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Awesome Artificial General Intelligence and Computational Cognitive Sciences Awesome

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 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 systems with capabilities such as abstracting, explaining, learning, planning, and making decisions. And such intelligence may generally help people improve scientific research, engineering, and the arts, which are the hallmarks of human intelligence.

Awesome AGI & CoCoSci is an all-in-one collection, consisting of recources from basic courses and tutorials, to papers and books around diverse topics in mutiple perspectives. Both junior and senior researchers, whether learning, working on, or working around AGI and CoCoSci, meet their interest here.

Contributing

Contributions are greatly welcomed! Please refer to Contribution Guidelines before taking any action.

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Contents

Papers

Abduction

Explanation

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Scientific Discovery

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Rationalization

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Applications in AI

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Bayesian Modeling

Bayesian Induction

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Generative Model

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Nonparametric Model

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Bayesian Optimization

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Concepts

Theory of Concepts

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Human Concept Representation

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AI Concept Representation

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Complexity & Information Theory

Theory

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Dimensionality Reduction

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Visual Complexity

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Communications

Non-Verbal Communication

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Pragmatics

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Language Compositionality

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Coordination

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Domain Specific Language

Design Theory

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Design Practises

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Design Automation

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Imperative DSL Applications

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Declarative DSL Applications

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Logic DSL Applications

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DSL Program Synthesis

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Problem Solving

Human-Level Problem Solving

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Planning

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Intrinsic Motivation

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Reinforcement Learning

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Inverse Reinforcement Learning

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System 1 & System 2

Dual-Coding Theory

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Neural-Symbolic AI

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Explainability

Trustworthy AI

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Strong Machine Learning

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Explainable Deep Learning

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Embodied Intelligence

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Evolutionary Intelligence

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Methodologies for Experiments

Quantitative Analysis

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Scaling Up Behavioral Studies

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Decision Making

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Question Answering

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Human-Machine Comparison

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Association Test

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Virtual Reality

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Meta-Level Considerations

Meta Learning

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Marr's Levels of Analysis

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Gestalt

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The Aha! Moment

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Rationality

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Cognitive Architecture

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Science Logology

Philosophy of Science

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Science of Science

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Literature Mining

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Scientific Writing

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Science Education

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Democratization of Science

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Laboratory Automation

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AI Assisted Research

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Theory of Mind

<!--* [Cognitive Science](https://plato.stanford.edu/entries/cognitive-science/) - ***Plato Stanford***. * [The Mind/Brain Identity Theory](https://plato.stanford.edu/entries/mind-identity/) - ***Plato Stanford***. * [Mental Representation](https://plato.stanford.edu/entries/mental-representation/) - ***Plato Stanford***. * [Mental Imagery](https://plato.stanford.edu/entries/mental-imagery/) - ***Plato Stanford***. * [Temporal Consciousness](https://plato.stanford.edu/entries/consciousness-temporal/) - ***Plato Stanford***. * [The Experience and Perception of Time](https://plato.stanford.edu/entries/time-experience/) - ***Plato Stanford***. * [Practical Reason](https://plato.stanford.edu/entries/practical-reason/) - ***Plato Stanford***. * [Memory](https://plato.stanford.edu/entries/memory/) - ***Plato Stanford***.--> <!-- * [The Computational Theory of Mind](https://plato.stanford.edu/entries/computational-mind/) - ***Plato Stanford***. A computational philosophy account on ToM. -->

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Analogy

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Causality

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Commonsense

Intuitive Physics

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AI Commonsense Reasoning

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Commonsense Knowledgebase

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Inductive Logic & Program Synthesis

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Knowledge Representation

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Cognitive Development

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Learning in the Open World

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Learning with Cognitive Plausibility

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<!-- ### Tasks & Environments #### Dataset Aggregation * [A Dataset and Architecture for Visual Reasoning with a Working Memory](https://link.springer.com/chapter/10.1007%2F978-3-030-01249-6_44) - ***ECCV'18***, 2018. [[Project](https://github.com/google/cog)]. * [PHYRE: A New Benchmark for Physical Reasoning](https://research.fb.com/wp-content/uploads/2019/08/PHYRE-A-New-Benchmark-for-Physical-Reasoning-v4.pdf) - ***NeurIPS'19***, 2019. * [CATER: A diagnostic dataset for Compositional Actions & TEmporal Reasoning](https://openreview.net/forum?id=HJgzt2VKPB) - ***ICLR'20***, 2020. [[Project](https://rohitgirdhar.github.io/CATER/)]. * [CausalWorld: A Robotic Manipulation Benchmark for Causal Structure and Transfer Learning](https://arxiv.org/abs/2010.04296), 2020. #### Embodied AI Environment * [ThreeDWorld](http://www.threedworld.org/) - ***MIT-IBM***. [[Paper](https://arxiv.org/abs/2007.04954)]. * [Rearrangement: A Challenge for Embodied AI](https://arxiv.org/pdf/2011.01975.pdf), 2020. * [iGibson](http://svl.stanford.edu/igibson/) - ***Stanford***. [[Paper](https://ieeexplore.ieee.org/document/8954627)]. * [AI2-THOR](https://ai2thor.allenai.org/ithor) - ***Allen Institute***. [[Paper](https://arxiv.org/abs/1712.05474)]. * [Robo-THOR](https://ai2thor.allenai.org/robothor) - ***Allen Institute***. [[Paper](https://arxiv.org/abs/2004.06799)]. * [Manipula-THOR](https://ai2thor.allenai.org/manipulathor) - ***Allen Institute***. [[Paper](https://arxiv.org/abs/2104.11213)]. * [RLBench](https://sites.google.com/view/rlbench) - ***Imperial College***. [[Paper](https://ieeexplore.ieee.org/document/9001253)]. #### First-Person Vision * [First-Person Vision](https://ieeexplore.ieee.org/document/6232429) - ***Proceedings of the IEEE***, 2012. * [The Evolution of First Person Vision Methods: A Survey](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7055926) - ***Trans. CSVT***, 2015. * [Understanding the Nature of First-Person Videos: Characterization and Classification using Low-Level Features](http://vijaychan.github.io/Publications/2014%20CVPR%20Workshop%20-%20Understanding%20the%20Nature%20of%20First-Person%20Videos.pdf) - ***CVPR'14***, 2014. * [Pooled Motion Features for First-Person Videos](https://openaccess.thecvf.com/content_cvpr_2015/papers/Ryoo_Pooled_Motion_Features_2015_CVPR_paper.pdf) - ***CVPR'15***, 2015. * [Actor and Observer: Joint Modeling of First and Third-Person Videos](https://openaccess.thecvf.com/content_cvpr_2018/papers/Sigurdsson_Actor_and_Observer_CVPR_2018_paper.pdf) - ***CVPR'18***, 2018. * [Forecasting Human-Object Interaction: Joint Prediction of Motor Attention and Actions in First Person Video](https://link.springer.com/chapter/10.1007/978-3-030-58452-8_41) - ***ECCV'20***, 2020. * [Rolling-Unrolling LSTMs for Action Anticipation from First-Person Video](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9088213) - ***Trans. PAMI***, 2020. * [View-Action Representation Learning for Active First-Person Vision](https://ieeexplore.ieee.org/document/9064828) - ***Trans. CSVT***, 2021. * [Design and Use Paradigms for Gazebo, An Open-Source Multi-Robot Simulator](https://ieeexplore.ieee.org/abstract/document/1389727) - ***IROS'04***, 2004. [[Project](http://gazebosim.org/)]. * [ViZDoom: A Doom-based AI Research Platform for Visual Reinforcement Learning](https://arxiv.org/pdf/1605.02097v2.pdf) - ***CIG'16***, 2016. [[Project](http://vizdoom.cs.put.edu.pl/)]. * [Is First Person Vision Challenging for Object Tracking? The TREK-100 Benchmark Dataset](https://arxiv.org/abs/2011.12263), 2020. * **Visual Experience Database** [[Project](http://visualexperiencedatabase.org/research.html)]. [[Publications](http://visualexperiencedatabase.org/publications.html)]. #### Abstract Reasoning Challenge * [On the Measure of Intelligence](https://arxiv.org/pdf/1911.01547.pdf) - ***Google Research***, 2019. * [Abstract Reasoning Challenge](https://www.kaggle.com/c/abstraction-and-reasoning-challenge/) #### AI Birds Challenge * [AI-Birds](https://aibirds.org) - ***IJCAI***. * [Hi-Phy: A Benchmark for Hierarchical Physical Reasoning](https://openreview.net/forum?id=AcL1ORzw0Nf), 2021. #### Minecraft * [Mining Learning and Crafting Scientific Experiments: A Literature Review on the Use of Minecraft in Education and Research](https://eric.ed.gov/?id=EJ1097278) - ***Journal on Eduction Technology & Society***, 2016. ##### Malmo Platform for Minecraft AI * [The Malmo Platform for Artificial Intelligence Experimentation](https://www.microsoft.com/en-us/research/publication/malmo-platform-artificial-intelligence-experimentation/) ***IJCAI'16***, 2016. * [[Malmo](https://github.com/Microsoft/malmo#getting-started)]. * [[Malmo-env](https://github.com/Microsoft/malmo/tree/master/MalmoEnv)]. * [[Malmo-Tutorials](https://microsoft.github.io/malmo/0.17.0/Python_Examples/Tutorial.pdf)]. * [[MineRL](https://minerl.io/)]. * [[MarLo Challenge 2018](https://github.com/crowdAI/marLo)]. ##### **Artificial Intelligence** * [Multi-task curriculum learning in a complex, visual, hard-exploration domain: Minecraft](https://arxiv.org/abs/2106.14876), 2021. * [Learning to execute instructions in a Minecraft dialogue](https://www.aclweb.org/anthology/2020.acl-main.232/) - ***ACL'20***, 2020. * [Collaborative Dialogue in Minecraft](https://www.aclweb.org/anthology/P19-1537.pdf) - ***ACL'19***, 2019. * [Learning Skill Hierarchies from Predicate Descriptions and Self-Supervision](http://web.mit.edu/tslvr/www/papers/genplan20_camera_ready.pdf) - ***AAAI GenPlan Workshop***, 2020. * [AMRL: Aggregated Memory for Reinforcement Learning](https://openreview.net/pdf?id=Bkl7bREtDr) - ***ICLR'20***, 2020. * [MineRL: A Large-Scale Dataset of Minecraft Demonstrations](https://www.ijcai.org/Proceedings/2019/0339.pdf) ***IJCAI'19***, 2019. [[2020 Competition](https://arxiv.org/abs/2106.03748)]. * [Design Mining for Minecraft Architecture](http://www.cs.cornell.edu/~eland/papers/aiide2018.pdf) - ***AAAI'18***, 2018. * [Adaptive Agents in Minecraft: A Hybrid Paradigm for Combining Domain Knowledge with Reinforcement Learning](https://link.springer.com/chapter/10.1007%2F978-3-319-71679-4_6) - ***AAMAS'17***, 2017. * [Asynchronous Data Aggregation for Training End to End Visual Control Networks](https://www.microsoft.com/en-us/research/wp-content/uploads/2017/05/fp185-monfort-1.pdf) - ***AAMAS'17***, 2017. * [A Deep Hierarchical Approach to Lifelong Learning in Minecraft](https://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14630/13950) - ***AAAI'17***, 2017. * [Modular Multitask Reinforcement Learning with Policy Sketches](http://proceedings.mlr.press/v70/andreas17a.html) - ***ICML'17***, 2017. * [Control of memory, active perception, and action in minecraft](http://proceedings.mlr.press/v48/oh16.pdf) - ***ICML'16***, 2016. * [Learning Behavior from Demonstration in Minecraft via Symbolic Similarity Measures](fdg2015.org/papers/fdg2015_paper_11.pdf) - ***FDG'15***, 2015. ##### **Cognitive Science** * [How Players Speak to an Intelligent GameCharacter Using Natural Language Messages](http://todigra.org/index.php/todigra/article/view/88/139) - ***DiGRA***, 2018. * [Minecraft as a Generative Platform for Analyzing and Practicing Spatial Reasoning](https://link.springer.com/chapter/10.1007%2F978-3-030-57983-8_22) - ***Spatial Cognition'20***, 2020. * [Generative Design in Minecraft: Chronicle Challenge](http://computationalcreativity.net/iccc2019/papers/iccc19-lbp-7.pdf) - ***ICCC'20***, 2020. * [Minecraft as a Platform for Project-Based Learning in AI](https://aaai.org/ojs/index.php/AAAI/article/view/7070) - ***AAAI'20***, 2020. * [MC-Saar-Instruct: a Platform for Minecraft Instruction Giving Agents](https://www.aclweb.org/anthology/2020.sigdial-1.7.pdf) - ***SIGDial'20***, 2020. *[Back to Top](#c)-->

Academic Tools

Courses

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Programming

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Paper Writing

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Paper Reading

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Literature Management

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Knowledge Management

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Institute & Researcher

MIT

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Stanford

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Princeton

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Harvard

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UCLA

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UC Berkeley

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BNU

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PKU

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UCSD

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NYU

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JHU

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SIT

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People & Book

John Hopcroft

Theoretical computer scientist.

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Ulf Grenander

Applied mathematician, the founder of General Pattern Theory.

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David Marr

Computational Cognitive Neuroscientist, the establisher of the Levels of Analysis.

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Michael Tomasello

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

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Judea Pearl

Applied mathematician, proposed causal intervention on siamese bayesian networks.

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Susan Carey

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

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Daniel Kahneman

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

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Karl Popper

Scientific philosophor, the founder of scientific verification theories.

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About

The initiator 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 the literature reviews when the initiator is working on Abduction and Bayesian modeling, other topics are also collected with biases, more or less. Abduction may be 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!

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