana Gioia and Cognitive Poetics: An Introduction by Peter Stockwell.


Interpretation, Allegory, and Allegoresis by Peter Berek, Allegory and Allegoresis, Rhetoric and Hermeneutics by Rita Copeland and Stephen Melville and A Survey of Book Recommender Systems by Haifa Alharthi, Diana Inkpen and Stan Szpakowicz.


What is an Allusion? by William Irwin, The Poetics of Literary Allusion by Ziva Ben-Porat, Intertextuality, Allusion, and Quotation: An International Bibliography of Criticial Studies by Udo J. Hebel, Beyond Literal Meanings: The Psychology of Allusion by Sam Glucksberg and The Aesthetics of Allusion by William T. Irwin.


Computational Humor by Kim Binsted, Benjamin Bergen, Seana Coulson, Anton Nijholt, Oliviero Stock, Carlo Strapparava, Graeme Ritchie, Ruli Manurung, Helen Pain, Annalu Waller and Dave O'Mara and Joke Retrieval: Recognizing the Same Joke Told Differently by Lisa Friedland and James Allan.

Artificial Intelligence and Music


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.


Aesthetics: An Introduction to the Philosophy of Art by Anne Sheppard, Philosophy of the Arts: An Introduction to Aesthetics by Gordon Graham, Aesthetics and Cognitive Science by Gregory Currie, Aesthetics and Cognitive Science by Dustin Stokes and The Aesthetics of Music by Roger Scruton.


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.

Athletic Performance

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.


The Effect of Music Listening on Work Performance by Teresa Lesiuk, The Effect of Preferred Music on Mood and Performance in a High-cognitive Demand Occupation by Teresa Lesiuk, Background Music and Cognitive Performance by Leslie A. Angel, Donald J. Polzella and Greg C. Elvers and The Impact of Background Music on Adult Listeners: A Meta-analysis by Juliane Kämpfe, Peter Sedlmeier and Frank Renkewitz.


Effects of Mood States on Creativity by Franck Zenasni and Todd Lubart and The Effects of Musical Mood Induction on Creativity by Jill E. Adaman and Paul H. Blaney.


Optimal Experience: Psychological Studies of Flow in Consciousness edited by Mihaly Csikszentmihalyi and Isabella S. Csikszentmihalyi and Mindfulness, Attention, and Flow during Music Listening: An Empirical Investigation by Frank M. Diaz.

Studying and Concentration

The Perceived Impact of Playing Music while Studying: Age and Cultural Differences by Anastasia Kotsopoulou and Susan Hallam, The Effects of Different Types of Music on Mood, Tension, and Mental Clarity by Rollin McCraty, Bob Barrios-Choplin, Michael Atkinson and Dana Tomasino, Background Music and Cognitive Performance by Leslie A. Angel, Donald J. Polzella and Greg C. Elvers and The Impact of Background Music on Adult Listeners: A Meta-analysis by Juliane Kämpfe, Peter Sedlmeier and Frank Renkewitz.

Problem Solving

Human Problem Solving by Allen Newell and Herbert A. Simon, Effect of Music on Cooperative Problem Solving in Children by Sanford L. Chertock and Relationship between Musical Accompaniment and Learning Style in Problem Solving by Linda Burton.

Intelligent Tutoring Systems

Affect-aware Tutors: Recognising and Responding to Student Affect by Beverly P. Woolf, Winslow Burleson, Ivon Arroyo, Toby Dragon, David Cooper and Rosalind Picard, Multimodal Affect Recognition in Intelligent Tutoring Systems by Ntombikayise Banda and Peter Robinson and Affect and Cognitive Processes in Educational Contexts by Klaus Fiedler and Susanne Beier.

Affective Computing

Affective Computing by Rosalind W. Picard, Recognition and Simulation of Emotions by Christian Kleine-Cosack, Recognition of Vocal Emotions from Acoustic Profile by Krishna Asawa, Vikrant Verma and Ankit Agrawal, Multimodal Emotion Recognition for Human-computer Interaction: A Survey by Michele Mukeshimana, Xiaojuan Ban, Nelson Karani and Ruoyi Liu, Mind-reading Machines: Automated Inference of Complex Mental States by Rana A. El Kaliouby and Empirically Building and Evaluating a Probabilistic Model of User Affect by Cristina Conati and Heather Maclaren.

Computational Representation of Music

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.

Music Understanding

The Origins of Music Perception and Cognition: A Developmental Perspective by Sandra Trehub, E. Glenn Schellenberg and David S. Hill, Thinking in Sound: The Cognitive Psychology of Human Audition edited by Stephen McAdams and Emmanuel Bigand, Exploring the Functional Neuroanatomy of Music Performance, Perception, and Comprehension by Lawrence M. Parsons, Perception and Cognition of Music edited by Irène Deliège and John A. Sloboda, Towards a Neural Basis of Music Perception by Stefan Koelsch and Walter A. Siebel, Music Cognition and the Cognitive Sciences by Marcus Pearce and Martin Rohrmeier and Understanding Music with AI: Perspectives on Music Cognition edited by Mira Balaban, Kemal Ebcioğlu and Otto Laske.

Music Generation

Algorithmic Composition: Paradigms of Automated Music Generation by Gerhard Nierhaus, Algorithmic Composition: Computational Thinking in Music by Michael Edwards, AI Methods in Algorithmic Composition: A Comprehensive Survey by Jose D. Fernández and Francisco Vico and Deep Learning Techniques for Music Generation - A Survey by Jean-Pierre Briot, Gaëtan Hadjeres and François Pachet.

Recommender Systems

Music Recommender Systems by Markus Schedl, Peter Knees, Brian McFee, Dmitry Bogdanov and Marius Kaminskas, Current Challenges and Visions in Music Recommender Systems Research by Markus Schedl, Hamed Zamani, Ching-Wei Chen, Yashar Deldjoo and Mehdi Elahi and A Comparative Study of Music Recommendation Systems by Ashish Patel and Rajesh Wadhvani.

Playlist Generation

"More of an Art than a Science": Supporting the Creation of Playlists and Mixes by Sally Jo Cunningham, David Bainbridge and Annette Falconer, A Discussion of Musical Features for Automatic Music Playlist Generation using Affective Technologies by Darryl Griffiths, Stuart Cunningham and Jonathan Weinel, Towards a Personal Automatic Music Playlist Generation Algorithm: The Need for Contextual Information by Gordon Reynolds, Dan Barry, Ted Burke and Eugene Coyle and Automated Generation of Music Playlists: Survey and Experiments by Geoffray Bonnin and Dietmar Jannach.

Artificial Intelligence and Mathematics


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.

Knowledge Representation and Reasoning

Deep Neural Solver for Math Word Problems by Yan Wang, Xiaojiang Liu and Shuming Shi, Deep Learning for Symbolic Mathematics by Guillaume Lample and François Charton and Analysing Mathematical Reasoning Abilities of Neural Models by David Saxton, Edward Grefenstette, Felix Hill and Pushmeet Kohli.

Theorem Proving

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.

Abductive Reasoning and Interpretation

Abductive Reasoning

Peirce's Theory of Abduction by Arthur W. Burks, The Inference to the Best Explanation by Gilbert H. Harman, On the Mechanization of Abductive Logic by Harry E. Pople, The Computational Complexity of Abduction by Tom Bylander, Dean Allemang, Michael C. Tanner and John R. Josephson and Abductive Inference: Computation, Philosophy, Technology edited by John R. Josephson and Susan G. Josephson.

Incremental Interpretation

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.

Natural Language

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.

Social Cognition

How People Explain Behavior: A New Theoretical Framework by Bertram F. Malle, Understanding Social Interactions using Incremental Abductive Inference by Ben Meadows and Miranda Emery and An Abductive Approach to Understanding Social Interactions by Ben Meadows, Patrick Langley and Miranda Emery.


Conflicting Readings: Variety and Validity in Interpretation by Paul B. Armstrong, Arguing over Intentions by Paisley Livingston, Interpretation and Justification by David Novitz, Validity in Interpretation by Eric D. Hirsch, Validity in Interpretation and the Literary Institution by Ken M. Newton and Argumentation and the Social Grounds of Knowledge by Charles A. Willard.

Observational Learning


Observational Learning by Albert Bandura, Social Learning Theory by Albert Bandura and Richard H. Walters and Perspectives on Observational Learning in Animals by Thomas R. Zentall.

Programming by Demonstration

Robot Learning from Human Teachers by Sonia Chernova and Andrea L. Thomaz, Robot Learning from Demonstration by Christopher G. Atkeson and Stefan Schaal, A Survey of Robot Learning from Demonstration by Brenna D. Argall, Sonia Chernova, Manuela Veloso and Brett Browning, Robot Programming by Demonstration by Aude Billard, Sylvain Calinon, Ruediger Dillmann and Stefan Schaal, Programming by Demonstration: A Taxonomy of Current Relevant Methods to Teach and Describe New Skills to Robots by Jordi Bautista-Ballester, Jaume Vergés-Llahí and Domenec Puig and Learning Procedural Knowledge through Observation by Michael van Lent and John E. Laird.

Interactive Task Learning

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 Systems

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.

Reinforcement Learning


Reinforcement Learning: An Introduction by Richard S. Sutton and Andrew G. Barto and Algorithms for Reinforcement Learning by Csaba Szepesvári.

Multi-armed Bandits

On the Likelihood that One Unknown Probability Exceeds Another in View of the Evidence of Two Samples by William R. Thompson, On the Theory of Apportionment by William R. Thompson, Some Aspects of the Sequential Design of Experiments by Herbert Robbins, A Problem in the Sequential Design of Experiments by Richard Bellman and Bandit Problems: Sequential Allocation of Experiments by Donald A. Berry and Bert Fristedt.

Markov Decision Processes

A Markovian Decision Process by Richard Bellman, Dynamic Programming and Markov Processes by Ronald A. Howard, Learning Machines: A Unified View by John H. Andreae, A Set of Successive Approximation Methods for Discounted Markovian Decision Problems by Johannes A. E. E. van Nunen and Modified Policy Iteration Algorithms for Discounted Markov Decision Problems by Martin L. Puterman and Moon C. Shin.

Learning Automata

Learning Automata - A Survey by Kumpati S. Narendra and Mandayam A. L. Thathachar and Learning Automata: An Introduction by Kumpati S. Narendra and Mandayam A. L. Thathachar.

Dynamic Programming

Dynamic Programming by Richard Bellman, Dynamic Programming by Ronald A. Howard and Learning from Delayed Rewards by Christopher J. C. H. Watkins.

Monte Carlo Methods

Monte Carlo Methods by Malvin H. Kalos and Paula A. Whitlock, Monte Carlo Methods by John Hammersley, Simulation and the Monte Carlo Method by Reuven Y. Rubinstein and Dirk P. Kroese, Monte Carlo: Concepts, Algorithms, and Applications by George Fishman and Monte Carlo Statistical Methods by Christian Robert and George Casella.

Temporal-difference Learning

An Adaptive Optimal Controller for Discrete-time Markov Environments by Ian H. Witten, Temporal Credit Assignment in Reinforcement Learning by Richard S. Sutton, Learning to Predict by the Methods of Temporal Differences by Richard S. Sutton and Analysis of Temporal-diffference Learning with Function Approximation by John N. Tsitsiklis and Benjamin Van Roy.

Multi-step Bootstrapping

Learning from Delayed Rewards by Christopher J. C. H. Watkins, Truncating Temporal Differences: On the Efficient Implementation of TD (lambda) for Reinforcement Learning by Pawel Cichosz and Effective Multi-step Temporal-difference Learning for Non-linear Function Approximation by Harm van Seijen.

Exploration versus Exploitation

The Apparent Conflict between Estimation and Control - A Survey of the Two-armed Bandit Problem by Ian H. Witten, Optimal Control Systems by Aleksandr A. Feldbaum and Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence by John H. Holland.

Function Approximation

Issues in Using Function Approximation for Reinforcement Learning by Sebastian Thrun and Anton Schwartz, Residual Algorithms: Reinforcement Learning with Function Approximation by Leemon Baird, Generalization in Reinforcement Learning: Safely Approximating the Value Function by Justin A. Boyan and Andrew W. Moore, An Analysis of Linear Models, Linear Value-function Approximation, and Feature Selection for Reinforcement Learning by Ronald Parr, Lihong Li, Gavin Taylor, Christopher Painter-Wakefield and Michael L. Littman and An Analysis of Reinforcement Learning with Function Approximation by Francisco S. Melo, Sean P. Meyn and M. Isabel Ribeiro.

Policy Optimization

Comparing Policy-gradient Algorithms by Richard S. Sutton, Satinder P. Singh and David A. McAllester, Policy Gradient Methods for Reinforcement Learning with Function Approximation by Richard S. Sutton, David A. McAllester, Satinder P. Singh and Yishay Mansour and A Class of Gradient-estimating Algorithms for Reinforcement Learning in Neural Networks by Ronald J. Williams.


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.


Multiobjective Reinforcement Learning: A Comprehensive Overview by Chunming Liu, Xin Xu and Dewen Hu, Empirical Evaluation Methods for Multiobjective Reinforcement Learning Algorithms by Peter Vamplew, Richard Dazeley, Adam Berry, Rustam Issabekov and Evan Dekker and Multi-objective Deep Reinforcement Learning by Hossam Mossalam, Yannis M. Assael, Diederik M. Roijers and Shimon Whiteson.


Multi-agent Reinforcement Learning: A Critical Survey by Yoav Shoham, Rob Powers and Trond Grenager and A Unified Game-theoretic Approach to Multiagent Reinforcement Learning by Marc Lanctot, Vinicius Zambaldi, Audrunas Gruslys, Angeliki Lazaridou, Karl Tuyls, Julien Perolat, David Silver and Thore Graepel.

Deep Reinforcement Learning

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

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.

Developmental Neuroscience

Developmental Biology by Scott Gilbert, Evolutionary Developmental Biology by Brian K. Hall, Developmental Neurobiology edited by Mahendra S. Rao and Marcus Jacobson, Development of the Nervous System by Dan H. Sanes, Thomas A. Reh and William A. Harris, Building Brains: An Introduction to Neural Development by David J. Price, Andrew P. Jarman, John O. Mason and Peter C. Kind, Principles of Neural Development by Dale Purves and Jeff W. Lichtman, Theoretical Models of Neural Development by Geoffrey J. Goodhill and Developmental Cognitive Neuroscience by Mark H. Johnson and Michelle de Haan.

Evolutionary Neurogenetics

Genetic Correlates of the Evolving Primate Brain by Eric J. Vallender, Genetic Basis of Human Brain Evolution by Eric J. Vallender, Nitzan Mekel-Bobrov and Bruce T. Lahn, Neuroscience: Genes and Human Brain Evolution by Daniel H. Geschwind and Genevieve Konopka, Genetic Links between Brain Development and Brain Evolution by Sandra L. Gilbert, William B. Dobyns and Bruce T. Lahn, Genetic Changes Shaping the Human Brain by Byoung-Il Bae, Divya Jayaraman and Christopher A. Walsh and Human-specific Genomic Signatures of Neocortical Expansion by Marta Florio, Victor Borrell and Wieland B. Huttner.

Developmental Neurogenetics

Building Brains: An Introduction to Neural Development by David J. Price, Andrew P. Jarman, John O. Mason and Peter C. Kind, Gene Expression and Cell-cell Interactions in the Developing Nervous System edited by Jean M. Lauder and Phillip G. Nelson and Genetic Links between Brain Development and Brain Evolution by Sandra L. Gilbert, William B. Dobyns and Bruce T. Lahn.

Behavioral Neurogenetics

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.

Computational Genetics

Modeling and Simulation of Genetic Regulatory Systems: A Literature Review by Hidde De Jong, Genetic Network Modeling by Eugene P. van Someren, Lodewyk F. A. Wessels, Eric Backer and Marcel J. T. Reinders, Computational Modeling of Genetic and Biochemical Networks edited by James M. Bower and Hamid Bolouri, Modelling Gene Networks at Different Organisational Levels by Thomas Schlitt and Alvis Brazma, Current Approaches to Gene Regulatory Network Modelling by Thomas Schlitt and Alvis Brazma and Modelling and Analysis of Gene Regulatory Networks by Guy Karlebach and Ron Shamir.

Computational Neurogenetics

Computational Neurogenetics by Nikola K. Kasabov and Lubica Benuskova and Computational Neurogenetic Modeling by Lubica Benuskova and Nikola K. Kasabov.

Evolutionary Computation

Introduction to Evolutionary Computing by Agoston E. Eiben and James E. Smith, Evolutionary Computation: A Unified Approach by Kenneth A. De Jong, Advances in Evolutionary Computing: Theory and Applications edited by Ashish Ghosh and Shigeyoshi Tsutsui, Evolutionary Algorithms for Solving Multi-objective Problems by Carlos A. Coello Coello, Gary B. Lamont and David A. Van Veldhuizen and Evolving Virtual Creatures by Karl Sims.

Computational Neural Development

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.

Computational Neuroethology


The Study of Instinct by Nikolaas Tinbergen, Learning and Instinct in Animals by William H. Thorpe and Instinct and Innate Behavior: Toward an Ethological Psychology by Gordon M. Burghardt.


The Foundations of Ethology by Konrad Lorenz, Ethology: The Mechanisms and Evolution of Behavior by James L. Gould, Animal Motivation and Cognition by Frederick Toates, Animal Cognition by Kristin Andrews and Ljiljana Radenovic, Animal Behavior by Stephen J. Crowley and Colin Allen, Motivation of Human and Animal Behavior: An Ethological View by Konrad Lorenz and Paul Leyhausen, Ethological Concepts and Human Development by Wagner H. Bridger, On Aims and Methods of Cognitive Ethology by Dale Jamieson and Marc Bekoff and Cognitive Ethology: A New Approach for Studying Human Cognition by Alan Kingstone, Daniel Smilek and John D. Eastwood.

Computational Ethology

Toward a Science of Computational Ethology by David J. Anderson and Pietro Perona, Artificial Ethology by Owen Holland and David McFarland, Lessons from Cognitive Ethology: Animal Models for Ethological Computing by Irene M. Pepperberg and Old Tricks, New Dogs: Ethology and Interactive Creatures by Bruce M. Blumberg.


What is Neuroethology? by Jörg-Peter Ewert, The Scope of Neuroethology by Graham Hoyle and Neuroethology and the Philosophy of Cognitive Science by Brian L. Keeley.

Computational Neuroethology

Intelligence as Adaptive Behavior: An Experiment in Computational Neuroethology by Randall D. Beer and Computational Neuroethology: A Provisional Manifesto by David Cliff.