deep-econom (deep_econom) wrote,

образование для исследователей AGI (от Пей Ван / Pei Wang)

Предлагаемое образование
для будущих исследователей AGI

Пей Ван

Следующий список представляет собой
частичный план обучения для студентов, заинтересованных в иссле

Suggested Education for Future AGI Researchers

Pei Wang

The following list is a partial education plan for students interested in the research of Artificial General Intelligence. It includes materials for roughly 25 one-semester courses.


  1. The opinions expressed here are highly personal. Not only are the topics and reading materials selected according to my opinion, but also there are my own works included wherever relevant (they are distinguished from the others using square brackets).

  2. This list is not intended to cover all relevant topics, but what I think as the most important. Some crucial decisions are on what NOT to include, as well as on how to allocate time among the topics. Therefore, adding new topics into the list is not always a good idea.

Introductory Readings

The following materials can be read by anyone with a high-school education.

A. Undergraduate-level Coursework

Each of the following topics can be covered by a one-semester undergraduate course, with the recommended textbook.

  1. Discrete Mathematics
    Discrete Mathematics and Its Applications, 7/E, Kenneth Rosen

  2. Probability and Statistics
    A Modern Introduction to Probability and Statistics, 2/E, Dekking et al.

  3. Computer Programming
    Java How to Program, 7/E, Deitel & Associates

  4. Data Structure and Algorithms
    Data Structures and Algorithm Analysis in Java, 2/E, Mark Allen Weiss

  5. Operating System
    Operating System Concepts, 9/E, Avi Silberschatz et al.

  6. Cognitive Psychology
    Cognitive Psychology, 4/E, Douglas Medin et al.

  7. Cognitive Neuroscience
    Cognition, Brain, and Consciousness, Bernard J. Baars, Nicole M. Gage

  8. Language and Cognition
    Language in Mind: An Introduction to Psycholinguistics, 1/E,  Julie Sedivy

  9. Theory of Knowledge
    Epistemology, Richard Feldman

  10. Artificial Intelligence
    Artificial Intelligence: Structures and Strategies for Complex Problem Solving, 6/E, George F. Luger

B. Graduate-level Study

Each of the following topics can be covered by a one-semester graduate course (or upper-division undergraduate course), with the recommended textbook.

  1. Theoretical Computer Science
    Introduction to Automata Theory, Languages, and Computation, 3/E, John E. Hopcroft, Rajeev Motwani, Jeffrey D. Ullman

  2. Reasoning Under Uncertainty
    Readings in Uncertain Reasoning, Glenn Shafer, Judea Pearl

  3. Machine Learning
    Machine Learning, Peter Flach

  4. Philosophical Logic
    Philosophy of Logics, Susan Haack

  5. Decision Theory
    Rationality in Action: Contemporary Approaches, Paul K. Moser

  6. Categorization
    Concepts: Core Readings, Eric Margolis, Stephen Laurence

  7. Perception and Action
    Sensorimotor Foundations of Higher Cognition, Patrick Haggard, Yves Rossetti, Mitsuo Kawato

  8. Memory
    Human Memory: Theory And Practice, A.D. Baddeley

  9. Developmental Psychology
    Theories of Developmental Psychology, 6/E, Patricia A. Miller

  10. Philosophy of Science
    Philosophy of Science: The Central Issues, 2/E, J. A. Cover, Martin Curd

C. Readings on Advanced Topics

Each of the following topics can be covered in a graduate seminar for roughly a month, using the listed materials.

  1. Research goal(s) of AI
    From here to Human-Level AI, John McCarthy
    Human-level artificial intelligence? Be serious!, Nils J. Nilsson
    (AA)AI: more than the sum of its parts, Ronald J. Brachman
    Universal Intelligence: A Definition of Machine Intelligence, Shane Legg, Marcus Hutter
    [What Do You Mean by "AI"? Pei Wang]

  2. Limitation of AI
    Minds, machines and Gödel, J. R. Lucas
    What Computers Can't Do, Hubert L. Dreyfus
    Minds, Brains, and Programs, John R. Searle
    The Emperor's New Mind, Roger Penrose
    [Three Fundamental Misconceptions of Artificial Intelligence, Pei Wang]

  3. Symbolic vs. connectionist AI
    Computer Science as Empirical Inquiry: Symbols and Search, Allen Newell, Herbert A. Simon
    Waking Up From the Boolean Dream, or, Subcognition as Computation, Douglas Hofstadter
    On the proper treatment of connectionism, Paul Smolensky
    Connectionism and Cognitive Architecture: a Critical Analysis, Jerry A. Fodor, Zenon W. Pylyshyn
    [Artificial General Intelligence and Classical Neural Network, Pei Wang]

  4. Machine learning
    Deep Learning, Yann LeCun, Yoshua Bengio, Geoffrey Hinton
    Mastering the game of Go with deep neural networks and tree search, David Silver et al.
    Deep Learning in Neural Networks: An Overview, Juergen Schmidhuber
    Systems That Learn: An Introduction to Learning Theory for Cognitive and Computer Scientists, Daniel N. Osherson, Michael Stob, Scott Weinstein
    [Different Conceptions of Learning: Function Approximation vs. Self-Organization, Pei Wang, Xiang Li]

  5. Non-classical computation
    Thinking may be more than computing, Peter Kugel
    Approximate Reasoning Using Anytime Algorithms, Shlomo Zilberstein
    Turing's Ideas and Models of Computation, Eugene Eberbach, Dina Goldin, Peter Wegner
    [Case-by-case Problem Solving, Pei Wang]

  6. Credit assignment and resource allocation
    Principles of Meta-Reasoning, Stuart Russell, Eric Wefald
    Manifesto for an Evolutionary Economics of Intelligence, Eric B. Baum
    Properties of the Bucket Brigade, John Holland
    The Parallel Terraced Scan: An Optimization For An Agent-Oriented Architecture, John Rehling, Douglas Hofstadter
    [Problem-Solving under Insufficient Resources, Pei Wang]

  7. Term logic
    Term logic, Wikipedia
    An Invitation to Formal Reasoning: The Logic of Terms, Frederic Sommers, George Englebretsen
    [Non-Axiomatic Logic: A Model of Intelligent Reasoning, Pei Wang]

  8. Uncertain probabilities
    Towards a unified theory of imprecise probability, Peter Walley
    Probabilistic Logic Networks, Ben Goertzel et al.
    [Confidence as Higher-Order Uncertainty, Pei Wang]

  9. Non-Tarskian semantics
    Holism, Conceptual-Role Semantics, and Syntactic Semantics, William J. Rapaport
    Logic without Model Theory, Robert Kowalski
    Contentful Mental States for Robot Baby, Paul R. Cohen et al.
    Procedural semantics, Philip N. Johnson-Laird
    [Experience-Grounded Semantics: A theory for intelligent systems, Pei Wang]

  10. Sensorimotor and cognition
    Intelligence without representation, Rodney A. Brooks
    How the Body Shapes the Way We Think: A New View of Intelligence, Rolf Pfeifer, Josh C. Bongard
    The symbol grounding problem, Stevan Harnad
    Perceptual symbol systems, Lawrence W. Barsalou
    The Ecological Approach to Visual Perception, James J. Gibson
    A sensorimotor account of vision and visual consciousness, J. Kevin O’Regan, Alva Nöe
    [Embodiment: Does a laptop have a body? Pei Wang]

  11. Analogy and metaphor
    The Analogical Mind, Dedre Gentner, Keith J. Holyoak, Boicho K. Kokinov
    Fluid Concepts and Creative Analogies, Douglas Hofstadter, FARG
    Metaphors We Live By, George Lakoff, Mark Johnson
    Case-Based Reasoning: Experiences, Lessons, & Future Directions, David B. Leake
    [Analogy in a general-purpose reasoning system, Pei Wang]

  12. Animal cognition
    The Principles of Learning and Behavior, Michael Domjan
    Animal Minds: Beyond Cognition to Consciousness, Donald R. Griffin
    The Thinking Ape: Evolutionary Origins of Intelligence, Richard Byrne
    [Issues in Temporal and Causal Inference, Pei Wang, Patrick Hammer]

  13. Reasoning about change
    Robot's Dilemma Revisited: The Frame Problem in Artificial Intelligence, Zenon W. Pylyshyn
    Some Philosophical Problems from the Standpoint of Artificial Intelligence, John McCarthy, Patrick J. Hayes
    Reasoning about plans, James F. Allen et al.
    Reinforcement Learning: An Introduction, Richard S. Sutton and Andrew G. Barto
    [Assumptions of decision-making models in AGI, Pei Wang, Patrick Hammer]

  14. Motivation and emotion
    Human Motivation, David C. McClelland
    The Functional Autonomy of Motives, Gordon W. Allport
    The Emotion Machine: Commonsense Thinking, Artificial Intelligence, and the Future of the Human Mind, Marvin Minsky
    Who Needs Emotions?: The Brain Meets the Robot, Jean-Marc Fellous, Michael A. Arbib
    [Motivation Management in AGI Systems, Pei Wang]

  15. Cognitive linguistics
    Cognitive Linguistics: Basic Readings, Dirk Geeraerts
    Language, Thought, and Logic, John M. Ellis
    [Natural Language Processing by Reasoning and Learning, Pei Wang]

  16. Self
    I Am a Strange Loop, Douglas R. Hofstadter
    A Cognitive Theory of Consciousness, Bernard Baars
    Metacognition in computation: A selected research review, Michael T. Cox
    [Self in NARS, an AGI System, Pei Wang, Xiang Li, Patrick Hammer]

  17. Cognitive architecture
    Unified Theories of Cognition, Allen Newell
    An Integrated Theory of the Mind, John R. Anderson, et al.

  18. Robotics
    An Introduction to AI Robotics, Robin R. Murphy
    Prospects for Human Level Intelligence for Humanoid Robots, Rodney A. Brooks
    Autonomous Mental Development by Robots and Animals, Juyang Weng et al.

  19. Agent and multi-agent system
    The Society of Mind, Marvin Minsky
    Agent Technology: Foundations, Applications, and Markets, Nicholas R. Jennings, Michael J. Wooldridge
    Multiagent Systems: A Modern Approach to Distributed Artificial Intelligence, Gerhard Weiss

  20. Contemporary AGI research
    Collections: 2007 , 2012
    Journal: JAGI
    [Rigid Flexibility: The Logic of Intelligence, Pei Wang]


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