An awesome & curated list for Artificial General Intelligence, an emerging inter-discipline field that combines artificial intelligence and computational cognitive sciences as majority, alone with probability and mathematical statistics, formal logic, cognitive and developmental psychology, computational philosophy, cognitive neuroscience, and computational sociology. We are promoting high-level machine intelligence by getting inspirations from the way that human learns and thinks, while obtaining a deeper understanding of human cognition simultaneously. We believe that this kind of reciprocative research is a potential way towards our big picture: building human-level intelligent agents with capabilities such as abstracting, explaining, learning, planning, and making decisions.

The author of this repo has been struggling to taxonomize related topics, since there are so many perspectives to follow, such as task-oriented, technique-oriented, and metaphysics-oriented. Finally he decided to focus on the perspective of The Sciences of Intelligence---each topic describes a phenomenon of intelligence, or an intelligent behavior---they show the objectives of reverse-engineering human intelligence for computational methods. These topics are never restricted to specific technical methods or tasks, but are trying to organize the nature of intelligence---from both the software perspective and the hardware perspective.

Obviously this reading list is far from covering the every aspect of AGI and CoCoSci. Since the list is a by-product of literature reviews when the author is working on Abduction, other non-Abduction topics are also collected with biases, more or less. Abduction is the way humans explain the world with the known, and discover the unknown, requiring much more investigations into its computational basis, cognitive underpinnings and applications to AI. Please feel free to reach out.

• Academic Tools
  • Courses
  • Programming
  • Paper Writing
  • Paper Reading
  • Resource Organizing
• Papers
  • Abduction
    • Computational Explanation
    • Computational Scientific Discovery
    • Applications in AI
  • Bayesian Modeling
    • Bayesian Induction
    • Generative Model
    • Nonparametric Model
    • Bayesian Optimization
  • Complexity & Information Theory
    • Theory
    • Dimensionality Reduction
    • Visual Complexity
  • Learning with Cognitive Plausibility
  • Communications & Pragmatics
    • Non-Verbal Communication
    • Rational Speech Act
    • Language Compositionality
  • Problem Solving
    • Human-Level Problem Solving
    • Planning
    • Intrinsic Motivation
    • Reinforcement Learning
    • Inverse Reinforcement Learning
  • System 1 & System 2
    • Dual-Coding Theory
    • Neural-Symbolic AI
  • Explainability
    • Trustworthy AI
    • Strong Machine Learning
    • Explainable Deep Learning
  • Embodied Intelligence
  • Evolutionary Intelligence
  • Methodologies for Experiments
    • Quantitative Analysis
    • Scaling Up Behavioral Studies
    • Question Answering
    • Human-Machine Comparison
  • Meta-Level Considerations
    • Meta Learning
    • Gestalt
    • Rationality & Rationalization
    • Cognitive Architecture
  • Theory of Mind
  • Analogy
  • Causality
  • Commonsense
    • Intuitive Physics
    • AI Commonsense Reasoning
    • Commonsense Knowledgebase
  • Inductive Logic & Program Synthesis
  • Knowledge Representation
  • Cognitive Development
  • Learning in the Open World
• Institute & Researcher
  • MIT
  • Stanford
  • Princeton
  • Harvard
  • UCLA
  • UC Berkeley
  • UCSD
  • NYU
  • BIGAI
• People & Book
  • Ulf Grenander
  • Michael Tomasello
  • Judea Pearl
  • Susan Carey
  • Daniel Kahneman
  • Karl Popper
  • John Hopcroft

• Computational Cognitive Science Courses - MIT, Harvard, Stanford. Courses on computational cognitive science from MIT, Harvard, Stanford.

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• Probabilistic Models of Cognition - MIT. The probabilistic approach to cognitive science, which models learning and reasoning as inference in complex probabilistic models.

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• LaTex Configuration - LaTex. LaTex template for configuration file with elegant reference style (gray-colored reference, page backward reference).

• BibTex Template - BibTex. BibTex template for including abbreviations of journals and conferences in AI, Mathematics, and Cognitive Sciences.

• How to construct a Nature summary paragraph - Nature. Nature official guidelines for composing abstracts.

• Scientific Papers - Nature. Nature guidance on writing scientific papers.

• The Machine Learning Reproducibility Checklist - McGill University. Guidelines for introducing a machine learning algorithm with guarantee of reproducibility.

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• How to Read a Paper - ACM SIGCOMM Computer Communication Review, 2007. [All Versions].

• How to (seriously) read a scientific paper - Science, 2016. [All Versions]. Science interview on reading scientific papers.

• It's not just you: science papers are getting harder to read - Nature, 2017. [All Versions].

• How to navigate a scientific paper with time constraints: a graphics approach - MIT. MIT guidance on strategies for reading papers given different time constraints.

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• Knowledge organization - Wikipedia.

• How to keep up with the scientific literature - Science, 2016. Science interview on organizing scientific papers.

• Scientific literature: Information overload - Nature, 2016. [All Versions]. Perspective on handling overloaded information from scientific literature.

• Microsoft Academic Graph - Microsoft Research. Heterogeneous graph containing scientific publication records, citation relationships between those publications, as well as authors, institutions, journals, conferences, and fields of study.

• An Overview of Microsoft Academic Service (MAS) and Applications - WWW'15, 2015. [All Versios]. Original paper on Microsoft Academic Graph.

• Semantic Scholar - Allen Institute for AI Research. AI-powered scientific literature research tool.

• Litmaps - Litmap Ltd.. For interactive literature map construction and linked document management.

• VOSviewer - Leiden University. For constructing and visualizing bibliometric networks.

• StateOfTheArt.AI. For tracking, collecting and visualizing the development of AI research.

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• Abduction - Plato Stanford.

• Scientific Explanation - Plato Stanford.

• Non-monotonic Logic - Plato Stanford.

• Philosophical Writings of Peirce - Courier Corporation, 1955. [All Versions].

• The Inference to the Best Explanation - Philosophical Review, 1965. [All Versions].

• Inference to the Best Explanation - Routledge, 1991. [All Versions].

• A Study of Thinking - Routledge, 1956. [All Versions].

• Abductive Reasoning and Learning - Springer, 2000. [All Versions].

• Abductive Reasoning: Logical Investigations into Discovery and Explanation - Springer, 2006. [All Versions].

• Abductive Cognition: The Epistemological and Eco-Cognitive Dimensions of Hypothetical Reasoning - Springer, 2009. [All Versions].

• Explanation and Abductive Inference - The Oxford Handbook of Thinking and Reasoning, 2012. [All Versions].

• Probabilistic models of cognition: Conceptual foundations - Trends in Cognitive Sciences, 2006. [All Versions].

• The structure and function of explanations - Trends in Cognitive Sciences, 2006. [All Versions].

• Explanatory Preferences Shape Learning and Inference - Trends in Cognitive Sciences, 2016. [All Versions].

• The Role of Explanatory Considerations in Updating - Cognition, 2015. [All Versions].

• Explanation, updating, and accuracy - Journal of Cognitive Psychology, 2016. [All Versions].

• Best, second-best, and good-enough explanations: How they matter to reasoning - Journal of Experimental Psychology, 2018. [All Versions].

• How explanation guides belief change - Trends in Cognitive Sciences, 2021. [All Versions].

• Use of current explanations in multicausal abductive reasoning - Cognitive Science, 2001. [All Versions].

• Kinematic mental simulations in abduction and deduction - Proceedings of National Academy of Sciences, 2013. [All Versions].

• Patterns of abduction - Synthese, 2007. [All Versions].

• Abduction: A categorical characterization - Journal of Applied Logic, 2015. [All Versions].

• Defending Abduction - Philosophy of Science, 1999. [All Versions].

• On the distinction between Peirce's abduction and Lipton's Inference to the best explanation - Synthese, 2011. [All Versions].

• Abduction − the context of discovery + underdetermination = inference to the best explanation - Synthese, 2019. [All Versions].

• Towards an Architecture for Cognitive Vision Using Qualitative Spatio-temporal Representations and Abduction - Spatial Cognition, 2002. [All Versions].

• Abductive inference within a pragmatic framework - Synthese, 2018. [All Versions].

• Disjunctive Abduction - New Generation Computing, 2019. [All Versions].

• Probabilistic alternatives to Bayesianism: the case of explanationism - Frontiers in Psychology, 2015. [All Versions].

• A Probabilistic Theory of Abductive Reasoning - ICAART, 2021. [All Versions].

• The order effect in human abductive reasoning: an empirical and computational study - Journal of Experimental & Theoretical Artificial Intelligence, 2006. [All Versions].

• Abduction, Induction, and Analogy - Model-Based Reasoning in Science and Technology, 2010. [All Versions].

• Remembrance of inferences past: Amortization in human hypothesis generation - Cognition, 2018. [All Versions].

• The AHA! Experience: Creativity Through Emergent Binding in Neural Networks - Cognitive Science, 2012. [All Versions].

• Explanation-seeking curiosity in childhood - Current Opinion in Behavioral Sciences, 2020. [All Versions].

• Why Imaginary Worlds? The psychological foundations and cultural evolution of fictions with imaginary worlds - Behavioral and Brain Sciences, 2021. [All Versions].

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• Scientific Discovery - Plato Stanford.

• Models of Discovery: And Other Topics in the Methods of Science - Springer, 1977. [All Versions].

• Scientific discovery: Computational explorations of the creative processes - MIT Press, 1987. [All Versions].

• Induction: Processes of Inference, Learning, and Discovery - MIT Press, 1989. [All Versions].

• Exploring science: The cognition and development of discovery processes - MIT Press, 2000. [All Versions].

• Dual Space Search During Scientific Reasoning - Cognitive Science, 1988. [All Versions].

• Complexity Management in a Discovery Task - CogSci'92, 1992. [All Versions].

• A dual-space model of iteratively deepening exploratory learning - International Journal of Human-Computer Studies, 1996. [All Versions].

• Heuristics for Scientific Experimentation: A Developmental Study - Cognitive Psychology, 1993. [All Versions].

• A 4-Space Model of Scientific Discovery - CogSci'95, 1995. [All Versions].

• When to trust the data: Further investigations of system error in a scientific reasoning task - Memory & Cognition, 1996. [All Versions].

• Confirmation, disconfirmation, and information in hypothesis testing - Psychological Review, 1987. [All Versions].

• Hypothesis generation, sparse categories, and the positive test strategy - Psychological Review, 2011. [All Versions].

• Children and adults as intuitive scientists - Psychological Review, 1989. [All Versions].

• Abduction and styles of scientifc thinking - Synthese, 2021. [All Versions].

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• Functional genomic hypothesis generation and experimentation by a robot scientist - Nature, 2004. [All Versions].

• Highly accurate protein structure prediction with AlphaFold - Nature, 2021. [All Versions].

• Interpretation as abduction - Artificial Intelligence, 1993. [All Versions].

• Probabilistic Horn abduction and Bayesian networks - Artificial Intelligence, 1993. [All Versions].

• Abductive Inference in Bayesian Networks: A Review - Advances in Bayesian Networks, 2004. [All Versions].

• Abductive Logic Programming - Journal of Logic Computation, 1992. [All Versions].

• ACLP: Abductive Constraint Logic Programming - The Journal of Logic Programming, 1999. [All Versions].

• Abduction in Logic Programming - Computational Logic, 2002. [All Versions].

• Bayesian Abductive Logic Programs: A Probabilistic Logic for Abductive Reasoning - IJCAI'11, 2011. [All Versions].

• Abductive Plan Recognition by Extending Bayesian Logic Programs - ECML'11, 2011. [All Versions].

• An Approach to Abductive Reasoning in Equational Logic - IJCAI'13, 2013. [All Versions].

• Abduction-Based Explanations for Machine Learning Models - AAAI'19, 2019. [All Versions].

• Probabilistic Sufficient Explanations - IJCAI'21, 2021. [All Versions].

• Machine Translation Using Abductive Inference - COLING, 1990. [All Versions].

• Anomaly detection through explanations - Ph.D Dissertation MIT, 2018. [All Versions].

• Discovering a symbolic planning language from continuous experience - CogSci'19, 2019. [All Versions].

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• Bayesian Epistemology - Plato Stanford.

• Probabilistic machine learning and artificial intelligence - Nature, 2015. [All Versions].

• Generalization, similarity, and Bayesian inference - Behavioral and Brain Sciences, 2001. [All Versions].

• Bayesian modeling of human concept learning - NIPS'99, 1999. [All Versions].

• Rules and Similarity in Concept Learning - NIPS'99, 1999. [All Versions].

• Theory-based Bayesian models of inductive learning and reasoning - Trends in Cognitive Sciences, 2006. [All Versions].

• Word learning as Bayesian inference - Psychological Review, 2007. [All Versions]. [APA].

• How to Grow a Mind: Statistics, Structure, and Abstraction - Science, 2011. [All Versions].

• Human-level concept learning through probabilistic program induction. - Science, 2015. [All Versions]. [Supplementary Material].

• Building Machines That Learn and Think Like People - Behavioral and Brain Sciences, 2017. [All Versions].

• The rational basis of representativeness - CogSci'01, 2001. [All Versions].

• Testing a Bayesian Measure of Representativeness Using a Large Image Database - NIPS'11, 2011. [All Versions].

• Constructing a hypothesis space from the Web for large-scale Bayesian word learning - CogSci'12, 2012. [All Versions].

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• Filters, random fields and maximum entropy (FRAME): Towards a unified theory for texture modeling - International Journal of Computer Vision, 1998. [All Versions].

• Object Perception as Bayesian Inference - Annual Review of Psychology, 2004. [All Versions].

• A tale of three probabilistic families: Discriminative, descriptive, and generative models - Quarterly of Applied Mathematics, 2018. [All Versions].

• From information scaling of natural images to regimes of statistical models - Quarterly of Applied Mathematics, 2008. [All Versions].

• A Theory of Generative ConvNet - ICML'16, 2016. [All Versions].

• Cooperative Training of Descriptor and Generator Networks - IEEE Transactions on Pattern Analysis and Machine Intelligence, 2018. [All Versions].

• Learning Latent Space Energy-Based Prior Model - NIPS'20, 2020. [All Versions]. [Project]. [Code].

• Learning Energy-Based Models by Diffusion Recovery Likelihood - ICLR'21, 2021. [All Versions]. [Code].

• Score-Based Generative Modeling through Stochastic Differential Equations - ICLR'21, 2021. [All Versions].

• Latent Space Factorisation and Manipulation via Matrix Subspace Projection - ICML'20, 2020. [All Versions].

• Minimax entropy principle and its application to texture modeling - Neural Computing, 1997. [All Versions].

• Parameter Expansion for Data Augmentation - Journal of the American Statistical Association, 1999. [All Versions].

• Image segmentation by data-driven markov chain monte carlo - IEEE Transactions on Pattern Analysis and Machine Intelligence, 2002. [All Versions].

• Efficient Learning of Sparse Representations with an Energy-Based Model - NIPS'06, 2006. [All Versions].

• A Tutorial on Energy-Based Learning - Predicting Structured Data, MIT Press, 2006. [All Versiosn].

• Unsupervised Representaton Learning with Deep Convolutional Generative Adversarial Networks - ICLR'16, 2016. [All Versions].

• Analysis of Langevin Monte Carlo via Convex Optimization - Journal of Machine Learning Research, 2019. [All Versions].

• A generative vision model that trains with high data efficiency and breaks text-based CAPTCHAs - Science, 2017. [All Versions].

• Where do hypotheses come from? - Cognitive Psychology, 2017. [All Versions].

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• A Bayesian Analysis of Some Non-parametric Problems - The Annals of Statistics, 1973. [All Versions].

• Mixtures of Dirichlet Process with Applications to Bayesian Nonparametric Problems - The Annals of Statistics, 1974. [All Versions].

• Latent Semantic Indexing: A Probabilistic Analysis - Journal of Computer and System Sciences, 2000. [All Versions].

• Nonparametric Bayesian Data Analysis - Statistical Science, 2004. [All Versions].

• Hierarchical topic models and the nested Chinese restaurant process - NIPS'03, 2003. [All Versions].

• Learning Systems of Concepts with an Infinite Relational Model - AAAI'06, 2006. [All Versions].

• The nested chinese restaurant process and bayesian nonparametric inference of topic hierarchies - Journal of the ACM, 2010. [All Versions].

• Infinite Latent Feature Models and the Indian Buffet Process - Gatsby Computational Neuroscience Unit Technical Report 2005-001, 2005. [All Versions].

• The Indian Buffet Process: An Introduction and Review - Journal of Machine Learning Research, 2011. [All Versions].

• Nonparametric Bayesian Logic - UAI'05, 2005. [All Versions].

• Infinite Hidden Relational Models - UAI'06, 2006. [All Versions].

• Statistical Predicate Invention - ICML'07, 2007. [All Versions].

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• A Tutorial on Bayesian Optimization - 2018. [All Versions].

• Practical Bayesian Optimization of Machine Learning Algorithms - NIPS'12, 2012. [All Versions].

• Taking the Human Out of the Loop: A Review of Bayesian Optimization - Proceedings of the IEEE, 2015. [All Versions].

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• A Mathematical Theory of Communication - The Bell System Technical Journal, 1948. [All Versions]. Shannon's original paper on Information Theory.

• An introduction to Kolmogorov complexity and its applications - Springer, 2008. [All Versions].

• Complexity and the representation of patterned sequences of symbols - Psychological Review, 1972. [All Versions].

• Visual Pattern Discrimination - IRE Transactions on Information Theory, 1962. [All Versions].

• Algorithmic Information Theory - IBM Journal of Research and Development, 1977. [All Versions].

• From Algorithmic to Subjective Randomness - NIPS'03, 2003. [All Versions].

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• A global geometric framework for nonlinear dimensionality reduction - Science, 2000. [All Versions].

• Reducing the dimensionality of data with neural networks - Science, 2006. [All Versions].

• Representation Learning: A Review and New Perspectives - IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013. [All Versions].

• Representation Learning: A Statistical Perspective - Annual Review of Statistics and Its Application, 2020. [All Versions].

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• Visual complexity: a review - Psychological Bulletin, 2006. [All Versions]. [APA].

• Image complexity and spatial information - International Workshop on Quality of Multimedia Experience, 2013. [All Versions].

• Seeing and speaking: How verbal “description length” encodes visual complexity - Journal of Experimental Psychology, 2022. [All Versions]. [APA].

• How variability shapes learning and generalization - Trends in Cognitive Sciences, 2022. [All Versions].

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• Accuracy and Precision - Wikipedia.

• Cognitive Science: Definition, Status, and Questions - Annual Review of Psychology, 1989. [All Versions].

• Recognition-by-Components: A Theory of Human Image Understanding - Psychological Review, 1987. [All Versions].

• Machine Behaviour - Nature, 2019. [All Versions].

• Dark, Beyond Deep: A Paradigm Shift to Cognitive AI with Humanlike Common Sense - Engineering, 2020. [All Versions].

• Self-supervised Learning Through the eyes of a Child - NIPS'20, 2020. [All Versions].

• CLEVRER: CoLlision Events for Video REpresentation and Reasoning - ICLR'20, 2020. [All Versions].

• BONGARD-LOGO: A New Benchmark for Human-Level Concept Learning and Reasoning - NIPS'20, 2020. [All Versions].

• The relationship between Precision-Recall and ROC curves - ICML'06, 2006. [All Versions].

• Distributional Generalization: A New Kind of Generalization - 2020. [All Versions].

• Learning and development in networks: The importance of starting small. - Cognition, 1993. [All Versions].

• Language acquisition in the absence of explicit negative evidence: how important is starting small? - Cognition, 1999. [All Versions].

• Curriculum Learning - ICML'09, 2009. [All Versions].

• Parsing video events with goal inference and intent prediction - ICCV'11, 2011. [All Versions].

• Inferring "Dark Matter" and "Dark Energy" from Videos - ICCV'13, 2013. [All Versions].

• Explainable and Explicit Visual Reasoning over Scene Graphs - CVPR'19, 2019. [All Versions].

• Attention over Learned Object Embeddings Enables Complex Visual Reasoning - NIPS'21, 2021. [All Versions].

• Distributed Representations of Words and Phrases and their Compositionality - NIPS'13, 2013. [All Versions].

• Motion Reasoning for Goal-Based Imitation Learning - ICRA'20, 2020. [All Versions].

• Action Genome: Actions as Compositions of Spatio-temporal Scene Graphs - CVPR'20, 2020. [All Versions].

• Refactoring Policy for Compositional Generalizability using Self-Supervised Object Proposals - NIPS'20, 2020. [All Versions].

• Something-Else: Compositional Action Recognition with Spatial-Temporal Interaction Networks - CVPR'20, 2020. [All Versions].

• Putting visual object recognition in context - CVPR'20, 2020. [All Versions].

• Multimodal Few-Shot Learning with Frozen Language Models - 2021. [All Versions].

• Describing Objects by their Attributes - CVPR'09, 2009. [All Versions].

• Panoramic Learning with A Standardized Machine Learning Formalism - 2021. [All Versions].

• Graininess of judgment under uncertainty: An accuracy-informativeness trade-off - Journal of Experimental Psychology, 1995. [All Versions].

• Federated Learning via Posterior Averaging: A New Perspective and Practical Algorithms - ICLR'20, 2020. [All Versions].

• Interplay between rule learning and rule switching in a perceptual categorization task - 2022. [All Versions].

• Deep Learning and the Information Bottleneck Principle - IEEE Information Theory Workshop'15, 2015. [All Versions].

• On the information bottleneck theory of deep learning - Journal of Statistical Mechanics: Theory and Experiment, 2019. [All Versions].

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• The Interactive Evolution of Human Communication Systems - Cognitive Science, 2010. [All Versions].

• Iconicity: From sign to system in human communication and language - Pragmatics & Cognition, 2014. [All Versions].

• The Picture Exchange Communication System - Behavior Modification, 1994. [All Versions].

• Graphical Language Games: Interactional Constraints on Representational Form - Cognitive Science, 2007. [All Versions].

• A multimodal discourse theory of visual narrative - Journal of Pragmatics, 2014. [All Versions].

• Pixelor: A Competitive Sketching AI Agent. So you think you can beat me? - ACM SIG Graph, 2020. [All Versions]. [Project].

• Pragmatic Inference and Visual Abstraction Enable Contextual Flexibility During Visual Communication - Computational Brain & Behavior, 2020. [All Versions].

• Emergent Graphical Conventions in a Visual Communication Game - 2021. [All Versions].

• Communicating artificial neural networks develop efficient color-naming systems - Proceedings of National Academy of Sciences, 2021. [All Versions].

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• Pragmatics - Plato Stanford.

• Predicting Pragmatic Reasoning in Language Games - Science, 2012. [All Versions].

• Pragmatic Language Interpretation as Probabilistic Inference - Trends in Cognitive Sciences, 2016. [All Versions].

• Pragmatic Reasoning through Semantic Inference - Semantics & Pragmatics, 2016. [All Versions].

• Processing gradable adjectives in context: A visual world study - Semantics and Linguistic Theory, 2016. [All Versions].

• Colors in Context: A Pragmatic Neural Model for Grounded Language Understanding - Transactions of the Association for Computational Linguistics, 2017. [All Versions].

• Social Pragmatics: Preschoolers Rely on Commonsense Psychology to Resolve Referential Underspecification - Child Development, 2019. [All Versions].

• Pragmatically Informative Image Captioning with Character-Level Inference - NAACL'18, 2018. [All Versions].

• Pragmatic Issue-Sensitive Image Captioning - ACL Findings: EMNLP'20, 2020. [All Versions].

• Disentangling contributions of visual information and interaction history in the formation of graphical conventions - CogSci'19, 2019. [All Versions].

• How young children integrate information sources to infer the meaning of words - Nature, 2021. [All Versions].

• Information Structure in Discourse: Towards an Integrated Formal Theory of Pragmatics - Semantics and Pragmatics, 1998. [All Versions].

• When Lingens meets Frege: communication without common ground - Philosophical Studies, 2021.

• Language as shaped by the environment: linguistic construal in a collaborative spatial task - Nature Humanities and Social Sciences Communications, 2020. [All Versions].

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• Compositionality - Plato Stanford.

• The Principle of Semantic Compositionality - Topoi, 1994. [All Versions].

• On The Emergence Of Compositionality - Proceedings of the Evolution of Language Conference'06, 2006. [All Versions].

• Multi-Agent Cooperation and the Emergence of (Natural) Language - ICLR'17, 2017. [All Versions].

• Emergence of Language with Multi-agent Games: Learning to Communicate with Sequences of Symbols - NIPS'18, 2018. [All Versions].

• Emergent communication through negotiation - ICLR'18, 2018. [All Versions].

• The language of generalization - Psychological Review, 2019. [All Versions].

• Compositionality and Generalization in Emergent Languages - ACL'20, 2020. [All Versions].

• Word formation supports efficient communication: The case of compounds - CogSci'22, 2022.

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• Elements of a theory of human problem solving - Psychological Review, 1958. [All Versions].

• Human Problem Solving - Englewood Cliffs, NJ: Prentice-hall, 1972. [All Versions].

• Learning to Solve Problems: A Handbook for Designing Problem-Solving Learning Environments - Taylorfrancis, 2010. [All Versions].

• Judgment under Uncertainty: Heuristics and Biases: Biases in judgments reveal some heuristics of thinking under uncertainty - Science, 1974. [All Versions].

• Computational evidence for hierarchically structured reinforcement learning in humans - Proceedings of National Academy of Sciences, 2020. [All Versions].

• Human Performance on Insight Problem Solving: A Review - The Journal of Problem Solving, 2011. [All Versions].

• Learning to perceive and act by trial and error - Machine Learning, 1991. [All Versions].

• Representations in distributed cognitive tasks - Cognitive Science, 1994. [All Versions].

• The nature of external representations in problem solving - Cognitive Science, 1997. [All Versions].

• Rapid trail-and-error learning with simulation supports flexible tool use and physical reasoning. - Proceedings of National Academy of Sciences, 2020. [All Versions]. [Project]. [Appendix].

• Abstract strategy learning underlies flexible transfer in physical problem solving - CogSci'20, 2020. [All Versions].

• Physion: Evaluating Physical Prediction from Vision in Humans and Machines - NIPS'21, 2021. [All Versions].

• Exploration: from machines to humans - Current Opinion in Behavioral Sciences, 2020. [All Versions].

• Balancing exploration and exploitation with information and randomization - Current Opinion in Behavioral Sciences, 2021. [All Versions].

• Hippocampal neurons construct a map of an abstract value space - Cell, 2021. [All Versions].

• Insightful problem solving and creative tool modification by captive nontool-using rooks - Proceedings of National Academy of Sciences, 2009. [All Versions]. [Supplementary Material].

• Intrinsic Exploration as Empowerment in a Richly Structured Online Game - 2022. [All Versions].

• Multi-task reinforcement learning in humans - Nature Human Behavior, 2021. [All Versions].

• Learning to act by integrating mental simulations and physical experiments - CogSci'18, 2018. [All Versions]. [Code].

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• From Skills to Symbols: Learning Symbolic Representations for Abstract High-Level Planning - Journal of Artificial Intelligence Research, 2018. [All Versions].

• Differentiable Physics and Stable Modes for Tool-Use and Manipulation Planning - Robotics: Science and Systems, 2018. [All Versions].

• On Monte Carlo Tree Search and Reinforcement Learning - Journal of Artificial Intelligence Research, 2017. [All Versions].

• Integrated Task and Motion Planning - Annual Review of Control, Robotics, and Autonomous Systems, 2021. [All Versions].

• Learning to act by integrating mental simulations and physical experiments - CogSci'21, 2018. [All Versions].

• What Is the Model in Model-Based Planning? - Cognitive Science, 2021. [All Versions].

• Discovering State and Action Abstractions for Generalized Task and Motion Planning - AAAI'22, 2022. [All Versions].

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• Intrinsically Motivated Reinforcement Learning - NIPS'04, 2004. [All Versions].

• What is intrinsic motivation? A typology of computational approaches - Frontiers in Neurorobotics, 2009. [All Versions].

• Curiosity-driven Exploration by Self-supervised Prediction - ICML'17, 2017. [All Versions].

• UCB Exploration via Q-Ensembles - 2017. [All Versions].

• Adapting Behavior via Intrinsic Reward: A Survey and Empirical Study - Journal of Artificial Intelligence Research, 2020. [All Versions].

• Causal Curiosity: RL Agents Discovering Self-supervised Experiments for Causal Representation Learning - ICML'21, 2021. [All Versions].

• Variational Information Maximisation for Intrinsically Motivated Reinforcement Learning - NIPS'15, 2015. [All Versions].

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• Reinforcement learning: An introduction - MIT Press, 2018. [All Versions].

• Reinforcement learning: A survey - Journal of Artificial Intelligence Research, 1996. [All Versions].

• Between MDPs and semi-MDPs: A framework for temporal abstraction in reinforcement learning - Artificial Intelligence, 1999. [All Versions].

• Reinforcement Learning and Control as Probabilistic Inference: Tutorial and Review - 2018. [All Versions]. [Slides].

• A Generalized Algorithm for Multi-Objective Reinforcement Learning and Policy Adaptation - NIPS'19, 2019. [All Versions].

• Solving Compositional Reinforcement Learning Problems via Task Reduction - ICLR'21, 2021. [All Versions].

• Neural Task Programming: Learning to Generalize Across Hierarchical Tasks - ICRA'18, 2018. [All Versions].

• Learning to act: qualitative learning of deterministic action models - Journal of Logic and Computation, 2017. [All Versions].

• Learning to Act and Observe in Partially Observable Domains - 2021. [All Versions].

• Why Generalization in RL is Difficult: Epistemic POMDPs and Implicit Partial Observability - NIPS'21, 2021. [All Versions].

• Learning to Perform Physics Experiments via Deep Reinforcement Learning - ICLR'17, 2017. [All Versions].

• Data-Efficient Learning for Complex and Real-Time Physical Problem Solving Using Augmented Simulation - Robotics and Automation Letters, 2021. [All Versions].

• A Survey of Preference-Based Reinforcement Learning Methods - Journal of Machine Learning Research, 2017. [All Versions].

• On the Expressivity of Markov Reward - NIPS'21, 2021. [All Versions].

• Constrained Policy Optimization - ICML'17, 2017. [All Versions].

• SUNRISE: A Simple Unified Framework for Ensemble Learning in Deep Reinforcement Learning - ICML'21, 2021. [All Versions].

• The Quest for a Common Model of the Intelligent Decision Maker - Multi-disciplinary Conference on Reinforcement Learning and Decision Making'22, 2022. [All Versions].

• Automatic curriculum learning for deep RL: a short survey - IJCAI'20, 2020. [All Versions].

• TeachMyAgent: a Benchmark for Automatic Curriculum Learning in Deep RL - ICML'21, 2021. [All Versions]. [Project].

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• Apprenticeship Learning via Inverse Reinforcement Learning - ICML'04, 2004. [All Versions].

• Bayesian Inverse Reinforcement Learning - IJCAI'07, 2007. [All Versions].

• From Language to Goals: Inverse Reinforcement Learning for Vision-Based Instruction Following - ICLR'19, 2019. [All Versions].

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• Cognitive development: An information processing approach - B.Blackwell, 1991. [All Versions].

• Reconstructing constructivism: Causal models, Bayesian learning mechanisms, and the theory theory - Psychological Bulletin, 2012. [All Versions].

• Towards a rational constructivist theory of cognitive development - Psychological Review, 2019. [All Versions].

• From exploration to play: A cross-sectional study of infant free play behavior - Developmental Psychology, 1981. [All Versions].

• Detecting Blickets: How Young Children Use Information about Novel Causal Powers in Categorization and Induction - Children Development, 2003. [All Versions].

• Serious fun: Preschoolers engage in more exploratory play when evidence is confounded - Developmental Psychology, 2007. [All Versions].

• Observing the unexpected enhances infants' learning and exploration - Science, 2015. [All Versions].

• Word, thought, and deed: the role of object categories in children's inductive inferences and exploratory play - Developmental Psychology, 2009. [All Versions].

• Where science starts: Spontaneous experiments in preschoolers' exploratory play - Cognition, 2011. [All Versions].

• Scientific thinking in young children: Theoretical advances, empirical research, and policy implications - Science, 2012. [All Versions].

• The origins of inquiry: inductive inference and exploration in early childhood - Trends in Cognitive Sciences, 2012. [All Versions].

• Finding New Facts; Thinking New Thoughts - Advances in Child Development and Behavior, 2012. [All Versions].

• Theory learning as stochastic search in the language of thought - Cognitive Development, 2012. [All Versions].

• Infants make more attempts to achieve a goal when they see adults persist - Science, 2017. [All Versions].

• Knowing when to quit: Children consider access to solutions when deciding whether to persist - CogSci'20, 2020. [All Versions].

• Bayesian Models of Conceptual Development: Learning as Building Models of the World - Annual Review of Developmental Psychology, 2020. [All Versions].

• Sticking to the Evidence? A Behavioral and Computational Case Study of Micro-Theory Change in the Domain of Magnetism - Cognitive Science, 2019. [All Versions].

• Cognitive pragmatism: Children flexibly choose between facts and conjectures - CogSci'18, 2018. [All Versions].

• Exploratory play, rational action, and efficient search - CogSci'20, 2020. [All Versions].

• Children selectively endorse speculative conjectures - Child Development, 2021. [All Versions].

• Play, Curiosity, and Cognition - Annual Review of Developmental Psychology, 2020. [All Versions].

• Learning higher-order generalizations through free play: Evidence from 2- and 3-year-old children - Developmental Psychology, 2017. [All Versions].

• The Child as Hacker - Trends in Cognitive Sciences, 2020. [All Versions].

• Childhood as a solution to explore–exploit tensions - Philosophical Transactions of the Royal Society B: Biological Sciences, 2020. [All Versions].

• Children's exploratory play tracks the discriminability of hypotheses - Nature Communications, 2021. [All Versions].

• A Developmental Perspective on Executive Function - Child Development, 2010. [All Versions].

• Rethinking Executive Function and Its Development - Psychological Science, 2020. [All Versions].

• Perception of partly occluded objects in infancy - Cognitive Psychology, 1983. [All Versions].

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• Online learning of symbolic concepts - Journal of Mathematical Psychology, 2017. [All Versions].

• Zero-Shot Learning—A Comprehensive Evaluation of the Good, the Bad and the Ugly - IEEE Transactions on Pattern Analysis and Machine Intelligence, 2018. [All Versions].

• Generalizing from a few examples: A survey on few-shot learning - ACM Computing Survey. [All Versions].

• Towards Open World Recognition - CVPR'15, 2015. [All Versions].

• Towards Open Set Deep Networks - CVPR'16, 2016. [All Versions].

• In the Wild: From ML Models to Pragmatic ML Systems - ICLR'20, 2020. [All Versions].

• Adversarial Filters of Dataset Biases - ICML'20, 2020. [All Versions].

• A Wholistic View of Continual Learning with Deep Neural Networks: Forgotten Lessons and the Bridge to Active and Open World Learning - 2020. [All Versions].

• Energy-Based Models for Continual Learning - NIPS'20, 2020. [All Versions]. [Project].

• Learning to Learn Image Classifiers with Visual Analogy - CVPR'18, 2018. [All Versions].

• Zero-Shot Object Detection - ECCV'18, 2018. [All Versions].

• Towards Open World Object Detection - CVPR'21, 2021. [All Versions]. [Project].

• Learning to Recognise Unseen Classes by A Few Similes - MM'17, 2017. [All Versions].

• Ontology-guided Semantic Composition for Zero-Shot Learning - KR'20, 2020. [All Versions].

• OntoZSL: Ontology-enhanced Zero-shot Learning - WWW'21, 2021. [All Versions].

• Knowledge-aware Zero-Shot Learning: Survey and Perspective - IJCAI'21 2021. [All Versions].

• From Red Wine to Red Tomato: Composition with Context - CVPR'17, 2017. [All Versions].

• Attributes as Operators: Factorizing Unseen Attribute-Object Compositions - ECCV'18, 2018. [All Versions].

• Learning Compositional Representations for Few-Shot Recognition - CVPR'19, 2019. [All Versions].

• Symmetry and Group in Attribute-Object Compositions - CVPR'20, 2020. [All Versions].

• A causal view of compositional zero-shot recognition - NIPS'20, 2020. [All Versions].

• Compositional Few-Shot Recognition with Primitive Discovery and Enhancing - MM'20, 2020. [All Versions].

• Learning Unseen Concepts via Hierarchical Decomposition and Composition - CVPR'20, 2020. [All Versions].

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• Center for Brains, Minds and Machines (CBMM) - NSF.

• Josh Tenenbaum - MIT, Computational Cognitive Science Group (CoCoSci Group) - Department of Brain and Cognitive Sciences, CSAIL, MIT.

• Rebecca Saxe - MIT, Social Cognitive Neuroscience Laboratory (SaxeLab) - Department of Brain and Cognitive Sciences, MIT.

• Laura Schulz - MIT, Early Childhood Cognition Lab - Department of Brain and Cognitive Sciences, MIT.

• Leslie Kaelbling - MIT, The Learning & Intelligent Systems Group - Department of Electrical Engineering and Computer Science, CSAIL, MIT.

• Armando Solar-Lezama - MIT, Computer-Aided Programming Group - Department of Electrical Engineering and Computer Science, CSAIL, MIT.

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• Noah Goodman - Stanford, Computation & Cognition Lab (CoCoLab) - Department of Psychology, Department of Computer Science, Stanford.

• Michael Frank - Stanford, The Stanford Language and Cognition Lab - Department of Psychology, Stanford.

• Tobias Gerstenberg - Stanford, Causality in Cognition Lab (CICL) - Department of Psychology, Stanford.

• Chelsea Finn - Stanford, Intelligence through Robotic Interaction at Scale (IRIS Group) - Department of Computer Science, Stanford.

• Jiajun Wu - Department of Computer Science, Stanford.

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• Tania Lombrozo - Princeton, Concepts & Cognition Lab - Department of Psychology, Princeton.

• Tom Griffiths - Princeton, Computational Cognitive Science Lab - Department of Psychology, Department of Computer Science, Princeton.

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• Elizabeth Spelke - Harvard, Harvard Laboratory for Developmental Studies - Department of Psychology, Harvard.

• Tomer Ullman - Harvard, Computation, Cognition, and Development Lab (CoCoDev) - Department of Psychology, Harvard.

• Samuel Gershman - Harvard, Computational Cognitive Neuroscience Lab (CCN Lab) - Department of Psychology, Harvard.

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• Center for Vision, Cognition, Learning and Autonomy (VCLA) - Department of Statistics, UCLA.

• Ying Nian Wu - Department of Statistics, UCLA.

• Tao Gao - UCLA, Visual Intelligence Lab - Department of Statistics, UCLA.

• Hongjing Lu - UCLA, Computational Vision and Learning Lab (CVL) - Department of Psychology, UCLA.

• Guy Van den Broeck - UCLA, StarAI Lab - Department of Computer Science, UCLA.

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• Anca Dragan - EECS, Berkeley, Interactive Autonomy and Collaborative Technologies Laboratory (InterACT) - Berkeley.

• Fei Xu - UCB, Berkeley Early Learning Lab (Xu Lab) - Department of Psychology, UCB.

• Alison Gopnik - UCB, Cognitive Development & Learning Lab (Gopnik Lab) - Department of Psychology, UCB.

• Steve Piantadosi - UCB, The computation and language lab (colala) - Department of Psychology, UCB.

• Celeste Kidd - UCB, Kidd Lab - Department of Psychology, UCB.

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• Judith Fan - UCSD, Cognitive Tools Lab - UCSD.

• Zhuowen Tu - UCSD, Machine Learning, Perception, and Cognition Lab (mlPC) - UCSD.

• Ed Vul - UCSD, Computational Cognition Lab - UCSD.

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• Ernest Davis - Department of Computer Science, Courant Institute of Mathematical Sciences, NYU.

• Gary Marcus - Department of Psychology, NYU.

• Brenden Lake - NYU, Human & Machine Learning Lab (Lake Lab) - NYU.

• Todd Gureckis - NYU, Computation & Cognition Lab - NYU.

• Wei Ji Ma - NYU, Wei Ji Ma Lab - NYU.

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• Beijing Institute for General Artificial Intelligence (BIGAI) - Beijing.

• Yixin Zhu - BIGAI & PKU.

• Song-Chun Zhu - BIGAI & PKU.

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Applied mathematician, the founder of General Pattern Theory.

• A Calculus of Ideas: A Mathematical Study of Thinking - World Scientific Publishing Company, 2012. [All Versions].

• General Pattern Theory: A Mathematical Study of Regular Structures - Oxford University Press, 1993. [All Versions].

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Cognitive scientist, set up the foundations of studying human communications.

• Origins of human communication - MIT Press, 2010. [All Versions].

• The cultural origins of human cognition - Havard University Press, 2000. [All Versions].

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Applied mathematician, proposed causal intervention on siamese bayesian networks.

• The Book of Why: The New Science of Cause and Effect - Basic Books, 2018. [All Versions].

• Causality: Models, Reasoning and Inference - Cambridge University Press, 2009. [All Versions].

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Developmental psychologist, proposed object as a core knowledge of human intelligence.

• The Origin of Concepts - Oxford University Press, 2009. [All Versions].

• Conceptual Change in Childhood - MIT Press, 1985. [All Versions].

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Computational cognitive scientist and Economist, set up the foundations for Decision Theory.

• Thinking, fast and slow - Farrar Straus Giroux, 2011. [All Versions].

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Scientific philosophor, the founder of scientific verification theories.

• The logic of scientific discovery - Routledge, 2005. [All Versions].

• All Life is Problem Solving - Routledge, 2001. [All Versions].

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Applied Mathematician, theoretical computer scientist.

• Foundations of Data Science - Cambridge University Press. [All Versions].

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