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T-720-ATAI-2016 Main

T-720-ATAI-2016 Readings & Study Material

This material is ordered (roughly) by specificity, starting with the general topic of what intelligence is, progressing towards good old-fashioned AI (aka constructionist AI) and then onward towards artificial general intelligence. Within each topic the papers are ordered by importance, the most important first.

The recommended minimum number of papers to be read in each category is listed in brackets [ a,b ] after the title, where a refers to the necessary mandatory number of papers to be read, and b refers to the recommended absolute minimum number (papers are ordered from most to least important, so start counting from the top). This means you are expected to read at the very least around 30 papers in this course, so that comes out to at least 2-3 papers per week. Keep at it and you'll be fine! (Slack and you will reap the consequences.)

Note: Assigned readings should be read before class. Alternatively, as a less desirable alternative, readings may be read after class. Reading the assigned readings not at all should generally be avoided.


Artificial Intelligence

Constructionist Systems & Methodology

Introductory Material Constructionist AI
Reinforcement Learning

[ 1,1 ]

Limitations of Constructionist AI

Artificial General Intelligence

Overview / Requirements
Metacognition / Integrated Cognitive Control

[ 2,2 ]

Universal Pedagogy

[ 2,3 ]


[ 1,2 ]

Seed AI
AGI Methodology

AGI-Aspiring Systems


[ 1,2 ]


[ 1,2 ]


[ 1,1 ]

Open Cog


  • Franklin, S. (2007). (LIDA) A Foundational Architecture for Artificial General Intelligence. Advances in Artificial General Intelligence: Concepts, Architectures and Algorithms. IOS Press, Amsterdam, The Netherlands, The Netherlands, pp. 36-54. PDF
  • Anderson, J.R. & Schunn, C.D. (2000). Implications of the ACT-R learning theory: No magic bullets. Advances in instructional psychology. 5:1-34. Lawrence Erlbaum | PDF
  • Laird, J.E.; Newell, A. & Rosenbloom, P.S. (1987). SOAR: An architecture for general intelligence. Artificial Intelligence, Volume 33, Issue 1, Pages 1-64 PDF
  • Snaider, J; McCall, R. & Franklin, S. (2011). The LIDA framework as a general tool for AGI. Artificial General Intelligence, Lecture Notes in Computer Science. 2011. Volume 6830/2011. pp. 133-142 PDF

Evaluation: Worlds, Tasks, Environments

[ 6,8 ]


[ 2,3 ]

Situatedness, Embodiment

Philosophical Topics

Symbols & Meaning

Self-Organization & Emergence



Additional Readings & Study Material


Additional Sources

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