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Ron Sun, Ph.D.
Professor and Department HeadCognitive Science Department Rensselaer Polytechnic Institute110 Eighth Street, Carnegie BuildingTroy, New York 12180, USAEmail: Dr.Ron.Sun [at] gmail [dot] com
Web: the Google Scholar pageWeb: the PhilPapers pageWeb: the ResearchGate page
Web: the RPI faculty pageWeb: the RPI homepage Web: the CogArch Lab page
RESEARCH INTERESTS
RESEARCH INTERESTS
Cognitive Science and Computational Intelligence in general
My research interest lies in the study and modeling of cognitive agents, especially in their abilities to learn, reason, and act in the real world. More specifically, my research can be roughly categorized into the following main strands: (1) cognitive architectures, (2) connectionist and hybrid (neural-symbolic) models, as well as (3) multi-agent interaction, cognitive social simulation, and cognitive social sciences.
Cognitive Architectures
The goal of the work on cognitive architecture is two fold: to better understand human cognition (broadly defined) in various domains and to develop more unified models (cognitive architectures) for better capturing human cognition generally. Such work thus includes both psychological experiments and computational modeling and simulation.
A hybrid cognitive architecture Clarion has been continuously developed over the past three decades, which combines both implicit and explicit processes and both procedural and declarative processes in one unified framework. See Clarion
In particular, this cognitive architecture addresses the motivation-cognition-environment interaction, instead of focusing only on cognition in the narrow sense. For instance, it addresses the motivation-cognition interaction, in order to account for the relationship between motivation and performance. It also incorporates emotion, personality, morality, and so on, on the basis of motivation. The model is being used to capture, explain, and simulate a wide variety of relevant human data and phenomena.
This cognitive architecture also addresses the interaction of implicit and explicit cognition. Implicit processes have been shown to have tremendous impact on human cognition and yet they have not been taken seriously into consideration in most cognitive models. Clarion addresses not only implicit processes along side of explicit processes, but, more importantly, also their interaction in learning, reasoning, and various other activities. The resulting model is parsimonious in structure and possesses a variety of learning, reasoning, and other capabilities.
Learning in this cognitive architecture has been particularly concerned with skill acquisition in various domains, ranging from highly intellectual to sensory-motor tasks. It is accomplished, in the main, by reinforcement learning supplemented with rule induction. The model performs autonomous learning. It develops different types of representations, symbolic and subsymbolic, along side each other. The model has been used to capture, explain, and simulate a wide variety of human skill learning phenomena. See learning.
One of the technical focuses in this regard was the extraction of explicit plans (open-loop policies) based on the results of reinforcement learning, to enable explicit reasoning of plans, without a priori domain knowledge to begin with. A variety of algorithms have been explored for such plan extraction.
Another technical focus was the development of modular reinforcement learning models, in which multiple modules (or agents) compete and cooperate with each other to accomplish tasks, without a priori division of the tasks (i.e., without using any a priori domain-specific knowledge). See multi-agent learning.
Connectionist Reasoning, Knowledge Representation, and Hybrid (Neural-Symbolic) Models
Early on, the research work was mainly concerned with everyday commonsense reasoning by agents. This type of reasoning was characterized by a mixture of rule-based and similarity-based processes, exhibiting both rigor and flexibility. To capture such reasoning, a hybrid connectionist architecture (named CONSYDERR) was developed with both localist and distributed components, that unified rule-based and similarity-based processes and accounted for a variety of CSR patterns. See reasoning.
Within the framework, the following issues were also investigated: (1) The connectionist implementations of rules, logics, and schemas, and the variable binding problem in such implementations. They formed the basis for complex reasoning in connectionist models. (2) Inheritance reasoning, which is an integral part of many CSR patterns. An intensional approach was developed within CONSYDERR that works in constant time. This work suggests that other similar reasoning patterns may also be handled intensionally. (3) Causality, which is an important commonsense construct. A connectionist account was developed based on CONSYDERR, which extended the existing logic-based account and dealt better with the inexact, cumulative, and subjective nature of commonsense causal reasoning. (4) Some attempts have also been made to extend the framework to deal with metaphor and analogy.
The interest in hybrid models also lies in developing more powerful, more integrated models that are capable of autonomous learning, acquiring both symbolic and subsymbolic knowledge and utilizing their synergy to achieve better performance. Psychological models of human learning were explored, especially those concerned with integrated learning of multiple forms of knowledge, as well as machine learning and neural network techniques and theories, especially those concerned with reinforcement learning. A synthesis of these two strands of work led to advances in developing hybrid models providing new insights and impetus. See learning.
Cognitive Social Simulation and Cognitive Social Sciences
Another general area of interest is: multi-agent interaction, cognitive social simulation, and cognitive social sciences. Extending the work on cognitive architectures beyond individual agents, social interaction needs to be taken into consideration. Social simulation on the basis of cognitive architectures (i.e., cognitive social simulation) enabled the exploration and understanding of many social phenomena in relation to individual cognition. See Cognitive Social Simulation. See also Cognitive Social Sciences.
Biographical Information
ANNOUNCEMENTS
ANNOUNCEMENTS
1. To see a description of any of the books, click on a title:
The Cambridge Handbook of Computational Cognitive Sciences. Volume 1. Cambridge University Press, New York. 2023.
The Cambridge Handbook of Computational Cognitive Sciences. Volume 2. Cambridge University Press, New York. 2023.
Anatomy of the Mind. Oxford University Press, New York. 2016.
Grounding Social Sciences in Cognitive Sciences. MIT Press, Cambridge, MA. 2012.
The Cambridge Handbook of Computational Psychology. Cambridge University Press, 2008.
Cognition and Multi-Agent Interaction: From Cognitive Modeling to Social Simulation. Cambridge University Press, 2006.
Duality of the Mind. Psychology Press (Lawrence Erlbaum Associates), 2002. (or here: Duality of the Mind)
Sequence Learning: Paradigms, Algorithms, and Applications. Springer-Verlag. 2000.
Integrating Rules and Connectionism for Robust Commonsense Reasoning. John Wiley and Sons, 1994.
Hybrid Neural Systems. Springer-Verlag, Heidelberg. 2000.
Connectionist Symbolic Integration. Psychology Press (Lawrence Erlbaum Associates), 1997.
Computational Architectures Integrating Symbolic and Connectionist Processing. Kluwer Academic Publishers.
2. To see a description of the past conferences, workshops, and/or journal special issues he (co)organized, click on a title:
The Special Issue of the journal Neural Networks on Deep Reinforcement Learning, 2018.
The Workshop on Cognitive Social Sciences: Grounding the Social Sciences in the Cognitive Sciences . at CogSci 2010, Portland, Oregon. August 11, 2010. (The Proceedings.)
The 2007 International Joint Conference on Neural Networks (IJCNN 2007). Orlando, Florida. August 12-17, 2007
The Twenty-Eighth Annual Conference of the Cognitive Science Society (CogSci 2006). Vancouver, Canada. July 26-30, 2006.
The AAAI-2006 Workshop on Cognitive Modeling and Agent-based Social Simulation . Boston, MA. July, 2006.
The Symposium on the Synergy between Implicit and Explicit Learning Processes , at the Twenty-Eighth Annual Conference of the Cognitive Science Society (CogSci2006). Vancouver, Canada. 2006.
The IJCAI'03 Workshop on Cognitive Modeling of Agents and Multi-Agent Interactions, at the International Joint Conference on Artificial Intelligence, Acapulco, Mexico. 2003.
The Panel on Principles of New Connectionism, at the International Joint Conference on Neural Networks (IJCNN'2002). Honolulu, Hawaii. May, 2002.
The Interaction of Explicit and Implicit Learning: A Symposium, at the 23rd Cognitive Science Conference, August 1-4, 2001. Edinburgh, Scotland.
The ICCS'01 Symposium on Cognitive Agents and Multi-Agent Interaction, at the International Conference of Cogntive Science, Beijing, 2001.
The IJCAI'99 Workshop on Neural, Symbolic, and Reinforcement Methods for Sequence Learning, at IJCAI'99, Stockholm, 1999.
The Panel on Neural Networks and High-level Intelligence and Cognition, at the International Joint Conference on Neural Networks (IJCNN 1999). Washington, DC. 1999.
The NIPS Workshop on Hybrid Neural Symbolic Integration, at NIPS'98. 1998.
The Special Issue of IEEE TNN on Neural Networks and Hybrid Intelligent Models, 1998.
The Workshop on Computational Cognitive Modeling: The Source of Power, at AAAI'96 in Portland, Oregon. 1996.
The Workshop on Connectionist Symbolic Integration, at IJCAI'95 in Montreal, Canada. 1995.
The Workshop on Integrating Connectionist and Symbolic Processes , at AAAI'92 in San Jose, CA. 1992.
3. To see a brief description of our Ph.D program in Cognitive Science, AI and Neural Nets, click here. If you need application forms for the Ph.D program, click here.
Note that I prefer only to supervise graduate students and post-docs who know a lot about my research (i.e., read some of my papers; see below) and wish to do related work. However, I am willing to consider proposals that are slightly afield from exceptionally outstanding students.
ONLINE RESOURCES
ONLINE RESOURCES
SOME JOURNALS (of which Ron Sun serves on the editorial board):
2. HYBRID (NEURAL-SYMBOLIC) SYSTEMS RESOURCES page, which includes:
An introduction to hybrid systems by Ron Sun ("Artificial Intelligence: Symbolic and Connectionist Approaches", an entry in: the International Encyclopedia of Social and Behavioral Sciences. 2001.),
An article by Ron Sun: Hybrid systems and connectionist implementationalism (in: Encyclopedia of Cognitive Science, pp.697-703. Nature Publishing Group (MacMillan). 2002).
Hybrid List moderated by Ron Sun
Bibliography on Connectionist Symbolic Integration (edited by Ron Sun, appeared in the book Computational Architectures Integrating Symbolic and Connectionist Processing, published by Kluwer).
3. HYBRID REINFORCEMENT LEARNING RESOURCES
4. COGNITIVE ARCHITECTURES RESOURCES
5. COGNITIVE SOCIAL SIMULATION RESOURCES
6. COGNITIVE MODELING RESOURCES at Cognitive Science Society
7. OTHER RESOURCES
SELECTED PUBLICATIONS
SELECTED PUBLICATIONS
Publications by Subject Areas:
Major Journal Papers:
R. Sun et al., An integrative model of self-efficacy within a computational cognitive architecture. Cognitive Systems Research, in press. https://www.sciencedirect.com/science/article/abs/pii/S138904172500052X
R. Sun, Dual-process theories, cognitive architectures, and hybrid neural-symbolic models. Neurosymbolic Artificial Intelligence, in press. https://content.iospress.com/articles/neurosymbolic-artificial-intelligence/nai240720 https://doi.org/10.3233/nai-240720 https://neurosymbolic-ai-journal.com/paper/dual-process-theories-cognitive-architectures-and-hybrid-neural-symbolic-models-0
R. Sun, Computational cognitive architectures, dual-process theories, and LLMs. Journal of Ningbo University, Vol.38, No.2, pp.17-28. 2025. [PDF] https://doi.org/10.20098/j.cnki.1001-5132.2024.1014
R. Sun, S. Bugrov, and D. Dai, A unified framework for interpreting a range of motivation-performance phenomena. Cognitive Systems Research, Vol.71, pp.24–40. 2022. https://authors.elsevier.com/a/1dy9L4xrDwMyCV https://doi.org/10.1016/j.cogsys.2021.09.003
R. Sun, J. Allen, and E. Werbin, Modeling emotion contagion within a computational cognitive architecture. Journal of Cognition and Culture, Vol.22, No.1-2, pp.60-89. 2022. https://doi.org/10.1163/15685373-12340125
N. Wilson and R. Sun, A mechanistic account of stress-induced performance degradation. Cognitive Computation, Vol.13, No.1, pp.207-227. 2021. https://rdcu.be/b34GG https://dx.doi.org/10.1007/s12559-020-09725-5
R. Sun, Exploring culture from the standpoint of a cognitive architecture. Philosophical Psychology, Vol.33, No.2, pp.155-180. 2020. [PDF] https://doi.org/10.1080/09515089.2020.1719054
R. Sun, Full human-machine symbiosis and truly intelligent cognitive systems. AI and Society: Journal of Knowledge, Culture and Communication, Vol.35, No.1, pp 17–28. 2020. https://doi.org/10.1007/s00146-017-0775-7
R. Sun, Cognitive social simulation for policy making. Policy Insights from the Behavioral and Brain Sciences, Vol.5, No.2, pp.240-246. 2018. https://doi.org/10.1177/2372732218785925
S. Bretz and R. Sun, Two models of moral judgment. Cognitive Science, Vol.42, No.S1, pp.4-37. 2018. https://onlinelibrary.wiley.com/doi/pdf/10.1111/cogs.12517
R. Sun, Why is a computational framework for motivational and metacognitive control needed? Journal of Experimental and Theoretical Artificial Intelligence, Vol.30, No.1, pp.13-37. 2018. http://www.tandfonline.com/eprint/4GPCrukk7qzn46kWjGZF/full
R. Sun, N. Wilson, and M. Lynch, Emotion: A unified mechanistic interpretation from a cognitive architecture. Cognitive Computation, Vol.8, No.1, pp.1-14. 2016. [formatted PDF]
R. Sun, Interpreting psychological notions: A dual-process computational theory. Journal of Applied Research in Memory and Cognition, Vol.4, No.3, pp.191–196. 2015. https://www.sciencedirect.com/science/article/pii/S2211368114000783
S. Helie and R. Sun, An integrative account of memory and reasoning phenomena. New Ideas in Psychology. Vol.35, pp.36-52. 2014. [PDF] [supplemental materials].
R. Sun and N. Wilson. Roles of implicit processes: instinct, intuition, and personality. Mind and Society, Vol.13, pp.109–134. 2014. [formatted PDF]
S. Helie and R. Sun, Autonomous learning in psychologically-oriented cognitive architectures: A survey. New Ideas in Psychology. Vol.34, pp.37–55. 2014. [formatted PDF]
R. Sun and N. Wilson, A model of personality should be a cognitive architecture itself. Cognitive Systems Research, Vol.29–30, pp.1–30. 2014. [formatted PDF]
R. Sun, Moral judgment, human motivation, and neural networks. Cognitive Computation, Vol.5, No.4, pp.566-579. 2013.
R. Sun, Autonomous generation of symbolic representations through subsymbolic activities. Philosophical Psychology, Vol.26, No.6, pp.888-912. 2013. [formatted PDF]
R. Sun and S. Helie, Psychologically realistic cognitive agents: Taking human cognition seriously. Journal of Experimental and Theoretical Artificial Intelligence, Vol.25, pp.65-92. 2013. [PDF]
R. Sun and P. Fleischer, A cognitive social simulation of tribal survival strategies: The importance of cognitive and motivational factors. Journal of Cognition and Culture, Vol.12, No.3-4, pp.287-321, 2012. [formatted PDF]
R. Sun and R. Mathews, Implicit cognition, emotion, and meta-cognitive control. Mind and Society (the special issue on Dual Processes Theories of Language and Thinking), Vol.11, No.1, pp.107-119. 2012.
R. Sun, Memory Systems within a Cognitive Architecture. New Ideas in Psychology, Vol.30, pp.227-240. 2012. [formatted PDF]
R. Sun, N. Wilson, and R. Mathews, Accounting for certain mental disorders within a comprehensive cognitive architecture. Cognitive Computation, Vol.3, No.2, pp.341-359. 2011.
R. C. Mathews, J. Tall, S. M. Lane, and R. Sun, Getting it right generally, but not precisely: learning the relation between multiple inputs and outputs. Memory and Cognition, Vol.39, No.6, pp.1133-1145. 2011. [PDF]
S. Helie and R. Sun, Incubation, insight, and creative problem solving: A unified theory and a connectionist model. Psychological Review, Vol.117, No.3, pp.994-1024. 2010. [formatted PDF] [Web]
R. Sun, Motivational representations within a computational cognitive architecture. Cognitive Computation, Vol.1, No.1, pp.91-103. 2009. [PDF]
N. Wilson, R. Sun, and R. Mathews, A motivationally-based simulation of performance degradation under pressure . Neural Networks, Vol.22, pp.502-508. 2009. [formatted PDF]
R. Sun, Theoretical status of computational cognitive modeling . Cognitive Systems Research, Vol.10, No.2, pp.124-140. 2009. [formatted PDF]
R. Sun, X. Zhang, and R. Mathews, Capturing human data in a letter counting task: Accessibility and action-centeredness in representing cognitive skills . Neural Networks, Vol.22, pp.15-29. 2009. [formatted PDF]
S. Lane, R. Mathews, B. Sallas, R. Prattini, and R. Sun, Facilitative interactions of model- and experience-based processes: Implications for type and flexibility of representation . Memory and Cognition, Vol.36, No.1, pp.157-169. 2008. [PDF]
B. Sallas, R. Mathews, S. Lane, and R. Sun, Developing rich and quickly accessed knowledge of an artificial grammar . Memory and Cognition, Vol.35, No.8, pp.2118-2133. 2007. [PDF]
R. Sun and I. Naveh, Social institution, cognition, and survival: A cognitive-social simulation . Mind and Society, Vol.6, No.2, pp.115-142. 2007. [PDF]
R. Sun, X. Zhang, P. Slusarz, and R. Mathews, The interaction of implicit learning, explicit hypothesis testing learning, and implicit-to-explicit knowledge extraction . Neural Networks, Vol.20, No.1, pp.34-47. 2007. [Elsevier formatted PDF]
R. Sun, The importance of cognitive architectures: An analysis based on CLARION. Journal of Experimental and Theoretical Artificial Intelligence, Vol.19, No.2, pp.159-193. 2007. [PDF]
R. Sun and X. Zhang, Accounting for a variety of reasoning data within a cognitive architecture. Journal of Experimental and Theoretical Artificial Intelligence, Vol.18, No.2, pp.169-191. 2006. [PDF]
R. Sun, X. Zhang, and R. Mathews, Modeling meta-cognition in a cognitive architecture. Cognitive Systems Research, Vol.7, No.4, pp.327-338. 2006. [Elsevier formatted PDF]
I. Naveh and R. Sun, A cognitively based simulation of academic science . Computational and Mathematical Organization Theory, Vol.12, pp.313-337. 2006. [PDF]
R. Sun, P. Slusarz, and C. Terry, The interaction of the explicit and the implicit in skill learning: A dual-process approach . Psychological Review, Vol.112, No.1, pp.159-192. 2005. [formatted PDF]
R. Sun, L. A. Coward, and M. J. Zenzen, On levels of cognitive modeling . Philosophical Psychology, Vol.18, No.5, pp.613-637. 2005. [Formatted PDF]
R. Sun and D. Qi, MARLBS: Team cooperation through bidding . International Journal of Computational Intelligence Research, Vol.1, No.1, pp.42-58. 2005. [formatted PDF]
D. Qi and R. Sun, Learning to cooperate in solving the traveling salesman problem. International Journal of Neural Systems, Vol.15, No.1&2, pp.151-162. 2005.
T. Domangue, R. Mathews, R. Sun, L. Roussel, and C. Guidry, The effects of model-based and memory-based processing on speed and accuracy of grammar string generation . Journal of Experimental Psychology: Learning, Memory, and Cognition, 30 (5), pp.1002-1011. 2004. [formatted PDF]
R. Sun, Desiderata for cognitive architectures . Philosophical Psychology, Vol.17, No.3, pp.341-373. 2004. [formatted PDF]
L. A. Coward and R. Sun, Criteria for an effective theory of consciousness and some preliminary attempts . Consciousness and Cognition, Vol.13, pp. 268-301. 2004. [formatted PDF]
R. Sun and I. Naveh, Simulating organizational decision-making using a cognitively realistic agent model . Journal of Artificial Societies and Social Simulation, Vol.7, No.3, June, 2004. [ http://jasss.soc.surrey.ac.uk/7/3/5.html ]
R. Sun and X. Zhang, Top-down versus bottom-up learning in cognitive skill acquisition . Cognitive Systems Research, Vol.5, No.1, pp.63-89, March 2004. [Elsevier-formatted PDF]
D. Qi and R. Sun, A multi-agent system integrating reinforcement learning, bidding and genetic algorithms. Web Intelligence and Agent Systems, Vol.1, No.3-4, pp.187-202. 2003.
A. Browne and R. Sun, Connectionist inference models. Neural Networks, Vol.14, No.10, pp.1331-1355, December 2001. [Elsevier-formatted PDF]
R. Sun, E. Merrill, and T. Peterson, From implicit skills to explicit knowledge: A bottom-up model of skill learning. Cognitive Science, Vol.25, No.2, pp.203-244. 2001. [Elsevier-formatted PDF]
R. Sun, Computation, reduction, and teleology of consciousness. Cognitive Systems Research, Vol.1, No.4, pp.241-249. 2001.
R. Sun, Cognitive science meets multi-agent systems: A prolegomenon. Philosophical Psychology, Vol.14, No.1, pp.5-28. 2001. [formatted PDF]
R. Sun and C. Sessions, Learning plans without a priori knowledge. Adaptive Behavior, Vol.8, No.3/4, pp.225-253. 2000. [formatted PDF]
R. Sun and C. Sessions, Self-segmentation of sequences: Automatic formation of hierarchies of sequential behaviors. IEEE Transactions on Systems, Man, and Cybernetics: Part B Cybernetics, Vol.30, No.3, pp.403-418. 2000. [PDF]
R. Sun, Symbol grounding: A new look at an old idea. Philosophical Psychology, Vol.13, No.2, pp.149-172. 2000. [formatted PDF]
R. Sun and T. Peterson, Multi-agent reinforcement learning: Weighting and partitioning. Neural Networks, Vol.12, No.4-5. pp.127-153. 1999. [Elsevier-formatted PDF]
R. Sun, T. Peterson, and E. Merrill, A hybrid architecture for situated learning of reactive sequential decision making. Applied Intelligence, Vol.11, pp.109-127. 1999.
R. Sun, Accounting for the computational basis of consciousness: A connectionist approach. Consciousness and Cognition, Vol.8, pp.529-565. December, 1999. [formatted PDF]
A. Browne and R. Sun, Connectionist variable binding. Expert Systems, Vol.16, No.3, pp.189-207. 1999. [PDF]
R. Sun, Computational models of consciousness: An evaluation. Journal of Intelligent Systems, Vol.9, pp.507-562. 1999 [formatted PDF]
R. Sun and T. Peterson, Autonomous learning of sequential tasks: Experiments and analyses. IEEE Transactions on Neural Networks, Vol.9, No.6, pp.1217-1234. November, 1998. [PDF]
R. Sun and T. Peterson, Some experiments with a hybrid model for learning sequential decision making. Information Sciences. vol.111, pp.83-107. 1998.
R. Sun, Learning, action, and consciousness: A hybrid approach towards modeling consciousness. Neural Networks, special issue on consciousness. 10 (7), pp.1317-1331. 1997. [Elsevier-formatted PDF]
R. Sun, Commonsense reasoning with rules, cases, and connectionist models: A paradigmatic comparison. Fuzzy Sets and Systems, Vol.82, pp.187-200, 1996. [Elsevier-formatted PDF]
R. Sun, Robust reasoning: Integrating rule-based and similarity-based reasoning. Artificial Intelligence (AIJ). Vol.75, No.2, pp.241-296. June, 1995. [Elsevier-formatted PDF]
R. Sun, A new approach towards modeling causality in commonsense reasoning. International Journal of Intelligent Systems, Vol. 10, No. 3. March, 1995. [formatted PDF]
R. Sun, Structuring knowledge in vague domains. IEEE Transactions on Knowledge and Data Engineering, Vol. 7, No. 1. pp. 120-136. Feb., 1995. [formatted PDF]
R. Sun, On schemas, logics, and neural assemblies. Applied Intelligence, Vol. 5, No. 2. pp. 83-102. 1995 (an invited paper for the special issue on high-level connectionist models). [formatted PDF]
R. Sun, A neural network model of causality. IEEE Transactions on Neural Networks, Vol. 5, No. 4. pp. 604-611. July, 1994. [formatted PDF]
R. Sun, An efficient feature-based connectionist inheritance scheme. IEEE Transactions on System, Man, and Cybernetics, Vol. 23, No. 1. pp. 23-54. 1993. [formatted PDF]
R. Sun, On variable binding in connectionist networks. Connection Science, Vol. 4, No. 2. pp. 93-124. 1992. [formatted PDF]
R. Sun, Beyond associative memories: Logics and variables in connectionist networks. Information Sciences, Special Issue on AI and Neural Networks, Vol. 70, No. 1&2. 1992. [PDF]
R. Sun, A connectionist model for commonsense reasoning incorporating rules and similarities. Knowledge Acquisition, Vol. 4. pp. 293-321. 1992.
R. Sun, Connectionist models of rule-based reasoning. AISB Quarterly, Special Issue on Hybrid Systems, No. 79. pp. 21-24. 1992.
Books:
R. Sun, The Cambridge Handbook of Computational Cognitive Sciences. Cambridge University Press, New York. 2023.
R. Sun, Anatomy of the Mind. Oxford University Press, New York. 2016.
R. Sun (ed.), Grounding Social Sciences in Cognitive Sciences. MIT Press, Cambridge, MA. 2012.
R. Sun (ed.), The Cambridge Handbook of Computational Psychology. Cambridge University Press, New York. 2008.
R. Sun, Cognition and Multi-Agent Interaction: From Cognitive Mdoeling to Social Simulation. Cambridge University Press, New York. 2006.
R. Sun, Duality of the Mind. Lawrence Erlbaum Associates, Mahwah, NJ. 2002.
R. Sun and L. Giles, (eds.) Sequence Learning: Paradigms, Algorithms, and Applications. Springer-Verlag, Heidelberg. 2000.
S. Wermter and R. Sun, (eds.) Hybrid Neural Systems. Springer-Verlag, Heidelberg. 2000.
R. Sun and F. Alexandre, (eds.) Connectionist-Symbolic Integration. Lawrence Erlbaum Associates, Mahwah, NJ. 1997.
R. Sun, Integrating Rules and Connectionism for Robust Commonsense Reasoning. John Wiley and Sons, New York. 1994.
R. Sun & L. Bookman, (eds.), Computational Architectures Integrating Neural and Symbolic Processes. Kluwer Academic Publishers, Needham, MA. 1994.
General Overviews:
R. Sun, Cognitive Modeling. In P. Atkinson, S. Delamont, A. Cernat, J.W. Sakshaug, & R.A. Williams (Eds.), SAGE Research Methods Foundations. 2020. http://dx.doi.org/10.4135/9781526421036869642
R. Sun, The CLARION cognitive architecture: Towards a comprehensive theory of the mind. In: S. Chipman (ed.), The Oxford Handbook of Cognitive Science. pp.117-133. Oxford University Press, New York. 2017.
R. Sun, Rationality and the true human condition. In: R. Frantz and L. Marsh (eds.), Minds, Models and Milieux: Commemorating the Centennial of the Birth of Herbert Simon. pp.71-90. Palgrave MacMillan, London, UK. 2016. [PDF]
S. Helie and R. Sun, Autonomous learning in psychologically oriented cognitive architectures: A survey. New Ideas in Psychology. Vol.34, pp.37–55. 2014.
R. Sun and S. Bringsjord, Cognitive systems and cognitive architectures. In: B. Wah (ed.), Encyclopedia of Computer Science and Engineering. Volume 1, pp.420-428. John Wiley and Sons, New York. 2009.
R. Sun, Cognitive Architectures and multi-agent social simulation. In: D. Lukose and Z. Shi (eds.), Multi-Agent Systems for Society (Lecture Notes in Artificial Intelligence, Volume 4078), pp.7-21. Springer-Verlag, Berlin. 2009.
R. Sun, Introduction to computational cognitive modeling. In: R. Sun (ed.), The Cambridge Handbook of Computational Psychology, pp.3-19. Cambridge University Press, New York. 2008.
R. Sun, Cognitive social simulation. In: R. Sun (ed.), The Cambridge Handbook of Computational Psychology, pp.530-548. Cambridge University Press, New York. 2008.
R. Sun, Cognitive social simulation incorporating cognitive architectures . IEEE Intelligent Systems, Vol.22, No.5, pp.33-39. September/October, 2007.
R. Sun, Hybrid systems and connectionist implementationalism. Encyclopedia of Cognitive Science, MacMillan Publishing Company, 2001.
R. Sun, Artificial intelligence: Connectionist and symbolic approaches. In: N. J. Smelser and P. B. Baltes (eds.), International Encyclopedia of the Social and Behavioral Sciences. pp.783-789. Pergamon/Elsevier, Oxford. 2001. [PDF]
R. Sun Individual action and collective function (an editorial). Cognitive Systems Research, Vol.2, No.1, 2001. [PDF] [Elsevier-formatted PDF]
R. Sun and L. Giles, Sequence learning: From prediction and recognition to sequential decision making. IEEE Intelligent Systems, Vol.16, No.4, pp.67-70. July/August, 2001. [formatted PDF]
R. Sun, Introduction to sequence learning. In: R. Sun and L. Giles, (eds.) Sequence Learning: Paradigms, Algorithms, and Applications. Springer-Verlag, Heidelberg, 2000.
S. Wermter and R. Sun, An overview of hybrid neural systems . (PDF) In: S. Wermter and R. Sun, (eds.) Hybrid Neural Systems. Springer-Verlag, Heidelberg. 2000.
R. Sun, V. Honavar and G. Oden, Integration of cognitive systems across disciplinary boundaries (an editorial). Cognitive Systems Research, Vol.1, No.1, pp.1-3. 1999. [Elsevier-formatted PDF]
R. Sun, Artificial intelligence. In: W. Bechtel and G. Graham, (eds.) A Companion to Cognitive Science, Blackwell Publishers, 1998.
R. Sun, Introduction to connectionist symbolic integration. In: R. Sun and F. Alexandre, (eds.) Connectionist-Symbolic Integration. Lawrence Erlbaum Associates. 1997.
R. Sun, and C. Ling, Computational cognitive modeling, the source of power and other related issues. AI Magazine. 19 (2), 113-120. 1997.
R. Sun, Hybrid connectionist models. AI Magazine. 17 (2), pp.99-103, Summer 1996.
R. Sun, On neural networks and symbolic processing. In: R. Sun and L. Bookman, (eds.) Computational Architectures Integrating Neural and Symbolic Processes. Kluwer Academic Publishers. 1994.
R. Sun and L. Bookman, How do symbols and networks fit together? Artificial Intelligence magazine. pp. 20-23. Summer, 1993.
L. Bookman and R. Sun, Integrating neural and symbolic processes (an editorial). Connection Science, special issue on integrating neural and symbolic processes, Vol. 5, No. 3-4. 1993.
Some Other Useful Papers:
C. S. Mekik, R. Sun, and D. Y. Dai, Similarity-based reasoning, Raven's matrices, and general intelligence. Proceedings of International Joint Conference on Artificial Intelligence and European Conference on Artificial Intelligence (IJCAI-ECAI 2018). Stockholm, Sweden. pp.1576-1582. July 13-19, 2018.
J. Allen and R. Sun, Emotion contagion in a cognitive architecture. In: Y. Jin and S. Kollias (eds.), Proceedings of IEEE Symposium Series in Computational Intelligence (SSCI 2016). Athens, Greece. December 6-9, 2016. Published by IEEE Press, Piscataway, NJ. 2016.
J. Licato, R. Sun, and S. Bringsjord, Structural representation and reasoning in a hybrid cognitive architecture. Proceedings of the 2014 International Joint Conference on Neural Networks. IEEE Press, Piscataway, NJ. 2014.
J. Licato, R. Sun, and S. Bringsjord, Using a hybrid cognitive architecture to model children’s errors in an analogy task. Proceedings of the Annual Conference of Cognitive Science Society, Quebec City, Quebec, Canada. July, 2014. pp.857-862. Published by Cognitive Science Society, Austin, Texas. 2014.
N. Wilson and R. Sun, Coping with bullying: A computational emotion-theoretic account. Proceedings of the Annual Conference of Cognitive Science Society, Quebec City, Quebec, Canada. pp.3119-3124. Published by Cognitive Science Society, Austin, Texas. July, 2014.
S. Helie, R. Sun, and L. Xiong, Mixed effects of distractor tasks on incubation. Proceedings of the 2008 Annual Conference of the Cognitive Science Society, Washington, DC. pp.1251-1256. Published by the Cognitive Science Society. July, 2008.
R. Sun, R. Mathews, and S. Lane, Implicit and explicit processes in the development of cognitive skills: A theoretical interpretation with some practical implications for science education. In: E. Vargios (ed.), Educational Psychology Research Focus, pp.1-26. Nova Science Publishers, Hauppauge, NY. 2007.
R. Sun, X. Zhang, and R. Mathews, Modeling meta-cognition in a cognitive architecture. Proceedings of the 27th Annual Conference of the Cognitive Science Society, Stresa, Italy. Lawrence Erlbaum Associates, Mahwah, NJ. 2005.
R. Sun, Meta-learning processes in multi-agent systems. In: Intelligent Agent Technology (IAT-2001). pp.210-219. World Scientific, Singapore. [PDF] [PDF]
R. Sun and D. Qi, Rationality assumptions and optimality of co-learning. Proceedings of PRIMA'2000, Lecture Notes in Artificial Intelligence, Springer-Verlag, Heidelberg, Germany. 2000. [PDF] [PDF]
R. Sun, A microfeature-based approach toward metaphor interpretation. Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI 1995). pp.424-430. Morgan Kaufmann, San Francisco, CA. 1995. [PDF]
R. Sun, Connectionist models of rule-based reasoning. Proceedings of the 13th Cognitive Science Society Conference, pp.437-442. Lawrence Erlbaum Associates, Hillsdale, NJ. 1991. [the 1991 David Marr Award] [PDF]
R. Sun, The discrete neuronal model and the probabilistic discrete neuronal model. In: B. Soucek (ed.), Neural and Intelligent Systems Integration, pp.161-178. John Wiley and Sons, New York. 1991. [PDF]
To download the papers, try also the ftp site.
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