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9. AGI 2016: New York, NY, USA
- Bas R. Steunebrink, Pei Wang, Ben Goertzel:
Artificial General Intelligence - 9th International Conference, AGI 2016, New York, NY, USA, July 16-19, 2016, Proceedings. Lecture Notes in Computer Science 9782, Springer 2016, ISBN 978-3-319-41648-9 - Tom Everitt
, Daniel Filan, Mayank Daswani, Marcus Hutter
Self-Modification of Policy and Utility Function in Rational Agents. 1-11 - Tom Everitt
Avoiding Wireheading with Value Reinforcement Learning. 12-22 - Jarryd Martin, Tom Everitt
Death and Suicide in Universal Artificial Intelligence. 23-32 - Eray Özkural:
Ultimate Intelligence Part II: Physical Complexity and Limits of Inductive Inference Systems. 33-42 - David (Weaver) Weinbaum, Viktoras Veitas:
Open-Ended Intelligence - On the Role of Individuation in AGI. 43-52 - James Babcock, János Kramár, Roman Yampolskiy:
The AGI Containment Problem. 53-63 - Garrett E. Katz, Di-Wei Huang, Rodolphe J. Gentili, James A. Reggia:
Imitation Learning as Cause-Effect Reasoning. 64-73 - Arthur Franz:
Some Theorems on Incremental Compression. 74-83 - Paul S. Rosenbloom, Abram Demski, Volkan Ustun:
Rethinking Sigma's Graphical Architecture: An Extension to Neural Networks. 84-94 - Alexey Potapov, Sergey Rodionov, Vita Potapova:
Real-Time GA-Based Probabilistic Programming in Application to Robot Control. 95-105 - Kristinn R. Thórisson, David Kremelberg, Bas R. Steunebrink, Eric Nivel:
About Understanding. 106-117 - Kristinn R. Thórisson, Jordi Bieger, Thröstur Thorarensen, Jóna S. Sigurðardóttir, Bas R. Steunebrink:
Why Artificial Intelligence Needs a Task Theory - And What It Might Look Like. 118-128 - Bas R. Steunebrink, Kristinn R. Thórisson, Jürgen Schmidhuber:
Growing Recursive Self-Improvers. 129-139 - Pei Wang, Xiang Li:
Different Conceptions of Learning: Function Approximation vs. Self-Organization. 140-149 - Pei Wang, Max Talanov
The Emotional Mechanisms in NARS. 150-159 - Patrick Hammer, Tony Lofthouse, Pei Wang:
The OpenNARS Implementation of the Non-Axiomatic Reasoning System. 160-170 - Claes Strannegård, Abdul Rahim Nizamani:
Integrating Symbolic and Sub-symbolic Reasoning. 171-180 - Claes Strannegård, Abdul Rahim Nizamani, Ulf Persson:
Integrating Axiomatic and Analogical Reasoning. 181-191 - John Licato, Maxwell Fowler:
Embracing Inference as Action: A Step Towards Human-Level Reasoning. 192-201 - Scott Garrabrant, Tsvi Benson-Tilsen, Siddharth Bhaskar, Abram Demski, Joanna Garrabrant, George Koleszarik, Evan Lloyd:
Asymptotic Logical Uncertainty and the Benford Test. 202-211 - Nico Potyka
Towards a Computational Framework for Function-Driven Concept Invention. 212-222 - Sean Markan:
System Induction Games and Cognitive Modeling as an AGI Methodology. 223-233 - Nutchanon Yongsatianchot, Stacy Marsella:
Integrating Model-Based Prediction and Facial Expressions in the Perception of Emotion. 234-243 - Grace Solomonoff:
A Few Notes on Multiple Theories and Conceptual Jump Size. 244-253 - Tony Lofthouse, Patrick Hammer:
Generalized Temporal Induction with Temporal Concepts in a Non-axiomatic Reasoning System. 254-257 - Craig Sherstan, Adam White, Marlos C. Machado, Patrick M. Pilarski
Introspective Agents: Confidence Measures for General Value Functions. 258-261 - Yura N. Perov, Frank D. Wood:
Automatic Sampler Discovery via Probabilistic Programming and Approximate Bayesian Computation. 262-273 - Mrwan Margem, Özgür Yilmaz:
How Much Computation and Distributedness is Needed in Sequence Learning Tasks? 274-283 - Andrew MacFie:
Analysis of Algorithms and Partial Algorithms. 284-293 - András Lörincz
Estimating Cartesian Compression via Deep Learning. 294-304 - Harry H. Porter III:
A Methodology for the Assessment of AI Consciousness. 305-313 - Lyle N. Long:
Toward Human-Level Massively-Parallel Neural Networks with Hodgkin-Huxley Neurons. 314-323 - Frédéric Alexandre, Maxime Carrere:
Modeling Neuromodulation as a Framework to Integrate Uncertainty in General Cognitive Architectures. 324-333 - Ben Goertzel, Misgana Bayetta Belachew, Matthew Iklé
Controlling Combinatorial Explosion in Inference via Synergy with Nonlinear-Dynamical Attention Allocation. 334-343 - Ben Goertzel:
Probabilistic Growth and Mining of Combinations: A Unifying Meta-Algorithm for Practical General Intelligence. 344-353 - Susumu Katayama:
Ideas for a Reinforcement Learning Algorithm that Learns Programs. 354-362
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