Suggested Education for Future AGI Researchers

https://sites.google.com/site/narswang/home/agi-introduction/agi-education

http://www.cis.temple.edu/~pwang/Writing/AGI-Curriculum.html 这个会跳转到谷歌上面链接。

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.

Notes:

  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.

  • Computing machinery and intelligence, Alan M. Turing
  • Gödel, Escher, Bach: An Eternal Golden Braid, Douglas R. Hofstadter

A. Undergraduate-level Coursework

Each of the following topic 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 topic 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, 4/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 topic 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. Deep 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 [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 Hostadter [Problem-Solving under Insufficient Resources, Pei Wang]
  7. Term logics Term logic, Wikipedia An Invitation to Formal Reasoning: The Logic of Terms, Frederic Sommers, George Englebretsen [Unified Inference in Extended Syllogism, 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. Embodied and situated 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 [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]
  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]
  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
  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 Conferences Collections: 2007 , 2012 Journal: JAGI [Rigid Flexibility: The Logic of Intelligence, Pei Wang]

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.

Notes:

  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.

  • Computing machinery and intelligence, Alan M. Turing
  • Gödel, Escher, Bach: An Eternal Golden Braid, Douglas R. Hofstadter

A. Undergraduate-level Coursework

Each of the following topic 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 topic 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, 4/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 topic 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. Deep 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 [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 Hostadter [Problem-Solving under Insufficient Resources, Pei Wang]
  7. Term logics Term logic, Wikipedia An Invitation to Formal Reasoning: The Logic of Terms, Frederic Sommers, George Englebretsen [Unified Inference in Extended Syllogism, 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. Embodied and situated 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 [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]
  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]
  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
  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 Conferences Collections: 2007 , 2012 Journal: JAGI [Rigid Flexibility: The Logic of Intelligence, Pei Wang]

原文发布于微信公众号 - CreateAMind(createamind)

原文发表时间:2018-01-17

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