Search Results

1 - 10 of 59 items :

  • "artificial general intelligence" x
Clear All

. Goertzel, B. (2011). Itamar Arel on the Path to Artificial General Intelligence. Available electronically at http://hplusmagazine.com/2011/02/04/itamar-arel-on-the-path-to-artificialgeneral- intelligence Hawkins, J.; and George, D. (2006). Hierarchical temporal memory: concepts, theory, and terminology. Available electronically at http://www.numenta.com/htmoverview/ education/Numenta_HTM_Concepts.pdf Heljakka, A.; Goertzel, B.; Silva, W.; Goertzel, I.; and Pennachin, C. (2007). Reinforcement Learning of Simple Behaviors in a Simulation World Using Probabilistic Logic

References Achler, T., and Amir, E. 2009. Neuroscience and AI share the same elegant mathematical trap. In Proc 2009 Conf on Artificial General Intelligence . Asuncion, A., and Newman, D. 2007. UCI Machine Learning Repository. http://www.ics.uci.edu/~mlearn/MLRepository.html AUVSI. 2009. AUVSI Unmanned Systems Online. http://www.auvsi.org/competitions/water.cfm Bayer, S.; Damianos, L.; Hirschman, L.; and Strong, G. 2004. A Summary of Previous Grand Challenge Proposals for Cognitive Systems. Technical report, The MITRE Corporation. Version 1.5, Prepared for

References Achler, T. 2012a. Artificial General Intelligence Begins with Recognition: Evaluating the Flexibility of Recognition. In Theoretical Foundations of Artificial General Intelligence. Springer. 197-217. Achler, T. 2012b. Towards Bridging the Gap Between Pattern Recognition and Symbolic Representation Within Neural Networks. Workshop on Neural-Symbolic Learning and Reasoning, AAAI-2012. Adams, S.; Arel, I.; Bach, J.; Coop, R.; Furlan, R.; Goertzel, B.; Hall, J. S.; Samsonovich, A.; Scheutz, M.; Schlesinger, M.; et al. 2012. Mapping the landscape of human

. Universal Algorithmic Intelligence: A Mathematical Top→Down Approach. In Goertzel, B., and Pennachin, C., eds., Artificial General Intelligence , Cognitive Technologies. Berlin: Springer. 227–290. Katayama, S. 2016. Ideas for a Reinforcement Learning Algorithm that Learns Programs. In Artificial General Intelligence - 9th International Conference, AGI 2016, AGI 2016, New York, USA, July 16–19, 2016, Proceedings , 354–362. Plume, D. 1998. A Calculator for Exact Real Number Computation . Ph.D. Dissertation, University of Edinburgh. Sutton, R. S., and Barto, A. G. 1998

., Proceedings of the 3rd Conference on Artificial General Intelligence, AGI 2010, 19-24. Amsterdam-Beijing-Paris: Atlantis Press. Goertzel, B. 2014. Artificial General Intelligence: Concept, State of the Art, and Future Prospects. Journal of Artificial General Intelligence 5(1):1-48. Harnad, S. 1991. Other Bodies, Other Minds: A Machine Incarnation of an Old Philosophical Problem. Minds and Machines 1(1):43-54. Hernández-Orallo, J., and Dowe, D. L. 2010. Measuring Universal Intelligence: Towards an Anytime Intelligence Test. Artificial Intelligence 174

Duplicates Emergent Behavior in the Brain. World Academy of Science, Engineering, and Technology 68:1-9. Available electronically from https://www.waset.org/journals/waset/v44/v44-1.pdf. Pissanetzky, S. 2011. Structural Emergence in Partially Ordered Sets is the Key to Intelligence. Artificial General Intelligence 92-101. Available electronically from http://dl.acm.org/citation.cfm?id=2032884. Pissanetzky, S.; and Lanzalaco, F. 2013. Black-box Brain Experiments, Causal Mathematical Logic, and the Thermodynamics of Intelligence. Under revision for Journal of Artificial

Science and Technology, Vol. 4, Academic Press, 155–170. Hutter, M. (2007) Universal algorithmic intelligence: A mathematical top→down approach. In B. Goertzel and C. Pennachin, editors, Artificial General Intelligence, Cognitive Technologies, pages 227–290. Springer, Berlin, 2007. Koza, J. R., Keane, M. A., Bennett III, F. H., and Mydlowec, W. (2000). Automatic Creation of Human-Competitive Programs and Controllers by Means of Genetic Programming. In Genetic Programming and Evolvable Machines, 1:121-164. Laird, J. E., Newell, A. and Rosenbloom P. S. (1987) Soar: an

. 2007. Learning to control a dynamic task: A system dynamics cognitive model of the slope effect. In Proceedings of the 8th International Conference on Cognitive Modeling , 61-66. Ann Arbor, MI. Halbrügge, M. in press. Keep it simple - A case study of model development in the context of the Dynamic Stock and Flows (DSF) task. Journal of Artificial General Intelligence . Kase, S.E., Ritter, F.E., and Schoelles, M. 2007. Using HPC and PGAs to optimize noisy computational models of cognition. In Innovations and Advanced Techniques in Systems, Computing Sciences and

architecture for cognition and performance with application to human-computer interaction. Human-Computer Interaction, 12 , 391-438. Kowler, E., Anderson, E., Dosher, B., Blaser, E. (1995). The role of attention in the programming of saccades. Vision Research, 35 (13), 1897-1916. Laird, J. E. (2008). Extending the Soar cognitive architecture . Paper presented at the 1st Artificial General Intelligence Conference, Memphis, TN. Laird, J. E., Wray, R. E., Marinier, R. P., & Langley, P. (2009). Claims and challenges in evaluating human-level intelligent systems . Paper

Proceedings of the Second Artificial General Intelligence Conference (AGI-09). Amsterdam-Paris: Atlantis Press. Lebiere, C., & Wray, R. (2006). Between a Rock and a Hard Place: Cognitive Science Principles Meet AI-Hard Problems. Technical Report SS-06-02. AAAI Press, Menlo Park, California. Newell, A. N. (1973). You can't play 20 questions with nature and win: Projective comments on papers in this symposium. In W. G. Chase (Ed.), Visual information processing. New York: Academic Press. Stewart, D. (1994). Interview with Herbert Simon. Omni Magazine, June 1994. Warwick