AGI
My AGI research roadmap: sixteen subfields to work through, a month-by-month reading list built like a self-taught graduate seminar, and the people worth following.
On this page
Designing Artificial Intelligence
- What will drive the future?
- Emotion response to product
Goal
- To create an agent that lives in computational space and is able to acquire and organize knowledge and use that to interact with humans and the world.
- To create a physical agent which evolves like living systems to understand and help ourselves.
Currently Doing
- Invisible Machines
- Philosophy of Science
- Read readings
- Organize this page
Approaches in AI I Want to Learn
Problems to Solve
Readings
Cognitive Science
Overview
- MIT Encyclopedia of the Cognitive Sciences
- The Cambridge Handbook of Intelligence
History of AI, Science & Life
AI Algorithms, Structures and Methods
- Handbook of Practical Logic and Automated Reasoning
- Artificial Intelligence by Russell and Norvig
- Reinforcement Learning, Sutton & Barto
- D. P. Bertsekas and J. N. Tsitsiklis, Neuro-Dynamic Programming
- P. R. Kumar and P. P. Varaiya, Stochastic Systems: Estimation, Identification, and Adaptive Control
- Information Theory
- M. J. Osborne and A. Rubenstein, A Course in Game Theory
- Y. Shoham and K. Leyton-Brown, Multiagent Systems: Algorithmic, Game-Theoretic, and Logical Foundations
- Neural Networks and Learning Machines, by Simon Haykin
- Learning to Learn, Springer
Cognitive Neuroscience & Psychology
Linguistics
- Foundations of Language by Ray Jackendoff
- Speech and Language Processing
Anthropology
Evolutionary
- The Design of Innovation by David Goldberg, a deep, wide-ranging and readable discussion on evolutionary learning
- Introduction to Evolutionary Computing
Readings on Advanced Topics
Each of the following topics can be covered in a graduate seminar for roughly a month, using the listed materials.
- 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; On Defining Artificial Intelligence, Pei Wang, with Commentaries and Author's Response
- 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
- 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
- Machine learning: Deep Learning, Yann LeCun, Yoshua Bengio, Geoffrey Hinton; Deep Learning in Neural Networks: An Overview, Juergen Schmidhuber; Mastering the game of Go with deep neural networks and tree search, David Silver et al.; Different Conceptions of Learning: Function Approximation vs. Self-Organization, Pei Wang, Xiang Li
- 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
- 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
- Term logics: 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
- 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
- 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
- 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; Action in Perception, Alva Nöe; Perception from an AGI Perspective, Pei Wang, Patrick Hammer
- 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
- 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
- Planning and decision making: 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
- 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 and The Emotional Mechanisms in NARS, Pei Wang
- Cognitive linguistics: Cognitive Linguistics: Basic Readings, Dirk Geeraerts; Language, Thought, and Logic, John M. Ellis; Natural Language Processing by Reasoning and Learning, Pei Wang
- Self and Consciousness: A Cognitive Theory of Consciousness, Bernard Baars; Metacognition in computation: A selected research review, Michael T. Cox; Consciousness, Intentionality, and Causality, Walter J. Freeman; Self in NARS, an AGI System, Pei Wang, Xiang Li, Patrick Hammer
- Cognitive architecture: Unified Theories of Cognition, Allen Newell; An Integrated Theory of the Mind, John R. Anderson, et al.; Rigid Flexibility: The Logic of Intelligence, Pei Wang
- 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.; Solving a Problem With or Without a Program, Pei Wang
- 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; From NARS to a Thinking Machine, Pei Wang