1. bookVolume 5 (2014): Issue 1 (December 2014)
Journal Details
First Published
23 Nov 2011
Publication timeframe
2 times per year
Open Access

Artificial General Intelligence: Concept, State of the Art, and Future Prospects

Published Online: 30 Dec 2014
Volume & Issue: Volume 5 (2014) - Issue 1 (December 2014)
Page range: 1 - 48
Accepted: 15 Mar 2014
Journal Details
First Published
23 Nov 2011
Publication timeframe
2 times per year

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_11Search 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.v33i1.2322Search 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/S0140525X0300013X15179936Search 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/S1793843009000050Search 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/9780195370676.001.0001Search 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.2009.2037513Search 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_41Search in Google Scholar

Bengio, Y. 2009. Learning deep architectures for AI. Foundations and Trends in Machine Learning 2(1):1-127.10.1561/2200000006Search in Google Scholar

Binet, A., and Simon, T. 1916. The development of intelligence in children: The Binet-Simon Scale. Number 11. Williams & Wilkins Company.10.1037/11069-000Search 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.2010.08.004Search 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.2012.04.001Search 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.10.1007/978-3-540-68677-4Search 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.2010.16Search 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.v25i1.7831Search 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_8Search 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.2009.5250807Search 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.10.1007/978-3-540-73954-8Search 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.2010.09.006Search in Google Scholar

Hibbard, B. 2012. Avoiding unintended AI behaviors. In Artificial General Intelligence. Springer. 107-116.10.1007/978-3-642-35506-6_12Search 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)00062-9Search 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.0712231105226516018292226Search in Google Scholar

Jilk, D. J., and Lebiere, C. 2008. SAL: An explicitly pluralistic cognitive architecture. Journal of Experimental and Theoretical Artificial Intelligence 20:197-218.10.1080/09528130802319128Search 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.7.3.153Search in Google Scholar

Kaplan, F. 2008. Neurorobotics: an experimental science of embodiment. Frontiers in neuroscience 2(1):22.10.3389/neuro.01.023.2008257008218982102Search 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/7688.001.0001Search in Google Scholar

Langley, P. 2005. An adaptive architecture for physical agents. In Proceedings of the 2005Search 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.2013.6639343Search 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-xSearch 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_18Search 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_47Search 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/nrn184816429124Search 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.10.1145/1774674.1774683Search 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/BF03037089Search 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_2Search in Google Scholar

Oudeyer, P.-Y., and Kaplan, F. 2006. Discovering communication. Connection Science 18(2):189-206.10.1080/09540090600768567Search 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/3585.001.0001Search 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):1498-1530.10.1109/5.58327Search in Google Scholar

Richardson, M., and Domingos, P. 2006. Markov logic networks. Machine learning 62(1-2):107-136.10.1007/s10994-006-5833-1Search 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/e446312006-001Search 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.1991.170605Search 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_23Search 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/S0140525X00005756Search 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/S0140525X00030910Search 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.3575-07.2007Search 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)90223-2Search 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)90131-7Search in Google Scholar

Spearman, C. 1904. General Intelligence, Objectively Determined and Measured. The American Journal of Psychology 15(2):201-292.10.2307/1412107Search 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.2003.07.001Search 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):529-537.10.1080/08919402.1915.10533983Search in Google Scholar

Thrun, S., and Mitchell, T. 1995. Lifelong robot learning. Robotics and Autonomous Systems.10.1007/978-3-642-79629-6_7Search in Google Scholar

Turing, A. M. 1950. Computing machinery and intelligence. Mind 433-460.10.1093/mind/LIX.236.433Search 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.3125Search 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.2006.1672985Search 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/9780198570509.003.0021Search in Google Scholar

Recommended articles from Trend MD