[[/public:t-720-atai:atai-22:main|DCS-T-720-ATAI-2022 Main]]


===== T-720-ATAI-2022 Readings & Study Material =====



[[/public:t-720-atai:atai-21:readings?#readings_readme|Readings README]] (Do not skip!)
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===== INTELLIGENCE: THE PHENOMENON =====
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====The Human Animal [ 9,10 ]====
//What is intelligence? \\ How do experts talk about it? \\ What has been uncovered? \\ What uniquely separates the phenomenon of intelligence from other similar phenomena in the world?//

  * [[http://en.wikipedia.org/wiki/General_intelligence_factor| The g factor]] on Wikipedia.
  * [[http://en.wikipedia.org/wiki/Theory_of_multiple_intelligences| Multiple theory of intelligence]] on Wikipedia.
  * [[https://arxiv.org/pdf/0706.3639.pdf|A Collection of Definitions of Intelligence]] by Legg & Hutter.
  * [[http://www.complex-systems.com/pdf/04-1-2.pdf|Evolution, Learning & Culture: Computational Metaphors for Adaptive Algorithms]] by R. K. Belew
  * [[http://consc.net/papers/computation.html|A Computational Foundation for the Study of Cognition]] by D. Chalmers
  * [[https://www.csee.umbc.edu/courses/471/papers/turing.pdf|Computing Machinery and Intelligence]] by A. M. Turing.
  * [[https://www.psychologytoday.com/intl/blog/consciousness-and-the-brain/201202/what-is-thought|What is Thought?]] by Ezequiel Morsella
  * [[https://en.wikipedia.org/wiki/Thought|Thought]] on Wikipedia.
  * [[https://en.wikipedia.org/wiki/Animal_cognition|Animal Cognition]] on Wikipedia.  
  * [[https://lexfridman.com/?powerpress_pinw=3920-podcast|Podcast interview with Jeff Hawkins]] by L. Friedman

====Other Kinds of Animals  ====
//Are animals other than humans also intelligent? \\
Note: You are encouraged to find other material on this topic (please let instructor know if you find interesting things). //

  * Parrots [ 3,3 ]
    * [[https://www.youtube.com/watch?v=dKvVaRlz0Y4|Alex the Parrot]] on YouTube (video repeats halfway, skip rest).  
    * [[https://en.wikipedia.org/wiki/Alex_(parrot)|Alex on Wikipedia]] 
    * [[https://www.youtube.com/watch?v=BG-0Bpe0J34|Parrots vs. Children]] BBC Earth on YouTube
  * Gorillas [ 1,2 ]
    * [[https://www.youtube.com/watch?v=SNuZ4OE6vCk|Koko the Gorilla]] on YouTube.  
    * [[https://en.wikipedia.org/wiki/Koko_(gorilla)|Koko on Wikipedia]] 
  * Crows [ 2,3 ]
    * [[https://www.youtube.com/watch?v=cbSu2PXOTOc|Crow solving an 8-step puzzle]] on YouTube
    * [[https://www.youtube.com/watch?v=ZerUbHmuY04|Crow demonstrates causal understanding]] on YouTube
    * [[https://www.youtube.com/watch?v=0fiAoqwsc9g|TED talk on crow intelligence]] by John Marzluff  
  * Bumblebees [ 1,2] 
      * [[https://www.youtube.com/watch?v=exsrX6qsKkA|Bumblebees learn by observation]] on YouTube. 
      * [[https://www.researchgate.net/publication/272748457_Information_transfer_beyond_the_waggle_dance_Observational_learning_in_bees_and_flies|Paper on Bumblebees learning by observation]] by Loukola et al.
  * Elephants [ 0,0 ]
    * [[https://apple.news/AQEvvY_wbRdqE1J-3MU_6WQ|Why Aren't Elephants Smarter Than Humans Since Their Brains Are Bigger?]] by Fabian van den Berg  


====Requirements for General Autonomous Intelligence [ 4,6 ] ====
//When engineers make an artifact, like a bridge or a space rocket, they start by listing the artifact's **requirements**. This way, for any proposed implementation, they can check their progress by comparing the performance of a prototype to these. The below papers consider what are necessary and sufficient requirements for a machine with **real** intelligence. (Therefore, these speak to defining what 'intelligent systems' in fact really means.)//

  * [[https://sciendo.com/article/10.2478/jagi-2019-0002|On Defining Artificial Intelligence]] by Pei Wang
  * [[http://www.cs.bham.ac.uk/research/projects/cogaff/Sloman.kd.pdf | Architectural Requirements for Human-like Agents Both Natural and Artificial]] by A. Sloman 
  * [[http://www.cs.bham.ac.uk/research/projects/cogaff/misc/fully-deliberative.html|Requirements for deliberative systems]] by A. Sloman -- key sections: from 8 onwards.
  * [[https://pdfs.semanticscholar.org/4688/e564f16662838938a2d729685baab068751b.pdf?_ga=2.196493103.2038245345.1566469635-1488191987.1566469635|Cognitive Architecture Requirements for Achieving AGI]] by J.E. Laird et al. 
  * [[http://www.isle.org/~langley/papers/final.arch.pdf| Cognitive architectures: Research issues and challenges.]] by Langley, P., Laird, J.E., Rogers, S. (2009). Cognitive Systems Research Vol 10(2), pp. 141-160.
  * [[http://alumni.media.mit.edu/~kris/ftp/agi-09-TransversalSkills-Thorisson-Nivel.pdf|Holistic Intelligence: Transversal Skills & Current Methodologies]] by K.R. Thórisson & E. Nivel. 
  * [[https://iccm-conference.neocities.org/2007/files/wray__lebiere__weinstein__jha__springer__belding__best____van_parunak.pdf|Towards a Complete, Multi-level Cognitive Architecture]] by R. Wray et al. 
  * [[http://cis-linux1.temple.edu/~pwang/Publication/AGI_Aspects.pdf|Introduction: Aspects of Artificial General Intelligence]] by P. Wang & B. Goerzel (first 3 sections)
  * [[https://ojs.aaai.org//index.php/aimagazine/article/view/2322|Mapping the Landscape of Human-Level Artificial General Intelligence]] by Adams et al. 



====AI: The Research Field====

//Artificial intelligence (AI) started as a research field. It still is. Just like research results in physics are useful for engineering, results in AI are useful for industry. AI is still in formation, much like computer science. It is a knowledge-generating enterprise funded by the public through universities and competitive research grants. Applications of AI are funded by companies and through various other means (including competitive grants for //applied research//). The knowledge generated in AI research is in part determined by the nature of the enterprise - how it's organized, who are the influencers, what are low-hanging fruit, etc.//

==== Part I: The Basics ==== 

  * [[http://www-formal.stanford.edu/jmc/whatisai/|What Is AI?]] by J. McCarthy.
  * [[http://www.iiim.is/2010/05/questions-about-artificial-intelligence/| Four Basic Questions about Artificial Intelligence]] by P. Wang.
  * [[http://cis-linux1.temple.edu/~pwang/Publication/AI_Misconceptions.pdf|Three Fundamental Misconceptions of Artificial Intelligence]] by P. Wang.

====Part II: GMI====

  * [[http://cadia.ru.is/wiki/public:t720-atai-2012:what_is_agi|What is GMI?]] by K. R. Thórisson.  
  * [[https://en.wikipedia.org/wiki/Artificial_general_intelligence| Artificial General Intelligence]] on Wikipedia.
  * [[https://aeon.co/essays/how-close-are-we-to-creating-artificial-intelligence| The very laws of physics imply that artificial intelligence must be possible. What's holding us up?]] by D. Deutch.
  * [[https://www.youtube.com/watch?v=lcUk1cYWY9I]] Can artificial intelligence become sentient, or smarter than we are - and then what? | Techtopia @ Deutsche Welle



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===== WORLDS, TASKS & ENVIRONMENTS  =====
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==== Worlds [ 4,4 ]====

  * {{/public:t-720-atai:draftofkrthorissonsforthcomingbookonintelligence.pdf|Earth Offers Great Variety}} by K.R. Thórisson
  * [[http://alumni.media.mit.edu/~kris/ftp/AGI16_task_theory.pdf|Why Artificial Intelligence Needs a Task Theory — And What It Might Look Like]] by K. R. Thórisson et al.
  * [[https://en.wikipedia.org/wiki/Laplace%27s_demon|Laplace's demon on Wikipedia]]
  * [[https://en.wikipedia.org/wiki/Maxwell%27s_demon|Maxwell's demon on Wikipedia]]
  * {{public:t-720-atai:szilard-1929-entropy-intelligent-beings.pdf|On the Decrease of Entropy in a Thermodynamic System by the Intervention of Intelligent Beings}} by L. Szilard

====Causation & Causal Relations ====
  * [[http://ftp.cs.ucla.edu/pub/stat_ser/r284-reprint.pdf|Bayesianism and Causality, or, Why I Am Only A Half-Bayesian]] by J. Pearl
  * [[http://alumni.media.mit.edu/~kris/ftp/AGI18__Cumulative_Learning_With_Causal_Relational_Models.pdf|Cumulative Learning With Causal-Relational Models]] by K.R.Thórisson et al. 


====Evaluation: Approaches, Tools, Techniques [ 3,5 ]====


  * [[http://alumni.media.mit.edu/~kris/ftp/EGPAI_2016_paper_9.pdf|Evaluation of General-Purpose Artificial Intelligence: Why, What & How]] by J. Bieger et al.
  * [[https://www.aaai.org/ojs/index.php/aimagazine/article/view/2748/2650|A New AI Evaluation Cosmos: Ready to Play the Game?]] by J. Hernandez-Orallo et al. 
  * [[http://alumni.media.mit.edu/~kris/ftp/AGIEvaluationFlexibleFramework-ThorissonEtAl2015.pdf|Towards Flexible Task-Environments For Comprehensive Evaluation of Artificial Intelligent Systems & Automatic Leaners]] by K. R. Thórisson et al.
  * [[http://alumni.media.mit.edu/~kris/ftp/Intricacy_and_Difficulty_AGI_2021.pdf|About the Intricacy of Tasks]] by Belanchia et al.
  * [[http://agi-conf.org/2010/wp-content/uploads/2009/06/paper_54.pdf|The Toy Box Problem]] by Johnston  
  * [[https://chatbotsmagazine.com/how-to-win-a-turing-test-the-loebner-prize-3ac2752250f1|Loebner Prize article]] by Charlie Moloney.
  * [[http://agi-conf.org/2020/wp-content/uploads/2020/06/AGI-20_paper_48.pdf|SAGE: Task-Environment Platform for Autonomy and Generality Evaluation]] by L. Eberding et al.
  * [[http://alumni.media.mit.edu/~kris/ftp/EGPAI_2016_paper_8.pdf|FraMoTEC: Modular Task-Environment Construction Framework for Evaluating Adaptive Control Systems]] by Thorarensen et al.
  * [[http://arxiv.org/pdf/1410.6142v3.pdf|The Lovelace Test]] by M. Reidl.
  * [[http://kryten.mm.rpi.edu/Bringsjord_Licato_PAGI_071512.pdf | Psychometric Artificial General Intelligence:  The Piaget-MacGuyver Room]] by Bringsjord, S. & Licato, J. In Theoretical Foundations of Artificial General Intelligence, edited by P. Wang and B. Goertzel (Atlantis Press).
  * [[http://www.atlantis-press.com/php/download_paper.php?id=1826 | AGI Preschool: A Framework for Evaluating Early-Stage Human-like AGIs]] by Goertzel, B. & Bugaj, S. V. Proceedings of the Second Conference on Artificial General Intelligence, Atlantis Press. 
  * [[http://webpages.math.luc.edu/~rgoebel1/publications/conferences/GoebelHespanhaTeelCaiSanfelice04NOLCOS.pdf|Hybrid Systems: Generalized Solutions & Robust Stability]] by R. Goebel et al.

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===== LEARNING  =====
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====Learning: General Overview [ 6,7 ]====

  * [[https://www.cs.ubc.ca/~murphyk/Bayes/pomdp.html|A Brief Intro to Reinforcement Learning]] by Kevin Murphy.
  * [[https://www.academia.edu/40043621/Cumulative_Learning|Cumulative Learning]] by K.R. Thórisson et al.
  * [[https://proceedings.mlr.press/v159/eberding22a/eberding22a.pdf|Comparison of Machine Learners
on an ABA Experiment Format of the Cart-Pole Task]] by Eberding et al.
  * [[http://alumni.media.mit.edu/~kris/ftp/AGI16_growing_recursive_self-improvers.pdf|Growing Recursive Self-Improvers]] by B. Steunebrink et al.
  * [[http://cis-linux1.temple.edu/~pwang/Publication/learning.pdf|The Logic of Learning]] by P. Wang. 
  * [[https://www.aaai.org/ojs/index.php/aimagazine/article/view/498/434|The Emergence of Artificial Intelligence: Learning to Learn]] by P. Bock.
  * [[https://en.wikipedia.org/wiki/Deep_learning|Deep learning on Wikipedia]] (Sections: Intro, Overview, and Neural Networks).
  * [[https://web.stanford.edu/class/psych209/Readings/SuttonBartoIPRLBook2ndEd.pdf|Reinforcement Learning: An Introduction]] by Rich Sutton and Andrew Barto (1998), //First chapter//. This is **"the"** introductory text on RL. 
    * //Alternative to Murphy:// [[http://www.gatsby.ucl.ac.uk/~dayan/papers/dw01.pdf|Reinforcement Learning in the Encyclopedia of Cognitive Science]] by P. Dayan and C. Watkins.

==== Self-Programming, Bootstrapping / Seed A(G)I / Seed Programming [ 2,4 ] ====

  * [[http://alumni.media.mit.edu/~kris/ftp/BoundedSeedAGI_agi14.pdf|Bounded Seed-AGI]] by E. Nivel et al.
  * [[http://arxiv.org/pdf/1502.06512.pdf|From Seed AI to Technological Singularity via Recursively Self-Improving Software]] by R. V. Yampolskiy.
  * [[http://cadia.ru.is/wiki/_media/creating_a_child_machine_-_raj_reddy.pdf|Creating a Child Machine: Reflections on Turing’s Proposal]] by R. Reddy.
  * [[http://alumni.media.mit.edu/~kris/ftp/agi-09-self-programming-Nivel-Thorisson.pdf|Self-Programming: Operationalizing Autonomy]] by E. Nivel & K.R. Thórisson. 
  * [[https://en.wikipedia.org/wiki/Von_Neumann_universal_constructor|Von Neumann Universal Constructor]] on Wikipedia.
  * [[http://alumni.media.mit.edu/~kris/ftp/nivel_thorisson_replicode_AGI13.pdf|Towards a Programming Paradigm for Control Systems With High Levels of Existential Autonomy]] by E. Nivel & K.R.Thórisson.
  * [[http://www.iiim.is/wp/wp-content/uploads/2011/05/wang-agisp-2011.pdf|Behavioral Self-Programming by Reasoning]] by P. Wang.
  * [[http://alumni.media.mit.edu/~kris/ftp/CumulativeLearning-ThorissonEtAl-AGI-2019.pdf|Cumulative Learning]] by K.R. Thórisson et al.
  * [[http://alumni.media.mit.edu/~kris/ftp/AGI16_growing_recursive_self-improvers.pdf|Growing Recursive Self-Improvers]] by B. Steunebrink et al.
  * [[http://www.aaai.org/Papers/Symposia/Fall/2007/FS-07-01/FS07-01-025.pdf | Self-awareness in Real-Time Cognitive Control Architectures]] by Sanz, R., López, I. & Hernández, C.


==== Artificial Pedagogy [ 2,4 ] ====

  * [[http://alumni.media.mit.edu/~kris/ftp/AGI17-pedagogical-pentagon.pdf|The Pedagogical Pentagon]] by J. Bieger et al.
  * [[http://alumni.media.mit.edu/~kris/ftp/BiegerEtAl-AGI2014-Raising_AI.pdf|Raising AI: Tutoring Matters]] by J. Bieger, K.R.Thórisson and D. Garrett.
  * [[http://pages.cs.wisc.edu/~jerryzhu/machineteaching/pub/MachineTeachingAAAI15.pdf|Machine teaching: An inverse problem to machine learning and an approach toward optimal education]] by X. Zhu
  * [[http://philsci-archive.pitt.edu/9085/1/SterrettBringingUpTuringsChild-Machine6April2012ForPSAArchive.pdf|Bringing up Turing's 'Child-Machine']] by S.G.Sterrett.
  * [[http://www.apa.org/pubs/journals/features/edu-a0037478.pdf|Matching Learning Style to Instructional Method: Effects on Comprehension]] by Rogowsky et al.
  * [[http://www.martin.zinkevich.org/publications/zilles11a.pdf|Models of cooperative teaching and learning]] by S. Zilles, S. Lange, R. Holte and M. Zinkevich.
  * [[http://aamas.csc.liv.ac.uk/Proceedings/aamas2013/docs/p1053.pdf|Teaching on a Budget: Agents Advising Agents in Reinforcement Learning]] by L. Torrey and M. E. Taylor.
  * [[http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.149.4701|Curriculum learning]] by Y. Bengio, J. Louradour, R. Collobert and J. Weston.



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===== METHODOLOGY & THEORY =====
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==== A(G)I Theories [ 2,3 ]===

  * {{/public:t-720-atai:peiwang_2019_ondefiningai_jagi.pdf|On Defining Artificial Intelligence, Journal of Artificial General Intelligence 10(2):1-37}} by Pei Wang.
  * [[http://act-r.psy.cmu.edu/wordpress/wp-content/uploads/2012/12/526FSQUERY.pdf| An integrated theory of mind]] by Anderson et al.
  * [[http://act-r.psy.cmu.edu/wordpress/wp-content/uploads/2012/12/526FSQUERY.pdf|(ACT-R) An Integrated Theory of the Mind]] by Anderson, J. R.; Bothell, D.; Byrne, M.D.; Douglass, S.; Lebiere, C. & Qin, Y.
  * [[http://arxiv.org/abs/0712.3329|Universal Intelligence: A Definition of Machine Intelligence]] by Shane Legg and Marcus Hutter.
  * [[http://alumni.media.mit.edu/%7Ekris/ftp/IJAAI.pdf|A Mind Model for Multimodal Communicative Creatures and Humanoids]] by Thórisson, K. R. 



====Part I: GOFAI Approaches====
  * [[http://alumni.media.mit.edu/%7Ekris/ftp/AIMag-CDM-ThorissonEtAl04.pdf| Constructionist Design Methdology]] paper by K.R. Thórisson.
  * [[https://en.wikipedia.org/wiki/Subsumption_architecture|Subsumption Architecture]] on Wikipedia.
    * //(supplementary)// [[http://people.csail.mit.edu/brooks/papers/how-to-build.pdf|How to Build Complete Creatures Rather than Isolated Cognitive Simulators]] by Rodney Brooks.
    * //(supplementary)// [[ftp://publications.ai.mit.edu/ai-publications/pdf/AIM-864.pdf|A Robust Layered Control System for a Mobile Robot]] by R. Brooks.  
  * {{:public:archmismatch-icse17.pdf| Architectural Mismatch or Why it’s hard to build systems out of existing parts}} by Garlan, D., R. Allen and J. Ockerbloom. 
    * Also available [[http://www.cs.cmu.edu/afs/cs/project/able/ftp/archmismatch-icse17/archmismatch-icse17.pdf|here]].
  * [[https://en.wikipedia.org/wiki/Belief%E2%80%93desire%E2%80%93intention_software_model|BDI Architecture]] on Wikipedia.
  * [[https://www.aaai.org/Papers/ICMAS/1995/ICMAS95-042.pdf|BDI Agents: From Theory to Practice]] by A.S. Rao & M.P. Georgeff.
  * [[http://www.artificialhumancompanions.com/robot-mind-robot-body-whatever-happened-subsumption-architecture/|Whatever happened to the subsumption architecture?]] by Simon Birrell.


====Part II: GMI Methodology [ 5,8 ] ====
  * [[http://alumni.media.mit.edu/~kris/ftp/Thorisson_chapt9_TFofAGI_Wang_Goertzel_2012.pdf|A New Constructivist AI: From Manual Construction to Self-Constructive Systems]] by K.R. Thórisson
  * {{:public:intro_to_software_arch.pdf| Introduction to Software Architecture}} by Garlan & Shaw.
  * [[https://drive.google.com/file/d/1pNbcrEuFMCBTGPVwKtld04t5Dz5qZsKd/view?usp=sharing
  * [[http://alumni.media.mit.edu/~kris/ftp/Thorisson-ReductioAdAbsurdum-AGI2013.pdf|Reductio ad Absurdum: On Oversimplification in Computer Science & its Pernicious Effect on Artificial Intelligence Research]] by K.R. Thórisson.
  * [[/public:t_720_atai:atai-20:ConstructivistAI|Constructivist AI Methodology]] by K.R. Thórisson
  * {{public:i-700-abms-07-1:cantherebeascienceofcomplexity-simon-iccsproc.pdf| Can there be a science of complex systems?}} by H. A. Simon
  * [[https://pdfs.semanticscholar.org/6071/193acf08170df3226b972dadf0c1f2365ba3.pdf|A Primer For Conant & Ashby's “Good-Regulator Theorem”]] by D.L. Scholten
  * [[http://cleamc11.vub.ac.be/books/Conant_Ashby.pdf|Every Good Regulator of a System Must be a Model of that System]] by Conant & Ashby
  * [[http://alumni.media.mit.edu/~kris/ftp/JAGI-Special-Self-Progr-Editorial-ThorissonEtAl-09.pdf|Approaches & Assumptions of Self-Programming in Achieving Artificial General Intelligence]] by K.R. Thórisson et al.
  * [[https://www.kurzweilai.net/essentials-of-general-intelligence-the-direct-path-to-agi|Essentials of General Intelligence: The Path Towards AGI]] by P. Voss. 
  * [[http://pespmc1.vub.ac.be/Papers/Cybernetics-EPST.pdf|Cybernetics and Second-Order Cybernetics]] by Heylighen & Joslyn
  * [[http://www.iiim.is/2010/05/agi-cognitive-synergy-goertzel/| Does the Future of AGI Lie in Cognitive Synergy?]] by B. Goertzel
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===== CONTROL & SYSTEMS =====

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==== Control & Systems [ 4,6 ] ====
  * {{/public:t-720-atai:control_theory.pdf|Introduction to Control Theory}}
  * [[http://journals.isss.org/index.php/proceedings53rd/article/viewFile/1249/410|Robert Rosen’s Anticipatory Systems Theory: The Art and Science of Thinking Ahead]] by J. Rosen.
  * [[https://en.wikipedia.org/wiki/Hybrid_system|Hybrid systems]] on Wikipedia.
  * [[http://research.microsoft.com/en-us/um/people/lamport/pubs/time-clocks.pdf|Time, Clocks, and the Ordering of Events in a Distributed System]] by L. Lamport. 
  * [[http://cadia.ru.is/wiki/_media/public:t-720-atai:goebeletal-hybriddynamicalsystems-2009.pdf|Hybrid Dynamical Systems]] by R. Goebel et al.
  * [[http://pub.ist.ac.at/~tah/Publications/the_theory_of_hybrid_automata.pdf|Theory of Hybrid Automata]] by T. A. Henzinger. 
  * [[http://pespmc1.vub.ac.be/Papers/Cybernetics-EPST.pdf|Cybernetics and Second-Order Cybernetics]] by Heylighen & Joslyn.
  * {{/public:t-720-atai:bellman.pdf|Control Theory}} by Bellman.

==== Models ====
  * {{public:t-720-atai:a_primer_for_conant_and_ashby_s_good-regulator_theorem.pdf|A Primer For Conant & Ashby's “Good-Regulator Theorem”}} by Daniel L. Scholten.
  * [[http://cleamc11.vub.ac.be/books/Conant_Ashby.pdf|Every Good Regulator of a System Must be a Model of that System]] by Conant & Ashby.
  * [[http://alumni.media.mit.edu/~kris/ftp/AGI18__Cumulative_Learning_With_Causal_Relational_Models.pdf|Cumulative Learning With Causal Relational Models]] by Thórisson & Talbot.


==== Generality ====

  * [[http://cadia.ru.is/wiki/public:t720-atai-2012:what_is_agi|What is GMI?]] by K. R. Thórisson.  
  * [[http://alumni.media.mit.edu/~kris/ftp/Seed-Programmed-General-Learning-Thorisson-PMLR-2020.pdf|Seed-Programmed Autonomous General Learning]] (sections 1 and 2) by K.R. Thórisson

==== Autonomy [ 3,3 ] ====

  * [[http://tierra.aslab.upm.es/~sanz/old/docs/ISIC-2000.pdf|Fridges, Elephants, and the Meaning of Autonomy and Intelligence]] by R. Sanz et al.
  * [[http://alumni.media.mit.edu/~kris/ftp/AutonomyCogArchReview-ThorissonHelgason-JAGI-2012.pdf|Cognitive Architectures & Autonomy: A Comparative Review]] by K.R. Thórisson & H.P. Helgason.
  * [[http://arxiv.org/pdf/cs/0002009.pdf|Syntactic Autonomy - Or Why There is no Autonomy Without Symbols and how Self-Organizing Systems Might Evolve Them]] by L. M. Rocha.
  * [[https://www.aaai.org/ojs/index.php/aimagazine/article/view/498/434|The Emergence of Artificial Intelligence: Learning to Learn]] by P. Bock.
  * [[https://www.researchgate.net/profile/Elena_Messina/publication/228630874_A_framework_for_autonomy_levels_for_unmanned_systems_(ALFUS)/links/0c960517a990b246b1000000.pdf|A Framework for Autonomy Levels for Unmanned Systems (ALFUS)]] by Huang et al.



==== Resource Management: Attention, Self-Control, Integrated Cognitive Control [ 3,5 ] ====

  * [[http://cogprints.org/5941/1/ASLAB-A-2007-011.pdf|Principles of Integrated Cognitive Control]] by R. Sanz et al.
  * [[http://alumni.media.mit.edu/~kris/ftp/Helgason-AGI2012-AwardEdition-Final.pdf|On Attention Mechanisms for AGI Architectures: A Design Proposal]] by Helgason et al.; accompanying video can be found [[https://www.youtube.com/watch?v=-EKO5u86DYM|here]].
  * [[http://web.mit.edu/torralba/www/josa.pdf| A model of attention that takes global scene factors into account]] by Torralba. 
  * [[http://alumni.media.mit.edu/~kris/ftp/nivel_thorisson_replicode_AGI13.pdf| Towards a Programming Paradigm for Control Systems with High Levels of Existential Autonomy]] by E. Nivel et al. 
  * [[http://alumni.media.mit.edu/~kris/ftp/Helgason%20et%20al-AGI2013.pdf|Predictive Heuristics for Decision-Making in Real-World Environments]] by H. Helgason et al.
  * [[http://alumni.media.mit.edu/~kris/ftp/HelgasonEtAl-2014-Attention-IJCSAI10339-20140314-163624-3675-40677.pdf|Towards a General Attention Mechanism for
Embedded Intelligent Systems]] by H. P. Helgason et al. 
  * [[http://alumni.media.mit.edu/~kris/ftp/Helgason-Thorisson-AttentionForAI.pdf|Attention Capabilities for AI Systems]] by H. P. Helgason & K. R. Thórisson.



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===== UNDERSTANDING & KNOWLEDGE REPRESENTATION =====
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==== Symbols & Meaning [3,5] ====

  * [[http://ai.stanford.edu/~nilsson/OnlinePubs-Nils/PublishedPapers/pssh.pdf|The Physical Symbol System Hypothesis: Status & Prospects]] by N. J. Nilsson.
  * [[http://cogprints.org/7150/1/10.1.1.83.5248.pdf|Minds, Brains & Programs]] by John Searle
  * [[http://consc.net/papers/subsymbolic.pdf|Subsymbolic Computation and the Chinese Room]] by D. Chalmers
  * [[http://www.informatics.indiana.edu/rocha/publications/pattee/pattee.html|The Physics of Symbols: Bridging the Epistemic Cut]] by H. H. Pattee
  * [[https://www.academia.edu/11263800/The_Cybernetic_Cut_Progressing_from_Description_to_Prescription_in_Systems_Theory|The 'Cybernetic Cut': Progressing from Description to Prescription in Systems Theory]] by D. L. Abel

==== Semantic & Operational Closure [ 1,2 ] ====

  * [[http://pespmc1.vub.ac.be/Papers/Cybernetics-EPST.pdf|Cybernetics and Second-Order Cybernetics]] by Heylighen & Joslyn
  * [[https://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=&cad=rja&uact=8&ved=2ahUKEwj1gdit6qDsAhXJ5-AKHZU3B8YQFjAAegQIARAC&url=http%3A%2F%2Fwww.academia.edu%2F863857%2FCell_psychology_an_evolutionary_approach_to_the_symbol-matter_problem%3Fauto%3Ddownload&usg=AOvVaw0RFBI0LxmOtGrKvE8u6Adz|Cell psychology: An evolutionary approach to the symbol-matter problem]] by H. Pattee


====About Understanding [ 3,5 ]====
  * [[http://alumni.media.mit.edu/~kris/ftp/AGI16_understanding.pdf|About Understanding]] by K. R. Thorisson et al.
  * [[http://alumni.media.mit.edu/~kris/ftp/IJCAI17-EGPAI-EvaluatingUnderstanding.pdf|Evaluating Understanding]] by K.R. Thórisson & J. Bieger
  * [[https://proceedings.mlr.press/v159/thorisson22b/thorisson22b.pdf|The ‘Explanation Hypothesis’
in General Self-Supervised Learning]]  by K.R. Thórisson
  * [[http://alumni.media.mit.edu/~kris/ftp/AGI17-UUW-DoMachinesUnderstand.pdf|Do Machines Understand? A Short Review of Understanding & Common Sense in Artificial Intelligence]]  by K.R. Thórisson & D. Kremelberg
  * [[http://alumni.media.mit.edu/~kris/ftp/AGI17_Understanding&CommonSense.pdf|Understanding & Common Sense: Two Sides of the Same Coin?]] by K. R. Thórisson & D. Kremelberg
  * [[https://www.researchgate.net/publication/359625587_Understanding_is_a_process|Understanding is a Process]] by Blaha et al.


==== Reasoning  [ 4,5 ]====

  * [[http://cis-linux1.temple.edu/~pwang/Publication/learning.pdf|The Logic of Learning]] by P. Wang. 
  * [[https://cis.temple.edu/~pwang/Publication/induction.pdf|A New Approach for Induction: From a Non-Axiomatic Logical Point of View]] by Pei Wang
  * [[http://alumni.media.mit.edu/~kris/ftp/AEGAP18_Abduction_Deduction_Causal_Relational_Models.pdf|Abduction & Deduction With Causal-Relational Models]] by K.R. Thórisson & A. Talbot
  * [[https://philosophynow.org/issues/106/Critical_Reasoning|Critical Reasoning]] by Marianne Talbot
  * [[http://cis-linux1.temple.edu/~pwang/Publication/evidence.pdf|Wason's Cards: What is Wrong?]] by P. Wang
  * [[https://pdfs.semanticscholar.org/7ca4/09b6b2569f7420d5ff481556c1c06e3e5b73.pdf|Return to Term Logic]] by Pei Wang
  * [[https://cis.temple.edu/~pwang/Publication/abduction.pdf|Abduction in Non-Axiomatic Logic]] by Pei Wang



==== Situatedness, Embodiment ====

  * [[http://pespmc1.vub.ac.be/riegler/papers/riegler02embodiment.pdf| When is a cognitive system embodied?]] by Riegler
  * [[http://cis-linux1.temple.edu/~pwang/Publication/embodiment.pdf|Does a Laptop Have a Body?]] by P. Wang
  * [[http://cogprints.org/2517/1/Lindblom.pdf| Social situatedness]] by Lindblom & Ziemke
  * [[http://alumni.media.mit.edu/~kris/ftp/nivel_thorisson_replicode_AGI13.pdf|Towards a Programming Paradigm for Control Systems With High Levels of Existential Autonomy]] by E. Nivel & K. R. Thórisson

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=====IMPLEMENTED AGI-ASPIRING SYSTEMS=====
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==== NARS [ 4,5 ] ====

  * [[http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.134.4298&rep=rep1&type=pdf|From NARS to a thinking machine]] by P. Wang
  * [[public:t720-atai-2012:nars|What is NARS]] by K. R. Thórisson
  * [[https://cis.temple.edu/~pwang/NARS-Intro.html| Introduction to NARS]] by P. Wang
  * [[http://www.cis.temple.edu/~wangp/Publication/unifiedAI.pdf| NARS intro paper]] by P. Wang
  * [[http://www.cis.temple.edu/~wangp/demos.html| NARS demos]] by P. Wang


==== AERA [ 3,5 ]====

  * [[http://alumni.media.mit.edu/~kris/ftp/PeeweeGranularity-Thorisson-Nivel-09.pdf|Achieving Artificial General Intelligence Through Peewee Granularity]] by Thórisson, K. R. & Nivel, E.
  * [[http://alumni.media.mit.edu/~kris/ftp/agi-09-self-programming-Nivel-Thorisson.pdf|Self-Programming: Operationalizing Autonomy]] by Nivel, E. & K. R. Thórisson
  * [[http://alumni.media.mit.edu/~kris/ftp/BoundedSeedAGI_agi14.pdf|Bounded Seed-AGI]] by E. Nivel et al.
  * [[http://alumni.media.mit.edu/%7Ekris/ftp/AAoNL-wHeadr.pdf|Autonomous Acquisition of Natural Communication]] by K. R. Thórisson et al.
  * [[http://alumni.media.mit.edu/~kris/ftp/AnytimeBoundedRationalityagi15_nivelEtAl.pdf|Anytime Bounded Rationality]] by E. Nivel et al.
  * [[http://arxiv.org/pdf/1312.6764v1.pdf|Bounded Recursive Self-Improvement]] by E. Nivel et al.


====  Drescher's Constructivist Schema System ====

  * [[http://cadia.ru.is/wiki/_media/public:t-720-atai:dvhw-made-up-minds-drescher-1991.pdf|Made-Up Minds: A Constructivist Approach to Artificial Intelligence]] by G. Drescher 
    * Read chapters 1 & 2 for understanding Drescher's (and Piaget's) psychological motivation; Ch. 3 & 4 for an overview of the computational framework.
==== Sigma [ 0,1 ] ====

  * [[http://cs.usc.edu/~rosenblo/Pubs/Sigma%20AISBQ%20D.pdf|The Sigma Cognitive Architecture and System]] by P. S. Rosenbloom


==== Open Cog [ 0,2 ] ====

  * [[http://goertzel.org/dynapsyc/2009/OpenCogPrime.pdf|OPENCOG PRIME: A COGNITIVE SYNERGY BASED ARCHITECTURE FOR ARTIFICIAL GENERAL INTELLIGENCE]] by Ben Goertzel
  * [[http://wiki.opencog.org/w/Getting_Started|Getting Started with Open Cog]] by Ben Goertzel

==== Other Such Systems [ 0,3 ] ====

  * 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. [[http://goertzel.org/agiri06/%5B4%5D%20StanFranklin.pdf | 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 [[http://www.lrdc.pitt.edu/schunn/research/papers/nomagicbullets.pdf || 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 [[http://shelf2.library.cmu.edu/Tech/16128161.pdf | 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 [[http://ccrg.cs.memphis.edu/assets/papers/2011/The%20LIDA%20Framework%20as%20a%20General%20Tool%20for%20AGI%20final.pdf | PDF]]
  * J. Schmidhuber (2016). [[https://mainatnips.github.io/mainatnips.github.io/slides/schmidhuber-rsi2016white.pdf|Learning how to Learn Learning Algorithms: Recursive Self-Improvement]]. - slides


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=====FOUNDATIONAL TOPICS=====
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=== Prerequisites ===

  * [[http://www.iep.utm.edu/chineser/|Searle's Chinese Room Argument]] on the Internet Encyclopedia of Philosophy
  * [[https://en.wikipedia.org/wiki/Chinese_room|The Chinese Room]] on Wikipedia


=== Self-Organization & Emergence [ 1,2 ] ===

  * [[http://web.stanford.edu/dept/HPS/WritingScience/RochaSelfOrganisation.pdf|Selected Self-Organization and the Semiotics of Evolutionary Systems]] by L. M. Rocha
  * [[http://people.reed.edu/~mab/publications/papers/weak-emergence.pdf|Weak Emergence]] by M. A. Bedau
  * [[http://consc.net/papers/emergence.pdf|Strong and Weak Emergence]] by D. Chalmers 
  * [[http://cadia.ru.is/wiki/_media/emergence-causation:eolss-scienceof-self-organiz.pdf|THE SCIENCE OF SELF-ORGANIZATION AND ADAPTIVITY]] by Heylighen


=== (Phenomenal) Consciousness [ 0,2 ] ===

  * [[http://organizations.utep.edu/portals/1475/nagel_bat.pdf|What's it Like to be a Bat?]] by T. Nagel
  * [[http://consc.net/papers/nature.pdf|Consciousness and its Place in Nature]] by D. J. Chalmers
  * [[https://www.youtube.com/watch?v=1d5RetvkkuQ|The Future of Consciousness]] - TEDx lecture by R. Hameroff
  * [[https://www.youtube.com/watch?v=GzCvlFRISIM|Consciousness is a Mathematical Pattern]] - TEDx lecture by M. Tegmark
  * [[https://www.youtube.com/watch?v=j_OPQgPIdKg|Consciousness and the Brain]] - TEDx lecture by J. Searle

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=====Societal Impact & Ethics [ 0,2 ] =====
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  * [[http://mobile.abc.net.au/news/2018-09-18/china-social-credit-a-model-citizen-in-a-digital-dictatorship/10200278?pfmredir=sm|Leave no dark corner]] by Matthew Carney.
  * [[https://www.oxfordmartin.ox.ac.uk/downloads/academic/The_Future_of_Employment.pdf|THE FUTURE OF EMPLOYMENT: HOW SUSCEPTIBLE ARE JOBS TO COMPUTERISATION?]] by Frey and Osborne

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===== Additional Readings & Study Material ======

=== Reinforcement Learning [ 0,2 ] ===

  * [[https://webdocs.cs.ualberta.ca/~sutton/book/the-book.html|Reinforcement Learning: An Introduction]] by Rich Sutton and Andrew Barto (1998) is //the// introductory text on RL. 
  * [[http://www.vetta.org/documents/Machine_Super_Intelligence.pdf|Machine Super Intelligence]] is Shane Legg's 2008 PhD thesis. While it is not on reinforcement learning, it does connect the concepts of RL to AI, what he calls "universal AI". 
  * [[https://www.udacity.com/course/reinforcement-learning--ud600|Reinforcement Learning]] is a full Udacity course on RL from Georgia Tech.
  * [[https://www.youtube.com/playlist?list=PLf6J8du4ey8CL0pUxbLQpdbbtDcc2M1bI|A Short Course on Reinforcement Learning]] by S. Singh at MLSS'11 introduces RL and discusses some important shortcomings and proposed first steps to solving them.
  * [[http://www0.cs.ucl.ac.uk/staff/d.silver/web/Teaching.html|Advanced Topics: RL]] by D. Silver is a more in-depth, modern RL course from one of the people who worked on Google DeepMind's Atari playing system that received a lot of (media) attention. 
  * [[http://videolectures.net/nips09_littman_mbrl/|Model-Based Reinforcement Learning]] is a tutorial given by Michael Littman at NeurIPS'09 about model-based RL, which is a lot less common than model-free RL, but not less interesting.
  * [[http://link.springer.com/chapter/10.1007/978-3-642-39521-5_13#page-1|Resource-Bounded Machines are Motivated to be Effective, Efficient & Curious]] by B. Steunebrink

=== Deep Learning [ 0,1 ]===

  * [[https://www.codementor.io/james_aka_yale/a-gentle-introduction-to-neural-networks-for-machine-learning-hkijvz7lp|A gentle introduction to neural networks]] by J. Le - gives a good overview of the different approaches
  * [[https://garymarcus.substack.com/p/form-function-and-the-giant-gulf|Form, function, and the giant gulf between drawing a picture and understanding the world]] by G. Marcus

=== Other [ 0,3 ] ===

  * [[https://www.aaai.org/ocs/index.php/FSS/FSS15/paper/view/11702/11476| Kognit: Intelligent Cognitive Enhancement Technology by Cognitive Models and Mixed Reality for Dementia Patients]] by D. Sonntag and [[https://vimeo.com/132704158|accompanying video]]
  * [[http://bicasociety.org/cogarch/architectures.htm| Comparison table of cognitive architectures]], courtesy of BICA Society / A. Samsonovich
  * [[http://qz.com/627989/why-are-so-many-smart-people-such-idiots-about-philosophy/|Why are so many smart people such idiots about philosophy?]] by O. Goldhill
  * [[http://alumni.media.mit.edu/~kris/ftp/CognitiveMapIEEE-09.pdf|Cognitive Map Architecture: Facilitation of Human-Robot Interaction in Humanoid Robots]] by V. Ng-Thow-Hing et al. (2009). 
  * [[http://www.aaai.org/Papers/Symposia/Fall/2004/FS-04-01/FS04-01-014.pdf | Toward a unified artificial intelligence]] by Wang, P. 
  * [[https://www.youtube.com/watch?v=8nHVUFqI0zk|Judea Pearl lecture on Causation]] on YouTube
  * [[https://www.willamette.edu/~gorr/classes/ids101/links/nutshell.pdf|Creativity in a Nutshell]] by M. Boden.
  * [[http://www.ppi-int.com/files/role-of-cse-in-se.pdf| The Role of Cognitive Systems Engineering in the Systems Engineering Design Process]] by Militello, L.G., Dominguez, C.O., Lintern, G. & Klein, G. (2010). In Systems Engineering, Vol 13(3), pp. 261-273. 
  * [[http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.158.4126&rep=rep1&type=pdf| A survey on transfer learning]] by Pan, S. J. & Yang, Q. (2011). IEEE Transactions on Knowledge and Data Engineering, 22(10), pp. 1345–1359.
  * [[http://tierra.aslab.upm.es/documents/controlled/ASLAB-A-2009-016.pdf | Systems, models and self-awareness: Towards architectural models of consciousness]] by Sanz, R., Hernandez, C., Gomez, J., Bermejo-Alonso, J., Rodriguez, M., Hernando, A. & Sanchez, G. (2009). International Journal Of Machine Consciousness, 1(2), pp.255–279. 
  * [[http://wiki.humanobs.org/_media/public:events:agi-summerschool-2012:silverpaper_reqforml3.pdf| Requirements for Machine Lifelong Learning]] by Silver, D.L. & Poirier, R. (2007). IWINAC, LNCS (4527), pp.313-319. 
  * [[http://www.lehigh.edu/~mhb0/physicalemergence.pdf| Physicalism, Emergence and Downward Causation]] by Campbell and Bickhard
  * [[http://agi-school.org/2009/dr-joscha-bach-man-as-machine|Man as Machine]] - J. Bach's AGI intro lecture at AGI Summer School 2009
  * {{:public:t-720-atai:atai-16:silver2016.pdf|Mastering the game of Go with deep neural networks and tree search}} by Silver, Huang et al. (DeepMind)
  * [[http://www.scottaaronson.com/papers/philos.pdf|Why Philosophers Should Care About Computational Complexity]] by S. Aaronsson
  * [[http://www-verimag.imag.fr/~sifakis/RecentPublications/2011/AVisionforCS.pdf|A Vision for Computer Science - the System Perspective]] by J. Sifakis



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===== Other Sources ======

  * [[http://www.degruyter.com/view/j/jagi| Journal of Artificial General Intelligence]]
  * [[http://aima.eecs.berkeley.edu/~russell/intro.html|Introduction to Artificial Intelligence - A Modern Approach]] by S. Russell & P. Norvig
  * [[http://philosophy.wisc.edu/forster/520/Chapter%201.pdf|Chapter 1: An Introduction to Philosophy of Science]] by Malcolm Forster

====Reinforcement Learning====

  * [[http://www.ualberta.ca/~szepesva/RLBook.html|Algorithms for Reinforcement Learning]] by Csaba Szepesvári (2010) is a much more recent, shorter book that discusses the strengths and weaknesses of various RL algorithms. See also: [[http://incompleteideas.net/sutton/RL-FAQ.html|Rich Sutton's FAQ]].
  * [[https://en.wikipedia.org/wiki/Deep_learning|Deep learning on Wikipedia]] (Sections: Intro, Overview, and Neural Networks).

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=====Readings README=====
**How to Use This Page**
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//Note: DO NOT SKIP READING THE BELOW TEXT//

Papers under each section are ordered from most to least important, so start counting from the top.

[ x,y ] \\
x: necessary mandatory number of papers to be read -- absolute minimum number. \\
y: the recommended number. \\
No number: Read all the papers listed. 

It is your responsibility to ensure that you grasp the concepts covered; the readings are //my top choices// for getting this done. However, if you are aware of alternative sources of treatment of the concepts covered in these you may prefer to read about them from your preferred source. If in doubt, ask me. 

You are expected to read a lot of papers in this course, **at least** 3-4 papers per week (5 recommended). //Keep at it and you'll be fine!// 

Assigned readings should be read //before// class. \\
If you do so you will already have some familiarity with the subject matter, which not only means you will //remember it better// but also that you can //ask questions for clarification during the lecture// and partially //steer its direction//. 

Reading the papers after class //is less effective//. 

Warning: Do not attempt to read them during class - this is absolutely the worst way to cover this material (but of course you may have it open for reference). 

Reading the assigned readings not at all should generally be avoided.

\\


**As you read papers from each of the following categories I want you ask yourself a few questions:** 

  * For each paper in each category X, ask yourself:
    * What is X? 
    * How does the human mind do X? 
    * Do current computers do X?
    * ...and ...
    * Do we need (to replicate or capture) what the human mind does to achieve X to create a machine that rivals the ability of humans to do X? 

If you can answer them satisfactorily when you're done reading you're good! Even if you can't you'll be fine if you: Write down the discrepancies and //bring them to class in the form of questions//. There is no such thing as a 'stupid question' when you're learning something new. 

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//EOF//