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Functional Programming

Type Inference


  • (incorporates former Speagram)


Kurs Pascala

Artificial General Intelligence


Algorithmic Game Theory: Prediction Markets (po polsku)

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kurs pracy w systemie Linux

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Overall course focus: (hmm…)

  1. Reinforcement learning.
  2. Concept formation and program synthesis.
  3. Adaptive and probabilistic logics.
  4. Mental development theory.


  1. Universal Artificial Intelligence: universal induction, exhaustive program search and reinforcement learning algorithms Δ (TeXmacs source Δ)
  2. Techniques of Reinforcement Learning Δ (TeXmacs source Δ)
    1. RL_Ch6_Evolutionary_modular.pdf Δ
    2. RL_Ch7_Hierarchical_RL.pdf Δ
  3. General Game Playing Δ (TeXmacs source Δ)
  4. Knowledge Representation and Language Δ (TeXmacs source Δ)
  5. Adaptive Logics for Reasoning Systems
    1. Adaptive (or Defeasible) Logics and OSCAR Δ (TODO: complete the notes about OSCAR)
    2. Frequency and/or Uncertainty Logics: Non Axiomatic Logic, Probabilistic Logic Networks (to come)
    • I’ve presented OSCAR and comments on John Pollock’s theory supported by his slides and article figures, I also introduced “propositional” NARS
  6. Estimation of Distribution Algorithms and Genetic Programming Δ (TeXmacs source Δ)
  7. Inductive (Logic) Programming
  8. Spreading Activation: memory retrieval, distributed reasoning, action selection, probabilities Δ (work in progress) approaches based on spreading activation mechanism or strong biological inspirations
  9. The Representation and Acquisition of Concepts Δ (to come later)
  10. Probabilistic Modeling and Probabilistic Logics
    1. OLD: Markov Logic Networks Δ
    2. NEW: Propositional Probabilistic Graphical Models Δ (TeXmacs source Δ)
    3. to come: Relational Probabilistic Models and Logics
  11. Cognitive Architectures

I’ve moved KR before logics to introduce representation means of representation-specific reasoning systems (NARS and PLN) there.


  1. Values and Others: Grounding Agents in Game Semantics Δ (to come)
    • We build semantics in both representational and logic aspects based on the notion of agent specific rewards/motivations.

Reviews: Artificial General Intelligence: A Gentle Introduction by Pei Wang

Major online reading:

More online reading:

Major offline reading (available to me):

  • “Artificial General Intelligence”, Ben Goertzel, Cassion Pennachin (editors), 2007, Cognitive Technologies series at Springer
  • “Rigid Flexibility. The Logic of Intelligence”, Pei Wang, 2006, Applied Logic series at Springer
  • “Universal Artificial Intelligence. Sequential Decisions based on Algorithmic Probability”, Marcus Hutter, 2005, Texts in Theoretical Computer Science series at Springer
  • “Knowledge Representation and the Semantics of Natural Language”, Hermann Helbig, 2006, Cognitive Technologies series at Springer
  • “The Cambridge Handbook of Thinking and Reasoning”, Keith Holyoak, Robert Morrison (editors), Cambridge University Press, 2005


Architectures / projects:

Some video lectures (currently not well selected):

Other links:


Outdated plan:

  1. Information, distributions, programs, intelligence.
    1. Shannon information and Kolmogorov information, measures of complexity.
    2. Decision and control theory topics. Markov decision processes, reinforcement learning (Q-learning, SARSA etc.).
      1. SAIL and Dav: robots that learn “from scratch”.
    3. “General algorithmic intelligence” AIXI.
    4. Self improving programs: “Goedel Machine”. “Verificationist” program synthesis.
    5. Graphical probability models.
      1. Bayesian networks.
      2. Hierarchical Temporal Memory from Numenta.
      3. Introduction to “estimation of distribution” algorithms.
    6. “Optimizationist” competent program synthesis: algorithm MOSES.
    7. Representing and learning concepts. PAC-learnability.
      1. Learning grammars.
      2. Higher order and recursive structure representation induction.
  2. Logic in a dynamic world.
    1. Adaptive logics overview (circumscription, defeasible argumentation, belief revision, etc.) Intensional and term logics.
      1. From semantic networks to logic: system SNePS.
    2. Reasoning about probability and uncertainty.
      1. Game semantics for logics.
      2. Recursive probability models.
      3. Probabilistic term logic. “Two-dimensional” truth values: system NARS.
      4. “Probabilistic Logic Networks” in Novamente.
    3. Inductive probabilistic logic programming vel probabilistic logic learning.
  3. Cognitive loop (in search for the “main()” of the artificial mind).
    1. Inference system as an agent: goals and activations. (SNePS, NARS)
    2. Cognitive loop in LIDA.
    3. Concept formation and modeling of self. (Novamente)
  4. Theory of mental development.
    1. Piagetan psychology.
    2. Mental development of an AGI.
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Page last modified on May 06, 2014, at 05:21 PM