The Sigma Cognitive Architecture and System: Towards Functionally Elegant Grand Unification

Anderson, J. R. 1983. The Architecture of Cognition. Cambridge, MA: Harvard University Press.Search in Google Scholar

Anderson, J. R. 1990. The Adaptive Character of Thought. Hillsdale, NJ: Lawrence Erlbaum Associates.Search in Google Scholar

Anderson, J. R. 2002. Spanning seven orders of magnitude: A challenge for cognitive modeling. Cognitive Science. 26: 85-112.10.1207/s15516709cog2601_3Search in Google Scholar

Anderson, J. R. 2007. How Can the Human Mind Occur in the Physical Universe. Oxford: Oxford University Press.10.1093/acprof:oso/9780195324259.001.0001Search in Google Scholar

Anderson, J. R.; Bothell, D.; Byrne, M. D.; Douglass, S., Lebiere, C.; and Qi, Y. 2004. An integrated theory of the mind. Psychological Review. 111: 1036-1060.10.1037/0033-295X.111.4.1036Search in Google Scholar

Badler, N. I. 1997. Real-time virtual humans. In Proceedings of the IEEE Workshop on Non-Rigid and Articulated Motion, 28-36.Search in Google Scholar

Bach, J. 2015. Modeling motivation in MicroPsi 2. In Proceedings of the 8th Conference on Artificial General Intelligence, 3-13.Search in Google Scholar

Bailey, T.; and Durrant-Whyte, H. 2006. Simultaneous localisation and mapping (SLAM): Part II State of the art. Robotics and Automation Magazine. 13: 108–117.10.1109/MRA.2006.1678144Search in Google Scholar

Bell, C. G.; and Newell, A. 1971. Computer Structures: Readings and Examples. New York, NY: McGraw-Hill.Search in Google Scholar

Bengio, Y.; Ducharme, R.; Vincent, P.; and Janvin, C. 2003. A neural probabilistic language model. The Journal of Machine Learning Research. 3: 1137-1155.Search in Google Scholar

Best, B.; Lebiere, C.; and Scarpinatto, C. 2002. A model of synthetic opponents in MOUT training simulations using the ACT-R cognitive architecture. In Proceedings of the Eleventh Conference on Computer Generated Forces and Behavior Representation.10.1037/e617892011-143Search in Google Scholar

Bonasso, R. P.; Firby, R. J.; Gat, E.; Kortenkamp, D.; Miller, D. P.; and Slack, M. G. 1997. Experiences with an Architecture for Intelligent Reactive Agent. Journal of Experimental and Theoretical Artificial Intelligence. 9: 237-256.10.1080/095281397147103Search in Google Scholar

Bostrom, N. 2001. Are you living in a computer simulation? Philosophical Quarterly. 53: 243-255.10.1111/1467-9213.00309Search in Google Scholar

Bridewell, W.; and Langley, P. 2011. A computational account of everyday abductive inference. In Proceedings of the Thirty-Third Annual Meeting of the Cognitive Science Society, 2289-2294.Search in Google Scholar

Bubic, A.; von Cramon, D. Y.; and Schubotz, R. I. 2010. Prediction, cognition and the brain. Frontiers in Human Neuroscience. 4: 25.Search in Google Scholar

Campbell, J.; Core, M.; Artstein, R.; Armstrong, L.; Hartholt, A.; Wilson, C.; Georgila, K.; Morbini, F.; Haynes, E.; Gomboc, D.; Birch, M.; Bobrow, J.; Chad Lane, H.; Gerten, J.; Leuski, A.; Traum, D.; Trimmer, M.; DiNinni, R.; Bosack, M.; Jones, T.; Clark, R.E.; and Yates, K.A., 2011. Developing INOTS to Support Interpersonal Skills Practice. In Proceedings of the Thirty-second Annual IEEE Aerospace Conference, 1-14.10.1109/AERO.2011.5747535Search in Google Scholar

Card, S. K.; Moran, T.P.; and Newell, A. 1983. The Psychology of Human-Computer Interaction. Hillsdale, NJ: Lawrence Erlbaum Associates.Search in Google Scholar

Cassimatis, N. 2002. Polyscheme: A Cognitive Architecture for Integrating Multiple Representation and Inference Schemes. Ph.D. diss., Media Laboratory, MIT, Cambridge, Mass.Search in Google Scholar

Chater, N.; Oaksford, M. 1999. Ten years of the rational analysis of cognition. Trends in Cognitive Sciences. 3: 57–65.10.1016/S1364-6613(98)01273-XSearch in Google Scholar

Chen, J.; Demski, A.; Han, T.; Morency, L-P.; Pynadath, D.; Rafidi, N.; and Rosenbloom, P. S. 2011. Fusing symbolic and decision-theoretic problem solving + perception in a graphical cognitive architecture. In Proceedings of the 2nd International Conference on Biologically Inspired Cognitive Architectures, 64-72.Search in Google Scholar

Collobert, R.; and Weston, J. 2008. A unified architecture for natural language processing: Deep neural networks with multitask learning. In Proceedings of the 25th international conference on Machine learning, 160-167.Search in Google Scholar

Coste-Manière, E.; and Simmons, R. G. 2000. Architecture, the backbone of robotic systems. In Proceedings of the International Conference on Robotics and Automation, 67-72.Search in Google Scholar

Dancy, C. L.; Ritter, F. E.; and Berry, K. 2012. Towards adding a physiological substrate to ACT-R. In Proceedings of the 21st Conference on Behavior Representation in Modeling and Simulation, 78-85.Search in Google Scholar

Deering, S. 1988. Watching the waist of the protocol hourglass. Keynote address at ICNP ‘98. de Kleer, J. 1986. An assumption-based TMS. Artificial Intelligence. 28:127–162.Search in Google Scholar

Dempster, A.P.; Laird, N.M.; and Rubin, D.B. 1977. Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society. 39: 1–38.10.1111/j.2517-6161.1977.tb01600.xSearch in Google Scholar

Derbinsky, N.; Laird, J. E.; and Smith, B. 2010. Towards efficiently supporting large symbolic declarative memories. In Proceedings of the 10th International Conference on Cognitive Modeling, 49-54.Search in Google Scholar

Deutsch, D. 2011. The Beginning of Infinity: Explanations that Transform the World. London, UK: Penguin Books.Search in Google Scholar

Domingos, P.; and Lowd, D. 2009. Markov Logic: An Interface Layer for Artificial Intelligence. San Raphael, CA: Morgan & Claypool.10.2200/S00206ED1V01Y200907AIM007Search in Google Scholar

Douglass, S.; Ball, J.; & Rodgers, S. 2009. Large declarative memories in ACT-R. In Proceedings of the 9th International Conference of Cognitive Modeling, 222-227.Search in Google Scholar

Eliasmith, C. 2013. How to Build a Brain: A Neural Architecture for Biological Cognition. Oxford: Oxford University Press.10.1093/acprof:oso/9780199794546.001.0001Search in Google Scholar

Falkenhainer, B; Forbus, K. D.; and Gentner, D 1989. The structure-mapping engine: Algorithm and examples. Artificial Intelligence. 41: 1–63.10.1016/0004-3702(89)90077-5Search in Google Scholar

Fikes, R., Hart, P; and Nilsson, N. 1972. Learning and Executing Generalized Robot Plans, Artificial Intelligence. 3: 251-288.10.1016/0004-3702(72)90051-3Search in Google Scholar

Forbus, K. D.; and Hinrichs, T. R. 2006. Companion Cognitive Systems: A step towards human-level AI. AI Magazine. 27:83-95.Search in Google Scholar

Forgy, C. L. 1982. Rete: A fast algorithm for the many pattern/many object pattern match problem. Artificial Intelligence. 19: 17-37.10.1016/0004-3702(82)90020-0Search in Google Scholar

Frackowiak R; and Markram H. 2015. The future of human cerebral cartography: a novel approach. Philosophical Transactions of the Royal Society of London B: Biological Sciences. 370.10.1098/rstb.2014.0171438751225823868Search in Google Scholar

Frintrop, S.; Rome, E.; and Christensen, H.I. 2010: Computational visual attention systems and their cognitive foundation: A survey. ACM Transactions on Applied Perception. 7.10.1145/1658349.1658355Search in Google Scholar

Garten, J.; Sagae, K.; Ustun, V.; and Dehghani, M. 2015. Combining distributed vector representations for words. In Proceedings of the NAACL Workshop on Vector Space Modeling for NLP, 95-101.Search in Google Scholar

Goertzel, B. 2014. Artificial General Intelligence: Concept, State of the Art, and Future Prospects. Journal of Artificial General Intelligence. 5:1-46.10.2478/jagi-2014-0001Search in Google Scholar

Goertzel, B.; Pennachin, C.; and Geisweiller, N. 2014. Engineering General Intelligence. Amsterdam: Atlantis Press.10.2991/978-94-6239-027-0Search in Google Scholar

Goodman, N. D.; Mansinghka, V. K.; Roy, D.; Bonawitz, K.; and Tenenbaum, J. B. 2008. Church: a language for generative models. In Proceedings of the 24th Conference on Uncertainty in Artificial Intelligence, 220-229.Search in Google Scholar

Hartholt, A.; Traum, D. Marsella, S. C.; Shapiro, A.; Stratou, G.; Leuski, A.; Morency, L.-P.; and Gratch, J. 2013. All together now: Introducing the Virtual Human Toolkit. In Proceedings of the 13th International Conference on Intelligent Virtual Agents, 368-381.Search in Google Scholar

Hutter, M. 2005. Universal Artificial Intelligence: Sequential Decisions Based on Algorithmic Probability. Berlin: Springer-Verlag.Search in Google Scholar

Itti, L.; and Baldi, P.F. 2006. Bayesian surprise attracts human attention. In Advances in Neural Information Processing Systems 18, 547-554.Search in Google Scholar

Itti, L.; and Borji, A. 2013. State-of-the-art in visual attention modeling. IEEE Transactions on Pattern Analysis and Machine Intelligence. 35: 185-207.10.1109/TPAMI.2012.8922487985Search in Google Scholar

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

Jones, M. N.; and Mewhort, D. J. 2007. Representing word meaning and order information in a composite holographic lexicon. Psychological review. 114: 1-37.10.1037/0033-295X.114.1.1Search in Google Scholar

Jordan, M. I.; and Sejnowski, T. J. 2001. Graphical Models: Foundations of Neural Computation. Cambridge, MA: MIT Press.10.7551/mitpress/3349.001.0001Search in Google Scholar

Joshi, H.; Rosenbloom, P. S.; and Ustun, V. 2014. Isolated word recognition in the Sigma cognitive architecture. Biologically Inspired Cognitive Architectures. 10: 1-9.10.1016/j.bica.2014.11.001Search in Google Scholar

Kahneman, D. 2011. Thinking Fast and Slow. New York, NY: Farrar, Straus and Giroux.Search in Google Scholar

Kieras, D. E.; and Meyer, D. E. 1997. An overview of the EPIC architecture for cognition and performance with application to human-computer interaction. Human-Computer Interaction, 12: 391-438.Search in Google Scholar

Koller, D.; and Friedman, N. 2009. Probabilistic Graphical Models: Principles and Techniques. Cambridge, MA: MIT Press.Search in Google Scholar

Kopp, S.; Krenn, B.; Marsella, S.; Marshall, A. N.; Pelachaud C.; Pirker, H.; and Vilhjálmsson, H. 2006. Towards a common framework for multimodal generation: The behavior markup language. In Proceedings of the 6^th International Conference on Intelligent Virtual Agents, 205-217.Search in Google Scholar

Kschischang, F. R.; Frey, B. J.; and Loeliger, H.-A. 2001. Factor graphs and the sum-product algorithm. IEEE Transactions on Information Theory. 47: 498-519.10.1109/18.910572Search in Google Scholar

Laird, J. E. 2012. The Soar Cognitive Architecture. Cambridge, MA: MIT Press.10.7551/mitpress/7688.001.0001Search in Google Scholar

Laird, J E.; Newell, A.; and Rosenbloom, P. S. 1987. Soar: An Architecture for General Intelligence. Artificial Intelligence. 33: 1-64.10.1016/0004-3702(87)90050-6Search in Google Scholar

Laird, J. E.; and Rosenbloom, P. S. 1990. Integrating execution, planning, and learning in Soar for external environments, Proceedings of the Eighth National Conference on Artificial Intelligence,1022-1029.Search in Google Scholar

Laird, J. E.; Rosenbloom, P. S.; and Newell, A. 1986. Chunking in Soar: The anatomy of a general learning mechanism. Machine Learning. 1: 11-46.10.1007/BF00116249Search in Google Scholar

Langley, P.; and Choi, D. 2006. A unified cognitive architecture for physical agents. In Proceedings of the Twenty-First AAAI Conference on Artificial Intelligence, 1469-1474.Search in Google Scholar

Langley, P.; Laird, J. E.; and Rogers, S. 2009. Cognitive architectures: Research issues and challenges. Cognitive Systems Research. 10: 141-160.10.1016/j.cogsys.2006.07.004Search in Google Scholar

Lebiere, C. 2013. Summary presentation for panel on Consensus and Outstanding Issues. AAAI 2013 Fall Symposium on Integrated Cognition.Search in Google Scholar

LeCun, Y.; Bengio, Y.; and Hinton, G. E. 2015. Deep Learning. Nature. 521: 436-444.10.1038/nature1453926017442Search in Google Scholar

Lenat, D.; and Guha, R. V. 1990. Building Large Knowledge-Based Systems: Representation and Inference in the Cyc Project. Reading, MA: Addison-Wesley.Search in Google Scholar

Madl, T.; and Franklin, S. 2012. A LIDA-based Model of the Attentional Blink. In Proceedings of the 11th International Conference on Cognitive Modeling, 283-288.Search in Google Scholar

Maes, P.; and Nardi, D. eds. 1988. Meta-Level Architectures and Reflection. Amsterdam: North Holland.Search in Google Scholar

Marr, D. 1982. Vision: A Computational Investigation into the Human Representation and Processing of Visual Information. San Francisco, CA: W. H. Freeman.Search in Google Scholar

Marsella, S.; and Gratch, J. 2009. EMA: A Process Model of Appraisal Dynamics. Journal of Cognitive Systems Research. 10: 70-90.10.1016/j.cogsys.2008.03.005Search in Google Scholar

McCallum, A.; Rohanemanesh, K.; Wick, M.; Schultz, K.; and Singh, S. 2008. FACTORIE: Efficient probabilistic programming via imperative declarations of structure, inference and learning. In Proceedings of the NIPS workshop on Probabilistic Programming.Search in Google Scholar

Meyer, D. E.; and Kieras, D. E. 1997. A computational theory of executive control processes and human multiple-task performance: Part 1. Basic Mechanisms. Psychological Review. 104: 3-65.10.1037/0033-295X.104.1.3Search in Google Scholar

Mikolov, T.; Chen, K.; Corrado, G.; and Dean, J. 2013. Efficient estimation of word representations in vector space. In Proceedings of the International Conference on Learning Representations.Search in Google Scholar

Milch, B.; Marthi, B.; Russell, S.; Sontag, D.; Ong, D. L.; and Kolobov, A. 2007. BLOG: Probabilistic models with unknown objects. In Introduction to Statistical Relational Learning eds. L. Getoor and B. Taskar. Cambridge, MA: MIT Press.Search in Google Scholar

Mnih, A.; and Kavukcuoglu, K. 2013. Learning word embeddings efficiently with noise-contrastive estimation. In Advances in Neural Information Processing Systems, 2265-2273.Search in Google Scholar

Minsky, M. 1986. The Society of Mind. New York, NY: Simon & Schuster.Search in Google Scholar

Moors, A.; Ellsworth, P.C.; Scherer, K.R.; and Frijda, N.H. 2013. Appraisal theories of emotion: State of the art and future development. Emotion Review. 5: 119-124.10.1177/1754073912468165Search in Google Scholar

Murphy, K. 2002. Dynamic Bayesian Networks: Representation, Inference and Learning. Ph.D. diss., Computer Science Division, UC Berkeley, Berkeley, Calif.Search in Google Scholar

Murphy, R. R. 2000. Introduction to AI Robotics. Cambridge, MA: MIT Press.Search in Google Scholar

Newell, A. 1990. Unified Theories of Cognition. Cambridge, MA: Harvard University Press.Search in Google Scholar

Newell, A.; Shaw, J. C.; and Simon, H. A. 1959. Report on a general problem-solving program. In Proceedings of the International Conference on Information Processing, 256-264.Search in Google Scholar

Newell, A.; Yost, G. R.; Laird, J. E.; Rosenbloom, P. S.; and Altmann, E. 1991. Formulating the problem space computational model. In CMU Computer Science: A 25th Anniversary Commemorative ed. R. F. Rashid. New York, NY: ACM Press/Addison-Wesley.Search in Google Scholar

Ng, A. Y.; and Russell, S. J. 2000. Algorithms for inverse reinforcement learning. In Proceedings of the 17^th International Conference on Machine Learning, 663–670.Search in Google Scholar

Niv, Y. 2009. Reinforcement learning in the brain. The Journal of Mathematical Psychology. 53: 139-154.10.1016/j.jmp.2008.12.005Search in Google Scholar

Oaksford, M.; and Chater, N. 2007. Bayesian Rationality: The Probabilistic Approach to Human Reasoning. Oxford: Oxford University Press.10.1093/acprof:oso/9780198524496.001.0001Search in Google Scholar

O’Connor, T.; and Wong, H. Y. 2015. Emergent properties. In The Stanford Encyclopedia of Philosophy (Summer 2015 Edition), ed. E. N. Zalta.Search in Google Scholar

O’Reilly, R. C.; and Munakata, Y. 2000. Computational Explorations in Cognitive Neuroscience: Understanding the Mind by Simulating the Brain. Cambridge, MA: MIT Press.10.7551/mitpress/2014.001.0001Search in Google Scholar

Ortony, A.; Norman, D. A.; and Revelle, W. 2005. Affect and Proto-affect in effective functioning. In Who Needs Emotions? The Brain Meets the Machine : eds. J. M. Fellous and M. A. Arbib. New York, NY: Oxford University Press.10.1093/acprof:oso/9780195166194.003.0007Search in Google Scholar

Pearl, J. 1988. Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. San Francisco, CA: Morgan Kaufman.10.1016/B978-0-08-051489-5.50008-4Search in Google Scholar

Pynadath, D. V.; and Marsella, S. C. 2005. PsychSim: Modeling Theory of Mind with decision-theoretic agents. In Proceedings of the 19^th International Joint Conference on Artificial Intelligence, 1181-1186.Search in Google Scholar

Pynadath, D. V.; Rosenbloom, P. S.; and Marsella, S. C. 2014. Reinforcement learning for adaptive Theory of Mind in the Sigma cognitive architecture. In Proceedings of the 7^th Annual Conference on Artificial General Intelligence, 143-154.Search in Google Scholar

Pynadath, D. V.; Rosenbloom, P. S.; Marsella, S. C.; and Li, L. 2013. Modeling two-player games in the Sigma graphical cognitive architecture. In Proceedings of the 6th Conference on Artificial General Intelligence, 98-108. Berlin: Springer.10.1007/978-3-642-39521-5_11Search in Google Scholar

Rosenbloom, P. S. 1982. A world-championship-level Othello program. Artificial Intelligence. 19: 279-320.10.1016/0004-3702(82)90003-0Search in Google Scholar

Rosenbloom, P. S. 2006. A cognitive odyssey: From the power law of practice to a general learning mechanism and beyond. Tutorials in Quantitative Methods for Psychology. 2: 43-51.10.20982/tqmp.02.2.p043Search in Google Scholar

Rosenbloom, P. S. 2009. Towards a new cognitive hourglass: Uniform implementation of cognitive architecture via factor graphs. In Proceedings of the 9th International Conference on Cognitive Modeling, 116-121.Search in Google Scholar

Rosenbloom, P. S. 2010. Combining procedural and declarative knowledge in a graphical architecture. In Proceedings of the 10^th International Conference on Cognitive Modeling, 205-210.Search in Google Scholar

Rosenbloom, P. S. 2011a. Rethinking cognitive architecture via graphical models. Cognitive Systems Research. 12: 198-209.10.1016/j.cogsys.2010.07.006Search in Google Scholar

Rosenbloom, P. S. 2011b. Mental imagery in a graphical cognitive architecture. In Proceedings of the 2nd International Conference on Biologically Inspired Cognitive Architectures, 314-323.Search in Google Scholar

Rosenbloom, P. S. 2011c. From memory to problem solving: Mechanism reuse in a graphical cognitive architecture. In Proceedings of the 4^th Conference on Artificial General Intelligence, 143-152.Search in Google Scholar

Rosenbloom, P. S. 2012a. Deconstructing reinforcement learning in Sigma. In Proceedings of the 5th Conference on Artificial General Intelligence, 262-271. Berlin: Springer.10.1007/978-3-642-35506-6_27Search in Google Scholar

Rosenbloom, P. S. 2012b. Extending mental imagery in Sigma. In Proceedings of the 5th Conference on Artificial General Intelligence, 272-281.Search in Google Scholar

Rosenbloom, P. S. 2012c. Towards a 50 msec cognitive cycle in a graphical architecture. In Proceedings of the 11^th International Conference on Cognitive Modeling, 305-310.Search in Google Scholar

Rosenbloom, P. S. 2014. Deconstructing episodic learning and memory in Sigma. In Proceedings of the 36^th Annual Conference of the Cognitive Science Society, 1317-1322.Search in Google Scholar

Rosenbloom, P. S. 2015. Supraarchitectural capability integration: From Soar to Sigma. In Proceedings of the 13^th International Conference on Cognitive Modeling, 67-68.Search in Google Scholar

Rosenbloom, P. S.; Demski, A.; Han, T.; and Ustun, V. 2013. Learning via gradient descent in Sigma. In Proceedings of the 12th International Conference on Cognitive Modeling, 35-40.Search in Google Scholar

Rosenbloom, P. S.; Demski, A.; and Ustun, V. 2015. Efficient message computation in Sigma’s graphical architecture. Biologically Inspired Cognitive Architectures. 11: 1-9.10.1016/j.bica.2014.11.009Search in Google Scholar

Rosenbloom, P. S.; Demski, A.; and Ustun, V. 2016. Rethinking Sigma’s graphical architecture while extending it to neural networks. In Proceedings of the 9^th Conference on Artificial General Intelligence.10.1007/978-3-319-41649-6_9Search in Google Scholar

Rosenbloom, P. S.; Gratch, J.; and Ustun, V. 2015. Towards emotion in Sigma: From Appraisal to Attention. In Proceedings of the 8th Conference on Artificial General Intelligence, 142-151.Search in Google Scholar

Rosenbloom, P. S.; Laird, J. E.; and Newell, A. 1988. Meta-levels in Soar. In Meta-Level Architectures and Reflection eds. P. Maes and D. Nardi. Amsterdam, Netherlands: North Holland.Search in Google Scholar

Rosenbloom, P. S.; Lee, S.; and Unruh, A. 1990. Responding to impasses in memory-driven behavior: A framework for planning, Proceedings of the Workshop on Innovative Approaches to Planning, Scheduling, and Control, 181-191.Search in Google Scholar

Rosenbloom, P. S.; Laird, J. E.; and Newell, A. 1987. Knowledge level learning in Soar. In Proceedings of Sixth National Conference on Artificial Intelligence, 499-504.Search in Google Scholar

Rosenbloom, P. S.; Laird, J. E.; and Newell, A. eds. 1993. The Soar Papers: Research on Integrated Intelligence. Cambridge, MA: MIT Press.Search in Google Scholar

Rumelhart, D. E.; Hinton, G. E.; and Williams, R. J. 1986. Learning representations by back-propagating errors. Nature. 323: 533-536.10.1038/323533a0Search in Google Scholar

Russell, S.; Binder, J.; Koller, D.; and Kanazawa, K. 1995. Local learning in probabilistic networks with hidden variables. In Proceedings of the 14^th International Joint Conference on AI, 1146-1152.Search in Google Scholar

Schneider, W.; and Shiffrin, R. M. 1977. Controlled and automatic human information processing: I. Detection, search, and attention. Psychological Review. 84: 1-66.10.1037/0033-295X.84.1.1Search in Google Scholar

Shapiro, A. 2011. Building a character animation system. In Proceedings of the 4^th International Conference on Motion in Games, 98-109.Search in Google Scholar

Simon, H. A. 1956. Rational choice and the structure of the environment. Psychological Review. 63: 129–138.10.1037/h004276913310708Search in Google Scholar

Singla, P.; and Domingos, P. 2008. Lifted first-order belief propagation. In Proceedings of the Twenty-Third AAAI Conference on Artificial Intelligence, 1094-1099.Search in Google Scholar

Sun, R. 2006. The CLARION cognitive architecture: Extending cognitive modeling to social simulation In Cognition and Multi-Agent Interaction ed. R. Sun. New York, NY: Cambridge University Press.10.1017/CBO9780511610721.005Search in Google Scholar

Sun, R.; and Wilson, N. 2010. Motivational processes within the perception-action cycle. Perception-Action Cycle: Models, Architectures, and Hardware. New York, NY: Springer.10.1007/978-1-4419-1452-1_14Search in Google Scholar

Sutton, R. S.; and Barto, A. G. 1998. Reinforcement Learning: An Introduction. Cambridge, MA: MIT Press.10.1109/TNN.1998.712192Search in Google Scholar

Swartout, W. 2010. Lessons learned from virtual humans. AI Magazine. 31: 9-20.10.1609/aimag.v31i1.2284Search in Google Scholar

Tambe, M.; and Rosenbloom, P. S. 1994. Investigating production system representations for non-combinatorial match. Artificial Intelligence. 68: 155-199.10.1016/0004-3702(94)90097-3Search in Google Scholar

Turney, P. D.; and Pantel, P. 2010. From frequency to meaning: Vector space models of semantics. Journal of Artificial Intelligence Research. 37: 141-188.10.1613/jair.2934Search in Google Scholar

Ustun, V; and Rosenbloom, P. S. 2015. Towards adaptive, interactive virtual humans in Sigma. In Proceedings of the 15^th International Conference on Intelligent Virtual Agents, 98-108.Search in Google Scholar

Ustun, V.; Rosenbloom, P. S.; Kim, J.; and Li, L. 2015. Building high fidelity human behavior models in the Sigma cognitive architecture. In Proceedings of the 2015 Winter Simulation Conference, 3124-3125.Search in Google Scholar

Ustun, V.; Rosenbloom, P. S.; Sagae, K.; and Demski, A. 2014. Distributed vector representations of words in the Sigma cognitive architecture. In Proceedings of the 7th Annual Conference on Artificial General Intelligence, 196-207.Search in Google Scholar

Veloso, M. M.; and Carbonell, J. G. 1993. Derivational analogy in Prodigy: Automating case acquisition, storage, and utilization. Machine Learning. 10: 249–278.10.1007/978-1-4615-3228-6_3Search in Google Scholar

Veness, J.; Ng, K. S.; Hutter, M.; Uther, W.; Silver, D. 2011. A Monte-Carlo AIXI approximation. Journal of Artificial Intelligence Research. 40: 95-14210.1613/jair.3125Search in Google Scholar

Vere, S. and Bickmore, T. 1990. A Basic Agent. Computational Intelligence. 6: 41-60.10.1111/j.1467-8640.1990.tb00128.xSearch in Google Scholar

Wang, P. 2007. The logic of intelligence. In Artificial General Intelligence eds. B. Goertzel and C. Pennachin. New York, NY: Springer.Search in Google Scholar

Whiten, A. ed. 1991. Natural Theories of Mind. Oxford: Basil Blackwell.Search in Google Scholar