Overall course focus: (hmm…)
- Reinforcement learning.
- Concept formation and program synthesis.
- Adaptive and probabilistic logics.
- Mental development theory.
Notes:
- Universal Artificial Intelligence: universal induction, exhaustive program search and reinforcement learning algorithms (TeXmacs source)
- Techniques of Reinforcement Learning (TeXmacs source)
- Attach:RL_Ch6_Evolutionary_modular.pdf
- Attach:RL_Ch7_Hierarchical_RL.pdf
- General Game Playing (TeXmacs source)
- Knowledge Representation and Language (TeXmacs source)
- Adaptive Logics for Reasoning Systems
- Adaptive (or Defeasible) Logics and OSCAR (TODO: complete the notes about OSCAR)
- 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
- Estimation of Distribution Algorithms and Genetic Programming (TeXmacs source)
- Inductive (Logic) Programming
- Spreading Activation: memory retrieval, distributed reasoning, action selection, probabilities (work in progress) approaches based on spreading activation mechanism or strong biological inspirations
- The Representation and Acquisition of Concepts Δ (to come later)
- based mostly on The Representation and Acquisition of Concepts. Psychology 747, Section 4097 - Fall 2003 [1], but also “Conceptual Spaces. Geometry of thought” and more
- Probabilistic Modeling and Probabilistic Logics
- OLD: Markov Logic Networks
- NEW: Propositional Probabilistic Graphical Models (TeXmacs source)
- to come: Relational Probabilistic Models and Logics
- Cognitive Architectures
I’ve moved KR before logics to introduce representation means of representation-specific reasoning systems (NARS and PLN) there.
Considered:
- 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 [2] by Pei Wang
Major online reading:
- Reinforcement Learning: An Introduction [3] Richard S. Sutton and Andrew G. Barto
- Proceedings of the AGIRI Workshop 2006 [4] (Editors: Ben Goertzel, Pei Wang), IOS Press
- AGI-08. Proceedings of the First Conference on Artificial General Intelligence [5], (Editors: Pei Wang, Ben Goertzel, Stan Franklin), IOS Press
More online reading:
- Thinking about Acting [6] by John Pollock
- A Working Hypothesis for General Intelligence [7], Eric B. Baum, October 2006
- Mathematical definition of “intelligence” (and consequences) [8], Warren D. Smith, June 2006
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
Places:
- Artificial General Intelligence Research Institute [9]
- OpenCog [10]
- Association for Uncertainty in Artificial Intelligence [11]
- Adaptive Logics Home Page [12]
- Reinforcement Learning and Artificial Intelligence [13] “RLAI research is research directed toward the long-standing goals of AI (understanding the mind, reproducing human abilities) and is based on reinforcement learning ideas (learning from and while interacting with the world).”
-
Architectures / projects:
- Novamente [14]
-
- LIDA [15]
- NARS [16]
- PolyScheme [17]
- SNePS [18]
- LEVELS OF ORGANIZATION IN GENERAL INTELLIGENCE [19] Eliezer S. Yudkowsky
- AdaptiveAI [20]
- Project Joshua Blue [22] (Bootstrapping semantics in an autonomic computing system [21])
- Ai [23]
- SAIL (Self-organizing Autonomous Incremental Learner) [24]
- SHRUTI [25] “From Simple Associations to Systematic Reasoning”
- Overview of the Texai project [26] (Steve Reed)
- SOAR [27]
- ACT-R [28]
- OSCAR
Some video lectures (currently not well selected):
- IBM Research’s Almaden Institute Conference on Cognitive Computing [29]
- Model-based Bayesian RL [30] and related lectures from ICML-07 Bayesian RL Tutorial [31] (site [32])
Other links:
- NIPS 2005 workshop. Towards human-level AI? [33] (with slides)
- Essentials of General Intelligence: The direct path to AGI [34] by Peter Voss
- AGIRI [35] AGI Research Institute wiki
- Marcus Hutter [36] / Towards a Universal Theory of Artificial Intelligence based on Algorithmic Probability and Sequential Decisions [37]
- Juergen Schmidhuber [38] / The New AI: General & Sound & Relevant for Physics [39]
- Site by Włodzisław Duch [40]
- Confabulation Theory [41]
- Logica Universalis [42]
- The Emotion Machine [43]
Attic
Outdated plan:
- Information, distributions, programs, intelligence.
- Shannon information and Kolmogorov information, measures of complexity.
- Decision and control theory topics. Markov decision processes, reinforcement learning (Q-learning, SARSA etc.).
- SAIL and Dav: robots that learn “from scratch”.
- “General algorithmic intelligence” AIXI.
- Self improving programs: “Goedel Machine”. “Verificationist” program synthesis.
- Graphical probability models.
- Bayesian networks.
- Hierarchical Temporal Memory from Numenta.
- Introduction to “estimation of distribution” algorithms.
- “Optimizationist” competent program synthesis: algorithm MOSES.
- Representing and learning concepts. PAC-learnability.
- Learning grammars.
- Higher order and recursive structure representation induction.
- Logic in a dynamic world.
- Adaptive logics overview (circumscription, defeasible argumentation, belief revision, etc.) Intensional and term logics.
- From semantic networks to logic: system SNePS.
- Reasoning about probability and uncertainty.
- Game semantics for logics.
- Recursive probability models.
- Probabilistic term logic. “Two-dimensional” truth values: system NARS.
- “Probabilistic Logic Networks” in Novamente.
- Inductive probabilistic logic programming vel probabilistic logic learning.
- Cognitive loop (in search for the “main()” of the artificial mind).
- Inference system as an agent: goals and activations. (SNePS, NARS)
- Cognitive loop in LIDA.
- Concept formation and modeling of self. (Novamente)
- Theory of mental development.
- Piagetan psychology.
- Mental development of an AGI.