[Achler, T. 2012a. Artificial General Intelligence Begins with Recognition: Evaluating the Flexibility of Recognition. In Theoretical Foundations of Artificial General Intelligence. Springer. 197-217.10.2991/978-94-91216-62-6_11]Search in Google Scholar
[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.]Search in Google Scholar
[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-level artificial general intelligence. AI Magazine 33(1):25-42.10.1609/aimag.v33i1.2322]Search in Google Scholar
[Albus, J. S. 2001. Engineering of mind: An introduction to the science of intelligent systems. Wiley.]Search in Google Scholar
[Alvarado, N.; Adams, S. S.; Burbeck, S.; and Latta, C. 2002. Beyond the Turing test: Performance metrics for evaluating a computer simulation of the human mind. In The 2nd International Conference on Development and Learning, 147-152. IEEE.]Search in Google Scholar
[Anderson, J. R., and Lebiere, C. 2003. The Newell test for a theory of cognition. Behavioral and Brain Sciences 26(05):587-601.10.1017/S0140525X0300013X15179936]Search in Google Scholar
[Anselmi, F.; Leibo, J. Z.; Rosasco, L.; Mutch, J.; Tacchetti, A.; and Poggio, T. 2013. Magic Materials: a theory of deep hierarchical architectures for learning sensory representations.]Search in Google Scholar
[Arel, I.; Rose, D.; and Coop, R. 2009. Destin: A scalable deep learning architecture with application to high-dimensional robust pattern recognition. In Proc. AAAI Fall Symposium on Biologically Inspired Cognitive Architectures, 1150-1157.]Search in Google Scholar
[Arel, I.; Rose, D.; and Karnowski, T. 2009. A deep learning architecture comprising homogeneous cortical circuits for scalable spatiotemporal pattern inference. In NIPS 2009 Workshop on Deep Learning for Speech Recognition and Related Applications.]Search in Google Scholar
[Baars, B. J., and Franklin, S. 2009. Consciousness is computational: The LIDA model of global workspace theory. International Journal of Machine Consciousness 1(01):23-32.10.1142/S1793843009000050]Search in Google Scholar
[Bach, J. 2009. Principles of synthetic intelligence PSI: an architecture of motivated cognition, volume 4. Oxford University Press.10.1093/acprof:oso/9780195370676.001.0001]Search in Google Scholar
[Baran`es, A., and Oudeyer, P.-Y. 2009. R-IAC: Robust intrinsically motivated exploration and active learning. Autonomous Mental Development, IEEE Transactions on 1(3):155-169.10.1109/TAMD.2009.2037513]Search in Google Scholar
[Ben-David, S., and Schuller, R. 2003. Exploiting task relatedness for multiple task learning. In Learning Theory and Kernel Machines. Springer. 567-580.10.1007/978-3-540-45167-9_41]Search in Google Scholar
[Bengio, Y. 2009. Learning deep architectures for AI. Foundations and Trends in Machine Learning 2(1):1-127.10.1561/2200000006]Search in Google Scholar
[Binet, A., and Simon, T. 1916. The development of intelligence in children: The Binet-Simon Scale. Number 11. Williams & Wilkins Company.10.1037/11069-000]Search in Google Scholar
[Bostrom, N. 2014. Superintelligence: Paths, Dangers, Strategies. Oxford University Press.]Search in Google Scholar
[Brooks, R. A. 2002. Flesh and machines: How robots will change us. Pantheon Books New York ]Search in Google Scholar
[Cassimatis, N. 2007. Adaptive algorithmic hybrids for human-level Artificial Intelligence. In Advances in Artificial General Intelligence: Concepts, Architectures and Algorithms, 94-112.]Search in Google Scholar
[Damer, B.; Newman, P.; Gordon, R.; and Barbalet, T. 2010. The EvoGrid: simulating pre-biotic emergent complexity.]Search in Google Scholar
[De Garis, H.; Shuo, C.; Goertzel, B.; and Ruiting, L. 2010. A world survey of artificial brain projects, Part I: Large-scale brain simulations. Neurocomputing 74(1):3-29.10.1016/j.neucom.2010.08.004]Search in Google Scholar
[Duch, W.; Oentaryo, R. J.; and Pasquier, M. 2008. Cognitive Architectures: Where do we go from here? In Proceedings of the First Conference on Artificial General Intelligence, volume 171, 122-136.]Search in Google Scholar
[Dye, L. 2010. Are Dolphins Also Persons? ABC News, Feb. 24 2010.]Search in Google Scholar
[Franklin, S., and Graesser, A. 1997. Is it an Agent, or just a Program?: A Taxonomy for Autonomous Agents. In Intelligent agents III: agent theories, architectures, and languages. Springer. 21-35.]Search in Google Scholar
[Franklin, S.; Strain, S.; Snaider, J.; McCall, R.; and Faghihi, U. 2012. Global workspace theory, its LIDA model and the underlying neuroscience. Biologically Inspired Cognitive Architectures 1:32-43.10.1016/j.bica.2012.04.001]Search in Google Scholar
[French, R. M. 1996. Subcognition and the Limits of the Turing Test. Machines and thought 11-26.]Search in Google Scholar
[Frye, J.; Ananthanarayanan, R.; and Modha, D. S. 2007. Towards real-time, mouse-scale cortical simulations. CoSyNe: Computational and Systems Neuroscience, Salt Lake City, Utah.]Search in Google Scholar
[Gardner, H. 1999. Intelligence reframed: Multiple intelligences for the 21st century. Basic Books.]Search in Google Scholar
[Gazzaniga, M. S.; Ivry, R. B.; and Mangun, G. R. 2009. Cognitive Neuroscience: The Biology of the Mind. W W Norton.]Search in Google Scholar
[Goertzel, B., and Pennachin, C. 2007. Artificial General Intelligence. Springer.10.1007/978-3-540-68677-4]Search in Google Scholar
[Goertzel, B., and Pitt, J. 2012. Nine Ways to Bias Open-Source AGI Toward Friendliness. Journal of Evolution and Technology 22:1.]Search in Google Scholar
[Goertzel, B., and Wigmore, J. 2011. Cognitive Synergy Is Tricky. Chinese Journal of Mind and Computation.]Search in Google Scholar
[Goertzel, B.; Lian, R.; Arel, I.; de Garis, H.; and Chen, S. 2010a. A world survey of artificial brain projects, Part II: Biologically inspired cognitive architectures. Neurocomputing 74(1):30-49.]Search in Google Scholar
[Goertzel, B.; Pennachin, C.; Araujo, S.; Silva, F.; Queiroz, M.; Lian, R.; Silva, W.; Ross, M.; Vepstas, L.; and Senna, A. 2010b. A general intelligence oriented architecture for embodied natural language processing. In 3d Conference on Artificial General Intelligence (AGI-2010). Atlantis Press.10.2991/agi.2010.16]Search in Google Scholar
[Goertzel, B.; Pitt, J.; Wigmore, J.; Geisweiller, N.; Cai, Z.; Lian, R.; Huang, D.; and Yu, G. 2011. Cognitive Synergy between Procedural and Declarative Learning in the Control of Animated and Robotic Agents Using the OpenCogPrime AGI Architecture. In Proceedings of AAAI-11. 10.1609/aaai.v25i1.7831]Search in Google Scholar
[Goertzel, B.; Ikl´e, M.; and Wigmore, J. 2012. The Architecture of Human-Like General Intelligence. In Theoretical Foundations of Artificial General Intelligence. Springer. 123-144.10.2991/978-94-91216-62-6_8]Search in Google Scholar
[Goertzel, B. 2009. OpenCogPrime: A cognitive synergy based architecture for artificial general intelligence. In Proceedings of ICCI’09: 8th IEEE International Conference on Cognitive Informatics, 60-68. IEEE.10.1109/COGINF.2009.5250807]Search in Google Scholar
[Goertzel, B. 2010. Toward a formal characterization of real-world general intelligence. In Proceedings of the Third Conference on Artificial General Intelligence, 19-24.]Search in Google Scholar
[Goertzel, B. 2014. Artificial General Intelligence. Japanese Artificial Intelligence Society Magazine, 2014-1.]Search in Google Scholar
[Gregory, R. J. 2004. Psychological testing: History, principles, and applications. Allyn & Bacon.]Search in Google Scholar
[Gubrud, M. A. 1997. Nanotechnology and international security. In Fifth Foresight Conference on Molecular Nanotechnology, 1.]Search in Google Scholar
[Hammer, B., and Hitzler, P. 2007. Perspectives of neural-symbolic integration, volume 77. Springer.10.1007/978-3-540-73954-8]Search in Google Scholar
[Han, J.; Zeng, S.; Tham, K.; Badgero, M.; and Weng, J. 2002. Dav: A humanoid robot platform for autonomous mental development. In Development and Learning, 2002. Proceedings. The 2nd International Conference on, 73-81. IEEE.]Search in Google Scholar
[Hawkins, J., and Blakeslee, S. 2007. On intelligence. Macmillan.]Search in Google Scholar
[Hayes, P., and Ford., K. 1995. Turing Test Considered Harmful. IJCAI-14.]Search in Google Scholar
[Hern´andez-Orallo, J., and Dowe, D. L. 2010. Measuring universal intelligence: Towards an anytime intelligence test. Artificial Intelligence 174(18):1508-1539.10.1016/j.artint.2010.09.006]Search in Google Scholar
[Hibbard, B. 2012. Avoiding unintended AI behaviors. In Artificial General Intelligence. Springer. 107-116.10.1007/978-3-642-35506-6_12]Search in Google Scholar
[Horwitz, B.; Friston, K. J.; and Taylor, J. G. 2000. Neural modeling and functional brain imaging: an overview. Neural networks 13(8):829-846.10.1016/S0893-6080(00)00062-9]Search in Google Scholar
[Hutter, M. 2005. Universal Artificial Intelligence: Sequential Decisions based on Algorithmic Probability. Springer.]Search in Google Scholar
[Hutter, M. 2006. Human Knowledge Compression Contest. http://prize.hutter1.net/.]Search in Google Scholar
[Izhikevich, E. M., and Edelman, G. M. 2008. Large-scale model of mammalian thalamocortical systems. Proc. of the national academy of sciences 105(9):3593-3593.10.1073/pnas.0712231105226516018292226]Search in Google Scholar
[Jilk, D. J., and Lebiere, C. 2008. SAL: An explicitly pluralistic cognitive architecture. Journal of Experimental and Theoretical Artificial Intelligence 20:197-218.10.1080/09528130802319128]Search in Google Scholar
[Jurafsky, D., and James, H. 2000. Speech and language processing: An introduction to natural language processing, computational linguistics, and speech. ]Search in Google Scholar
[Just, M. A., and Varma, S. 2007. The organization of thinking: What functional brain imaging reveals about the neuroarchitecture of complex cognition. Cognitive, Affective, and Behavioral Neuroscience 7:153-191.10.3758/CABN.7.3.153]Search in Google Scholar
[Kaplan, F. 2008. Neurorobotics: an experimental science of embodiment. Frontiers in neuroscience 2(1):22.10.3389/neuro.01.023.2008257008218982102]Search in Google Scholar
[Koza, J. R. 1992. Genetic programming: on the programming of computers by means of natural selection, volume 1. MIT press.]Search in Google Scholar
[Krichmar, J. L., and Edelman, G. M. 2006. Principles underlying the construction of brain-based devices. In Proceedings of AISB, volume 6, 37-42.]Search in Google Scholar
[Kurzweil, R. 2005. The singularity is near: When humans transcend biology. Penguin.]Search in Google Scholar
[Laird, J. E.; Wray, R.; Marinier, R.; and Langley, P. 2009. Claims and challenges in evaluating human-level intelligent systems. In Proceedings of the Second Conference on Artificial General Intelligence, 91-96.]Search in Google Scholar
[Laird, J. 2012. The Soar cognitive architecture. MIT Press.10.7551/mitpress/7688.001.0001]Search in Google Scholar
[Langley, P. 2005. An adaptive architecture for physical agents. In Proceedings of the 2005]Search in Google Scholar
[IEEE/WIC/ACM International Conference on Web Intelligence, 18-25. IEEE.]Search in Google Scholar
[Laud, A., and Dejong, G. 2003. The influence of reward on the speed of reinforcement learning. Proc. of the 20th International Conf. on Machine Learning.]Search in Google Scholar
[Le, Q. V. 2013. Building high-level features using large scale unsupervised learning. In 2013 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 8595-8598. IEEE.10.1109/ICASSP.2013.6639343]Search in Google Scholar
[Legg, S., and Hutter, M. 2007a. A collection of definitions of intelligence. Frontiers in Artificial Intelligence and Applications 157:17.]Search in Google Scholar
[Legg, S., and Hutter, M. 2007b. Universal intelligence: A definition of machine intelligence. Minds and Machines 17(4):391-444.10.1007/s11023-007-9079-x]Search in Google Scholar
[Legg, S., and Veness, J. 2013. An approximation of the universal intelligence measure. In Algorithmic Probability and Friends. Bayesian Prediction and Artificial Intelligence. Springer. 236-249.10.1007/978-3-642-44958-1_18]Search in Google Scholar
[Lenat, D. B., and Guha, R. V. 1989. Building large knowledge-based systems; representation and inference in the Cyc project. Addison-Wesley Longman Publishing Co., Inc.]Search in Google Scholar
[Li, G.; Lou, Z.; Wang, L.; Li, X.; and Freeman, W. J. 2005. Application of chaotic neural model based on olfactory system on pattern recognitions. In Advances in Natural Computation. Springer. 378-381.10.1007/11539087_47]Search in Google Scholar
[Li, L.; Walsh, T.; and Littman, M. 2006. Towards a unified theory of state abstraction for MDPs. Proc. of the ninth international symposium on AI and mathematics. ]Search in Google Scholar
[Markram, H. 2006. The blue brain project. Nature Reviews Neuroscience 7(2):153-160.10.1038/nrn184816429124]Search in Google Scholar
[Metta, G.; Sandini, G.; Vernon, D.; Natale, L.; and Nori, F. 2008. The iCub humanoid robot: an open platform for research in embodied cognition. In Proceedings of the 8th workshop on performance metrics for intelligent systems, 50-56. ACM.10.1145/1774674.1774683]Search in Google Scholar
[Modayil, J., and Kuipers, B. 2007. Autonomous development of a grounded object ontology by a learning robot. In Proceedings of the national conference on Artificial intelligence, volume 22, 1095. Menlo Park, CA; Cambridge, MA; London; AAAI Press; MIT Press; 1999.]Search in Google Scholar
[Mugan, J., and Kuipers, B. 2008. Towards the application of reinforcement learning to undirected developmental learning. International Conf. on Epigenetic Robotics.]Search in Google Scholar
[Mugan, J., and Kuipers, B. 2009. Autonomously Learning an Action Hierarchy Using a Learned Qualitative State Representation. In IJCAI, 1175-1180.]Search in Google Scholar
[Muggleton, S. 1991. Inductive logic programming. New generation computing 8(4):295-318.10.1007/BF03037089]Search in Google Scholar
[Nestor, A., and Kokinov, B. 2004. Towards Active Vision in the DUAL Cognitive Architecture. International Journal on Information Theories and Applications 11.]Search in Google Scholar
[Nilsson, N. J. 2005. Human-level artificial intelligence? Be serious! AI magazine 26(4):68.]Search in Google Scholar
[Nilsson, N. J. 2007. The physical symbol system hypothesis: status and prospects. In 50 years of artificial intelligence. Springer. 9-17.10.1007/978-3-540-77296-5_2]Search in Google Scholar
[Oudeyer, P.-Y., and Kaplan, F. 2006. Discovering communication. Connection Science 18(2):189-206.10.1080/09540090600768567]Search in Google Scholar
[Pfeifer, R., and Bongard, J. 2007. How the body shapes the way we think: a new view of intelligence. MIT press.10.7551/mitpress/3585.001.0001]Search in Google Scholar
[Reeke Jr, G. N.; Sporns, O.; and Edelman, G. M. 1990. Synthetic neural modeling: theDarwin’series of recognition automata. Proceedings of the IEEE 78(9):1498-1530.10.1109/5.58327]Search in Google Scholar
[Richardson, M., and Domingos, P. 2006. Markov logic networks. Machine learning 62(1-2):107-136.10.1007/s10994-006-5833-1]Search in Google Scholar
[Rosbe, J.; Chong, R. S.; and Kieras, D. E. 2001. Modeling with Perceptual and Memory Constraints: An EPIC-Soar Model of a Simplified Enroute Air Traffic Control Task. SOAR Technology Inc. Report.10.1037/e446312006-001]Search in Google Scholar
[Russell, S. J., and Norvig, P. 2010. Artificial intelligence: a modern approach. Prentice Hall.]Search in Google Scholar
[Samsonovich, A. V. 2010. Toward a Unified Catalog of Implemented Cognitive Architectures. BICA 221:195-244.]Search in Google Scholar
[Schmidhuber, J. 1991a. Curious model-building control systems.. Proc. International Joint Conf. on Neural Networks. 10.1109/IJCNN.1991.170605]Search in Google Scholar
[Schmidhuber, J. 1991b. A possibility for implementing curiosity and boredom in model-building neural controllers. Proc. of the International Conf. on Simulation of Adaptive Behavior: From Animals to Animats.]Search in Google Scholar
[Schmidhuber, J. 1995. Reinforcement-driven information acquisition in non-deterministic environments. Proc. ICANN’95.]Search in Google Scholar
[Schmidhuber, J. 2003. Exploring the predictable. In Advances in evolutionary computing. Springer. 579-612.10.1007/978-3-642-18965-4_23]Search in Google Scholar
[Schmidhuber, J. 2006. Godel machines: Fully Self-Referential Optimal Universal Self-Improvers. In Goertzel, B., and Pennachin, C., eds., Artificial General Intelligence. 119-226.]Search in Google Scholar
[Searle, J. R. 1980. Minds, brains, and programs. Behavioral and brain sciences 3(03):417-424.10.1017/S0140525X00005756]Search in Google Scholar
[Seth Baum, B. G., and Goertzel, T. 2011. Technological Forecasting and Social Change. Technological Forecasting and Social Change.]Search in Google Scholar
[Shapiro, S. C.; Rapaport,W. J.; Kandefer, M.; Johnson, F. L.; and Goldfain, A. 2007. Metacognition in SNePS. AI Magazine 28(1):17.]Search in Google Scholar
[Shastri, L., and Ajjanagadde, V. 1993. From simple associations to systematic reasoning: A connectionist representation of rules, variables and dynamic bindings using temporal synchrony. Behavioral and brain sciences 16(3):417-451.10.1017/S0140525X00030910]Search in Google Scholar
[Silver, R.; Boahen, K.; Grillner, S.; Kopell, N.; and Olsen, K. L. 2007. Neurotech for neuroscience: unifying concepts, organizing principles, and emerging tools. The Journal of Neuroscience 27(44):11807-11819.10.1523/JNEUROSCI.3575-07.2007]Search in Google Scholar
[Sloman, A. 2001. Varieties of affect and the cogaff architecture schema. In Proceedings of the AISB01 symposium on emotions, cognition, and affective computing. The Society for the Study of Artificial Intelligence and the Simulation of Behaviour.]Search in Google Scholar
[Socher, R.; Huval, B.; Bath, B. P.; Manning, C. D.; and Ng, A. Y. 2012. Convolutional-Recursive Deep Learning for 3D Object Classification. In NIPS, 665-673.]Search in Google Scholar
[Solomonoff, R. J. 1964a. A formal theory of inductive inference. Part I. Information and control 7(1):1-22.10.1016/S0019-9958(64)90223-2]Search in Google Scholar
[Solomonoff, R. J. 1964b. A formal theory of inductive inference. Part II. Information and control 7(2):224-254.10.1016/S0019-9958(64)90131-7]Search in Google Scholar
[Spearman, C. 1904. General Intelligence, Objectively Determined and Measured. The American Journal of Psychology 15(2):201-292.10.2307/1412107]Search in Google Scholar
[Sun, R., and Zhang, X. 2004. Top-down versus bottom-up learning in cognitive skill acquisition. Cognitive Systems Research 5(1):63-89.10.1016/j.cogsys.2003.07.001]Search in Google Scholar
[Taylor, M. E.; Kuhlmann, G.; and Stone, P. 2008. Transfer Learning and Intelligence: an Argument and Approach. FRONTIERS IN ARTIFICIAL INTELLIGENCE AND APPLICATIONS 171:326. ]Search in Google Scholar
[Terman, L. M. 1915. The mental hygiene of exceptional children. The Pedagogical Seminary 22(4):529-537.10.1080/08919402.1915.10533983]Search in Google Scholar
[Thrun, S., and Mitchell, T. 1995. Lifelong robot learning. Robotics and Autonomous Systems.10.1007/978-3-642-79629-6_7]Search in Google Scholar
[Turing, A. M. 1950. Computing machinery and intelligence. Mind 433-460.10.1093/mind/LIX.236.433]Search in Google Scholar
[Veness, J.; Ng, K. S.; Hutter, M.; Uther,W.; and Silver, D. 2011. A monte-carlo aixi approximation. Journal of Artificial Intelligence Research 40(1):95-142.10.1613/jair.3125]Search in Google Scholar
[Wang, P. 2006. Rigid Flexibility: The Logic of Intelligence. Springer.]Search in Google Scholar
[Wang, P. 2009. Embodiment: Does a Laptop Have a Body? In Proceedings of AGI-09, 74-179.]Search in Google Scholar
[Weng, J., and Hwang, W.-S. 2006. From neural networks to the brain: Autonomous mental development. Computational Intelligence Magazine, IEEE 1(3):15-31.10.1109/MCI.2006.1672985]Search in Google Scholar
[Weng, J.; Hwang, W. S.; Zhang, Y.; Yang, C.; and Smith, R. 2000. Developmental humanoids: Humanoids that develop skills automatically. In Proc. The First IEEE-RAS International Conference on Humanoid Robots, 7-8. Citeseer.]Search in Google Scholar
[Yudkowsky, E. 2008. Artificial intelligence as a positive and negative factor in global risk. In Global catastrophic risks. Oxford University Press. 303 10.1093/oso/9780198570509.003.0021]Search in Google Scholar
