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Awesome Artificial General Intelligence and Computational Cognitive Sciences

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.

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Contents

• Papers
  • Abduction
    • Explanation
    • Scientific Discovery
    • Rationalization
    • Applications in AI
  • Bayesian Modeling
    • Bayesian Induction
    • Generative Model
    • Nonparametric Model
    • Bayesian Optimization
  • Concepts
    • Theory of Concepts
    • Human Concept Represenataion
    • AI Concept Representation
  • Complexity & Information Theory
    • Theory
    • Dimensionality Reduction
    • Visual Complexity
  • Communications
    • Non-Verbal Communication
    • Pragmatics
    • Language Compositionality
    • Coordination
  • Domain Specific Language
    • Design Theory
    • Design Practises
    • Imperative DSL Applications
    • Declarative DSL Applications
    • Logic DSL Applications
    • DSL Program Synthesis
  • 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
    • Decision Making
    • Question Answering
    • Human-Machine Comparison
    • Association Test
    • Virtual Reality
  • Meta-Level Considerations
    • Meta Learning
    • Marr's Levels of Analysis
    • Gestalt
    • The Aha! Moment
    • Rationality
    • Cognitive Architecture
  • Science Logology
    • Philosophy of Science
    • Science of Science
    • Literature Mining
    • Scientific Writing
    • Science Education
    • Democratization of Science
    • Laboratory Automation
    • AI Assisted Research
  • 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
  • Learning with Cognitive Plausibility
  <!--* [Tasks & Environments](#te)-->
• Academic Tools
  • Courses
  • Programming
  • Paper Writing
  • Paper Reading
  • Literature Management
  • Knowledge Management
• Institute & Researcher
  • MIT
  • Stanford
  • Princeton
  • Harvard
  • UCLA
  • UC Berkeley
  • BNU
  • PKU
  • UCSD
  • NYU
  • JHU
  • SIT
• People & Book
  • John Hopcroft
  • Ulf Grenander
  • David Marr
  • Michael Tomasello
  • Judea Pearl
  • Susan Carey
  • Daniel Kahneman
  • Karl Popper
• About

Papers

Abduction

Explanation

• Abduction - Plato Stanford. A computational philosophy account on Abduction, one of the three thinking patterns besides Induction and Deduction, being unique for its potential to introduce new ideas into current knowledge.

• Scientific Explanation - Plato Stanford. A computational philosophy account on Scientific Explanation, a canonical application of Abduction.

• Scientific Reduction - Plato Stanford. A computational philosophy account on Scientific Reduction, which comes with no explicit boundary with Explanation.

• Non-monotonic Logic - Plato Stanford. A computational philosophy account on Non-monotonic Logic, a family of formal frameworks devised to capture and represent defeasible inference.

• Philosophical Writings of Peirce - Courier Corporation, 1955. [All Versions]. Original writings by C. S. Peirce, the establisher of Abduction.

• The Inference to the Best Explanation - Philosophical Review, 1965. [All Versions]. Lipton's original paper on Inference to the Best Explanation as a special case of Abduction.

• Inference to the Best Explanation - Routledge, 1991. [All Versions]. Lipton's book on Inference to the Best Explanation as a special case of Abduction.

• A Study of Thinking - Routledge, 1956. [All Versions]. A classic book on thinking patterns.

• Abductive Reasoning and Learning - Springer, 2000. [All Versions]. An introductory account on abductive reasoning.

• Abductive Reasoning: Logical Investigations into Discovery and Explanation - Springer, 2006. [All Versions]. An introductory account on abductive reasoning.

• 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]. A handbook on the formulations of Abduction.

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

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

• Explanatory Preferences Shape Learning and Inference - Trends in Cognitive Sciences, 2016. [All Versions]. An account showing that inductive bias is critical for explanation.

• 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]. A subjective probability account of Abduction.

• How explanation guides belief change - Trends in Cognitive Sciences, 2021. [All Versions]. A review on the subjective probability account of Abduction.

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

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

• Patterns of abduction - Synthese, 2007. [All Versions]. A categorization for Abduction in the account of pure philosophy.

• 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