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ORCID14th AGI 2021: Palo Alto, CA, USA
- Ben Goertzel
, Matthew Iklé, Alexey Potapov:
Artificial General Intelligence - 14th International Conference, AGI 2021, Palo Alto, CA, USA, October 15-18, 2021, Proceedings. Lecture Notes in Computer Science 13154, Springer 2022, ISBN 978-3-030-93757-7 - Samuel Allen Alexander, Marcus Hutter:
Reward-Punishment Symmetric Universal Intelligence. 1-10 - Mohammadreza Alidoust:
AGI Brain II: The Upgraded Version with Increased Versatility Index. 11-18 - Matteo Belenchia, Kristinn R. Thórisson, Leonard M. Eberding, Arash Sheikhlar:
Elements of Task Theory. 19-29 - Michael Timothy Bennett:
Symbol Emergence and the Solutions to Any Task. 30-40 - Michael Timothy Bennett:
Compression, The Fermi Paradox and Artificial Super-Intelligence. 41-44 - Michael Timothy Bennett, Yoshihiro Maruyama:
The Artificial Scientist: Logicist, Emergentist, and Universalist Approaches to Artificial General Intelligence. 45-54 - Tyler Cody:
Mesarovician Abstract Learning Systems. 55-64 - Leonard M. Eberding, Matteo Belenchia, Arash Sheikhlar, Kristinn R. Thórisson:
About the Intricacy of Tasks. 65-74 - Arthur Franz:
Experiments on the Generalization of Machine Learning Algorithms. 75-85 - Robert John Freeman:
Parsing Using a Grammar of Word Association Vectors. 86-95 - Christian Hahm, Boyang Xu, Pei Wang:
Goal Generation and Management in NARS. 96-105 - Anton Kolonin:
Neuro-Symbolic Architecture for Experiential Learning in Discrete and Functional Environments. 106-115 - Per Roald Leikanger:
Navigating Conceptual Space; A New Take on AGI. 116-126 - Yoshihiro Maruyama:
Categorical Artificial Intelligence: The Integration of Symbolic and Statistical AI for Verifiable, Ethical, and Trustworthy AI. 127-138 - Yoshihiro Maruyama:
Moral Philosophy of Artificial General Intelligence: Agency and Responsibility. 139-150 - Michael S. P. Miller:
The Piagetian Modeler. 151-162 - C. S. Munford:
Epistolution: How a Systems View of Biology May Explain General Intelligence. 163-173 - Eray Özkural:
Measures of Intelligence, Perception and Intelligent Agents. 174-183 - Alexey Potapov, Vitaly Bogdanov:
Univalent Foundations of AGI are (not) All You Need. 184-195 - Saty Raghavachary:
Biological Intelligence Considered in Terms of Physical Structures and Phenomena. 196-203 - Ali Raheman, Anton Kolonin, Ikram Ansari:
Adaptive Multi-strategy Market Making Agent. 204-209 - Vignav Ramesh, Anton Kolonin:
Unsupervised Context-Driven Question Answering Based on Link Grammar. 210-220 - Alexei V. Samsonovich:
A Virtual Actor Behavior Model Based on Emotional Biologically Inspired Cognitive Architecture. 221-227 - Arash Sheikhlar, Leonard M. Eberding, Kristinn R. Thórisson:
Causal Generalization in Autonomous Learning Controllers. 228-238 - Vladimir Smolin:
AI Future: From Internal Vectors to Simple Objects States Subspaces Maps. 239-249 - Grace Solomonoff:
A Thousand Brains and a Million Theories. 250-260 - Rachel A. StClair, William Edward Hahn, Elan Barenholtz:
The Role of Bio-Inspired Modularity in General Learning. 261-268 - Claes Strannegård, Niklas Engsner, Pietro Ferrari, Hans Glimmerfors, Marcus Hilding Södergren, Tobias Karlsson, Birger Kleve, Victor Skoglund:
The Ecosystem Path to AGI. 269-278 - Yurii A. Kolonin, Evgenii Vityaev:
On Comparative Analysis of Rule-Based Cognitive Architectures. 279-282 - Robert Wünsche:
20NAR1 - An Alternative NARS Implementation Design. 283-291 - Bowen Xu, Xinyi Zhan, Quansheng Ren:
The Gap Between Intelligence and Mind. 292-305 - Tom Xu, Yoshihiro Maruyama:
Neural String Diagrams: A Universal Modelling Language for Categorical Deep Learning. 306-315 - Roman V. Yampolskiy:
AGI Control Theory. 316-326 - King-Yin Yan:
AGI via Combining Logic with Deep Learning. 327-343 - Artem Zholus, Aleksandr I. Panov:
Case-Based Task Generalization in Model-Based Reinforcement Learning. 344-354 - Jincheng Zhou, Volkan Ustun:
PySigma: Towards Enhanced Grand Unification for the Sigma Cognitive Architecture. 355-366
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