Alexandr Ezhov and Dan Ventura, "Quantum Neural Networks", in Future Directions for Intelligent Systems and Information Science (Ed. N. Kasabov), Physica-Verlag, 2000.

Jeffrey S. Whiting, Jonathan Dinerstein, Paris K. Egbert and Dan Ventura, "Cognitive and Behavioral Model Ensembles for Autonomous Virtual Characters”, Computational Intelligence, in press, 2009.

Jonathan Dinerstein, Parris Egbert, Dan Ventura and Michael Goodrich, "Demonstration-based Behavior Programming for Embodied Virtual Agents", Computational Intelligence, 24(4): 235-256, 2008.

Kaivan Kamali, Dan Ventura, Amulya Garga and Soundar Kumara, "Geometric Task Decomposition in a Multi-agent Environment", Applied Artificial Intelligence, 20(5): 437-456, 2006.

Jonathan Dinerstein, Dan Ventura and Parris Egbert, "Fast and Robust Incremental Action Prediction for Interactive Agents", Computational Intelligence, 21(1): 90-110, 2005.

John Howell, John Yeazell and Dan Ventura, "Optically Simulating a Quantum Associative Memory", Physical Review A, vol. 62, article 42303, 2000.

Alexandr Ezhov, A. Nifanova, and Dan Ventura, "Distributed Queries for Quantum Associative Memory", Information Sciences, vol. 128 nos. 3-4, pp. 271-293, 2000.

Dan Ventura and Tony Martinez, "Quantum Associative Memory", Information Sciences, vol. 124 nos. 1-4, pp. 273-296, 2000.

Dan Ventura and Tony Martinez, "Initializing the Amplitude Distribution of a Quantum State", Foundations of Physics Letters, vol. 12 no. 6, pp. 547-559, 1999.

Dan Ventura, "Quantum Computational Intelligence: Answers and Questions", IEEE Intelligent Systems, vol. 14 no. 4, pp. 14-16, 1999.

Dan Ventura, “A Sub-symbolic Model of the Cognitive Processes of Re-representation and Insight”, Proceedings of the ACM Conference on Creativity and Cognition, pp. 409-410, 2009 (a longer version of this first appeared in Creative Intelligent Systems: Papers from the AAAI Spring Symposium, 2008).

Adam Drake and Dan Ventura, "Search Techniques for Fourier-Based Learning", Proceedings of the International Joint Conference on Artificial Intelligence, pp. 1040-1045, 2009 (this first appeared in Search in Artificial Intelligence and Robotics: Papers from the AAAI Workshop, 2008).

Ilya Raykhel and Dan Ventura, "Real-time Automatic Price Prediction for eBay Online Trading", Proceedings of the Innovative Applications of Artificial Intelligence Conference, pp. 135-140, 2009.

Neil Toronto, Bryan Morse, Kevin Seppi and Dan Ventura, "Super-Resolution via Recapture and Bayesian Effect Modeling", Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, to appear, 2009.

David Norton and Dan Ventura, "Improving the Separability of a Reservoir Facilitates Learning Transfer", Proceedings of the International Joint Conference on Neural Networks, pp.2288-2293, 2009 (this first appeared in Transfer Learning for Complex Tasks: Papers from the AAAI Workshop, 2008).

Kyle Dickerson and Dan Ventura, "Using Self-Organizing Maps to Implicitly Model Preference for a Musical Query-by-Content System", Proceedings of the International Joint Conference on Neural Networks, pp. 705-710, 2009.

Dan Ventura, "A Reductio Ad Absurdum Experiment in Sufficiency for Evaluating (Computational) Creative Systems", Proceedings of the International Joint Workshop on Computational Creativity, pp. 11-19, 2008.

Heather Chan and Dan Ventura, “Automatic Composition of Themed Mood Pieces”, Proceedings of the International Joint Workshop on Computational Creativity, pp. 109-115, 2008.

Adam Drake, Eric Ringger and Dan Ventura, "Sentiment Regression: Using Real-Valued Scores to Summarize Overall Document Sentiment", Proceedings of the IEEE International Conference on Semantic Computing, pp. 152-157, 2008.

Robert D. Van Dam, Irene Geary and Dan Ventura, "Adapting ADtrees for High Arity Features", Proceedings of the Association for the Advancement of Artificial Intelligence, pp. 708-713, 2008.

Jonathan Dinerstein, Parris Egbert,Dan Ventura and Michael Goodrich "Data-Driven Programming and Behavior for Autonomous Virtual Characters", Proceedings of the Association for the Advancement of Artificial Intelligence, pp. 1450-1451, 2008.

Dan Ventura, “Sub-symbolic Re-representation to Facilitate Learning Transfer”, Creative Intelligent Systems: Papers from the AAAI Spring Symposium, Technical Report SS-08-03, pp. 128-134, 2008.

Mike Gashler, Tony Martinez and Dan Ventura, "Iterative Non-linear Dimensionality Reduction with Manifold Sculpting", Neural Information Processing Systems 20, pp. 513-520, 2008.

Rob Van Dam and Dan Ventura, "ADtrees for Sequential Data and N-gram Counting", Proceedings of the IEEE International Conference on Systems, Man and Cybernetics, pp. 492-497, 2007.

Sabra Dinerstein, Jonathan Dinerstein and Dan Ventura, "Robust Multi-Modal Biometric Fusion via SVM Ensemble", Proceedings of the IEEE International Conference on Systems, Man and Cybernetics, pp. 1530-1535, 2007.

Jared Lundell and Dan Ventura, "A Data-dependent Distance Measure for Transductive Instance-based Learning", Proceedings of the IEEE International Conference on Systems, Man and Cybernetics, pp. 2825-2830, 2007.

Neil Toronto, Bryan Morse, Dan Ventura, Kevin Seppi, "The Hough Transform's Implicit Bayesian Foundation", Proceedings of the IEEE International Conference on Image Processing, pp. 377-380, 2007.

Jake Merrell, Dan Ventura and Bryan Morse, "Clustering Music via the Temporal Similarity of Timbre", IJCAI Workshop on Artificial Intelligence and Music, pp. 153-164, 2007.

Nancy Fulda and Dan Ventura, "Predicting and Preventing Coordination Problems in Cooperative Q-learning Systems", Proceedings of the International Joint Conference on Artificial Intelligence, pp. 780-785, 2007.

Jonathan Dinerstein, Dan Ventura and Parris Egbert, "Learning Policies for Embodied Virtual Agents Through Demonstration", Proceedings of the International Joint Conference on Artificial Intelligence, 1257-1262, 2007.

Eric Goodman and Dan Ventura, "Spatiotemporal Pattern Recognition via Liquid State Machines", Proceedings of the International Joint Conference on Neural Networks, pp. 7579-7584, July 2006.

Neil Toronto and Dan Ventura, "Learning Quantum Operators from Quantum State Pairs", Proceedings of the IEEE Congress on Evolutionary Computation, pp. 9157-9162, July 2006.

David Norton and Dan Ventura, "Preparing More Effective Liquid State Machines Using Hebbian Learning", Proceedings of the International Joint Conference on Neural Networks, pp. 8359-8364, July 2006.

Nancy Fulda and Dan Ventura, "Learning a Rendezvous Task with Dynamic Joint Action Perception", Proceedings of the International Joint Conference on Neural Networks, pp. 627-632, July 2006.

Adam Drake and Dan Ventura, "A Practical Generalization of Fourier-based Learning", Proceedings of the International Conference on Machine Learning, pp. 185-192, August 2005.

Neil Toronto, Dan Ventura and Bryan Morse, "Edge Inference for Image Interpolation", Proceedings of the International Joint Conference on Neural Networks, pp. 1782-1787, July 2005.

Eric Goodman and Dan Ventura, "Effectively Using Recurrently Connected Spiking Neural Networks", Proceedings of the International Joint Conference on Neural Networks, pp. 1542-1547, July 2005.

Adam Drake and Dan Ventura, "Comparing High-Order Binary Features", Proceedings of the Joint Conference on Information Sciences, pp. 428-431, July 2005.

Eric Goodman and Dan Ventura, "Time Invariance and Liquid State Machines", Proceedings of the Joint Conference on Information Sciences, pp. 420-423, July 2005.

Nancy Fulda and Dan Ventura, "Incremental Policy Learning: An Equilibrium Selection Algorithm for Reinforcement Learning Agents with Common Interests", Proceedings of the International Joint Conference on Neural Networks, pp. 1121-1126, July 2004.

Mark Richards and Dan Ventura, "Choosing a Starting Configuration for Particle Swarm Optimization", Proceedings of the International Joint Conference on Neural Networks, pp. 2309-2312, July 2004.

Stephen Whiting and Dan Ventura, "Learning Multiple Correct Classifications from Incomplete Data using Weakened Implicit Negatives", Proceedings of the International Joint Conference on Neural Networks, pp. 2953-2958, July 2004.

Bob Ricks and Dan Ventura, "Training a Quantum Neural Network", Neural Information Processing Systems 16, pp. 1019-1026, December 2003.

Nancy Fulda and Dan Ventura, "Target Sets: A Tool for Understanding and Predicting the Behavior of Interacting Q-learners", Proceedings of the International Joint Conference on Information Sciences, pp. 1549-1552, September 2003.

Mark Richards and Dan Ventura, "Dynamic Sociometry in Particle Swarm Optimization", Proceedings of the International Joint Conference on Information Sciences, pp. 1557-1560, September 2003.

Nancy Fulda and Dan Ventura, "Dynamic Joint Action Perception for Q-Learning Agents", Proceedings of the International Conference on Machine Learning and Applications, pp. 73-78, June 2003.

Nancy Fulda and Dan Ventura, "Concurrently Learning Neural Nets: Encouraging Optimal Behavior in Cooperative Reinforcement Learning Systems", Proceedings of the IEEE International Workshop on Soft Computing Techniques in Instrumentation, Measurement, and Related Applications, pp. 2-5, May 2003.

Dan Ventura, "Probabilistic Connections in Relaxation Networks", Proceedings of the International Joint Conference on Neural Networks, pp.934-938, May 2002.

Dan Ventura, "Pattern Classification Using a Quantum System", Proceedings of the Joint Conference on Information Sciences, pp.537-640, March 2002.

Dan Ventura, "A Quantum Analog to Basis Function Networks", Proceedings of the International Conference on Computing Anticipatory Systems, pp. 286-295, August 2001.

Dan Ventura, "On the Utility of Entanglement in Quantum Neural Computing", Proceedings of the International Joint Conference on Neural Networks, pp. 1565-1570, July 2001.

Dan Ventura, "Learning Quantum Operators", Proceedings of the Joint Conference on Information Sciences, pp. 750-752, March 2000.

Dan Ventura, "Implementing Competitive Learning in a Quantum System", Proceedings of the International Joint Conference on Neural Networks, paper 513 (CD-ROM), July 1999.

Dan Ventura, D. Randall Wilson, Tony Martinez and Brian Moncur, "A Neural Model of Centered Tri-gram Speech Recognition", Proceedings of the International Joint Conference on Neural Networks, paper 2188 (CD-ROM), July 1999.

D. Randall Wilson, Dan Ventura, Tony Martinez and Brian Moncur, "The Robustness of Relaxation Rates in Constraint Satisfaction Networks", Proceedings of the International Joint Conference on Neural Networks, paper 162 (CD-ROM), July 1999.

Dan Ventura and Tony Martinez, "A Quantum Associative Memory Based on Grover's Algorithm", Proceedings of the International Conference on Artificial Neural Networks and Genetic Algorithms, pp. 22-27, April 1999.

Dan Ventura, "Artificial Associative Memory using Quantum Processes", Proceedings of the Joint Conference on Information Sciences, vol. 2, pp. 218-221, October 1998.

Dan Ventura and Tony Martinez, "Quantum Associative Memory with Exponential Capacity", Proceedings of the International Joint Conference on Neural Networks, pp. 509-513, May 1998.

Dan Ventura and Tony Martinez, "Optimal Control Using a Neural/Evolutionary Hybrid System", Proceedings of the International Joint Conference on Neural Networks, pp. 1036-1041, May 1998.

Dan Ventura and Tony Martinez, "Using Evolutionary Computation to Facilitate Development of Neurocontrol", Proceedings of the International Workshop on Neural Networks and Neurocontrol, August 1997.

Dan Ventura and Tony Martinez, "An Artificial Neuron with Quantum Mechanical Properties", Proceedings of the International Conference on Artificial Neural Networks and Genetic Algorithms, pp. 482-485, April 1997.

Dan Ventura and Tony Martinez, "A General Evolutionary/Neural Hybrid Approach to Learning Optimization Problems", Proceedings of the World Congress on Neural Networks, pp. 1091-5, September 1996.

Dan Ventura and Tony Martinez, "Concerning a General Framework for the Development of Intelligent Systems", Proceedings of the International Conference on Artificial Intelligence, Expert Systems and Neural Networks, pp. 44-47, August 1996.

Dan Ventura and Tony Martinez, "Robust Optimization Using Training Set Evolution", Proceedings of the International Conference on Neural Networks, pp. 524-8, June 1996.

Dan Ventura, Tim Andersen and Tony Martinez, "Using Evolutionary Computation to Generate Training Set Data for Neural Networks", Proceedings of the International Conference on Artificial Neural Networks and Genetic Algorithms, pp. 468-471, April 1995.

Dan Ventura and Tony Martinez, "An Empirical Comparison of Discretization Methods", Proceedings of the International Symposium on Computer and Information Sciences, pp. 443-450, November 1995.

Dan Ventura and Tony Martinez, "Using Multiple Statistical Prototypes to Classify Continuously Valued Data", Proceedings of the International Symposium on Neuroinformatics and Neurocomputers, pp. 238-45, September 1995.

Dan Ventura and Tony Martinez, "BRACE: A Paradigm For the Discretization of Continuously Valued Data", Proceedings of the Florida Artificial Intelligence Research Symposium, pp. 117-121, May 1994.

Marcello Balduccini, Chitta Baral, Boyan Brodaric, Simon Colton, Peter Fox, David Gutelius, Knut Hinkelmann, Ian Horswill, Bernardo Huberman, Eva Hudlicka, Kristina Lerman, Christine Lisetti, Deborah McGuinness, Mary Lou Maher, Mark A. Musen, Mehran Sahami, Derek Sleeman, Barbara Thönssen, Juan Velasquez, and Dan Ventura, "Reports of the AAAI 2008 Spring Symposia", AI Magazine 29(3), pp. 107-115, 2008.

Dan Ventura, Mary Lou Maher and Simon Colton (editors), Creative Intelligent Systems: Papers from the AAAI Spring Symposium, Technical Report SS-08-03, AAAI Press, 2008

Steve Bair, Uday Chakraborty, Shu-Heng Chen, Heng-Da Cheng, David K.Y. Chiu, Sanjoy Das, Grit Denker, Richard Duro, Manuel Grana Romay, Donald Hung, Etienne Kerre, Hong Va Leong, Chang-Tien Lu, Jie Lu, Liam Maguire, Chong Wah Ngo, M. Sarfraz, Chris Tseng, Shusaku Tsumoto, Dan Ventura, Paul P. Wang, Xin Yao, C.N. Zhang, Kaizhong Zhang (Editors), Proceedings of the Eighth Joint Conference on Information Sciences, July 2005.

Ken Chen, Shu-Heng Chen, Heng-Da Cheng, David K.Y. Chin, Sanjoy Das, Richard Duro, Zhen Jiang, Nik Kasabov, Etienne Kerre, Hong Va Leong, Qing Li, Mi Lu, Manuel Grana Romay, Dan Ventura, Paul P. Wang, Jie Wu (Editors), Proceedings of the Seventh Joint Conference on Information Sciences, September 2003.

Dan Ventura and Tony Martinez, "Kvantovaya Accotsiativnaya Pamyat'", Neirokomp'yutery: razrabotka i primenenie, N9-10, pp. 34-53, 2002 (Russian translation of "Quantum Associative Memory" in the journal Neurocomputers: development and applications).

H. John Caulfield, Shu-Heng Chen, Heng-Da Cheng, Richard Duro, Vasant Hanovar, Etienne E. Kerre, Mi Lu, Manuel Grana Romay, Timothy K. Shih, Dan Ventura, Paul Wang, Yuanyuan Yang (Editors), Proceedings of the Sixth Joint Conference on Information Sciences, March 2002.

Dan Ventura, comments in "Discussion on Neurocomputers After Ten Years" (Eds. Frolov and Ezhov), Neural Network World, vol. 1 no. 2, pp. 103-174, 1999.

Dan Ventura, Quantum and Evolutionary Approaches to Computational Learning, Ph.D. Dissertation, Computer Science Department, Brigham Young University, 1998.

Dan Ventura, On Discretization as a Preprocessing Step for Supervised Learning Models, Master's Thesis, Computer Science Department, Brigham Young University, 1995.