Professor of Computer Science
echown@bowdoin.edu
207-725-3084
Computer Science
Searles Science Building - 221
Solving complex algorithmic problems requires the use of appropriate data structures such as stacks, priority queues, search trees, dictionaries, hash tables, and graphs. It also requires the ability to measure the efficiency of operations such as sorting and searching in order to make effective choices among alternative solutions. Offers a study of data structures, their efficiency, and their use in solving computational problems. Laboratory exercises provide an opportunity to design and implement these structures. Students interested in taking Computer Science 2101 are required to pass the computer science placement examination with a grade of C or better before class starts.
Advances in computer science, psychology, and neuroscience have shown that humans process information in ways that are very different from those used by computers. Explores the architecture and mechanisms that the human brain uses to process information. In many cases, these mechanisms are contrasted with their counterparts in traditional computer design. A central focus is to discern when the human cognitive architecture works well, when it performs poorly, and why. Conceptually oriented, drawing ideas from computer science, psychology, and neuroscience. No programming experience necessary.
Intended for students with some programming experience, but not enough to move directly into Data Structures. An accelerated introduction to the art of problem solving using the computer and the Python programming language. Weekly labs and programming assignments focus on "big data" and its impact on the world.
Explores the principles and techniques involved in programming computers to do tasks that would require intelligence if people did them. State-space and heuristic search techniques, logic and other knowledge representations, reinforcement learning, neural networks, and other approaches are applied to a variety of problems with an emphasis on agent-based approaches.
Besides teaching computer science courses in artificial intelligence, cognitive architecture, and computer programming, Eric Chown also enjoys researching the learning in humans and machines. Three years ago, he was awarded a five-year National Science Foundation Faculty Early Career Development Grant that was used to buy specialized robots for his project - "Computational Models of Space in Navigation and Other Domains." Chown has used these robots in several of his classes.
Journals
Chown, E. (1999). Making predictions in an uncertain world: Environmental structure andcognitive maps. Adaptive Behavior, 1-17.
Chown, E., Kaplan, S. & Kortenkamp, D. (1995). Prototypes, Location and AssociativeNetworks (PLAN): Towards a unified theory of cognitive mapping. Cognitive Science., 19, 1-52.
Kaplan, S., Sonntag, M. & Chown, E. (1991) Tracing recurrent activity in cognitive elements(TRACE): A model of temporal dynamics in a cell assembly. Connection Science, 3, 179206.
Highly Refereed Conferences
Chown, E. (2002). Reminiscence and arousal: A connectionist model. To appear in theProceedings of the Twenty Fourth Annual Meeting of the Cognitive Science Society.
Chown, E. & Dietterich, T. G. (2000). A divide and conquer approach to learning from priorknowledge. In Proceedings of the Seventeenth International Conference on Machine Learning, Langley, P. (ed.), Morgan Kauffman, 143-150.
Chown, E., Jones, R.M., & Henninger, A.E. (2002). An architecture for emotional decision-making agents. To appear in the proceedings of The First Annual Conference on Autonomous Agents & Multiagent Systems
Forbell, E., & Chown, E. (2000).Lexical contact during speech perception: A connectionist model. In Proceedings of the Twenty Second Annual Meeting of the Cognitive Science Society.
Henninger, A.E., Jones, R.M. & Chown (2003). Behaviors that emerge from emotion andcognition: Implementation and evaluation of a symbolic-connectionist architecture. To appear in the proceedings of The Second Annual Conference on Autonomous Agents & MultiagentSystem.
Kortenkamp, D. & Chown, E. (1993). A directional spreading activation network for mobilerobot navigation. From Animals to Animats 2, Proceedings of the Second International Conference on Simulation of Adaptive Behavior, Meyer, J.-A., Roitblat H. L. and Wilson,S.W. (eds.), MIT-Press.
Book Chapters
Chown, E. & Boots, B. (2005). Learning Cognitive Maps: Finding Useful Structure in an Uncertain World. To appear in Jefferies, M. & Yeap, A. (eds.) Robot and Cognitive Approaches to Spatial Mapping.
Chown, E. (2004) Cognitive Modeling. In Tucker, A. (ed.) Computer Science Handbook. Chapman & Hall.
PDF preprint »
Chown, E. (1999). Error tolerance and generalization in cognitive maps: Performance withoutprecision. In Golledge, R. (ed.) Wayfinding Behavior: Cognitive Mapping and Other Spatial Processes., 349-369 The Johns Hopkins University Press.
Commentaries
Chown, E. (1995). Reverberation reconsidered: On the path to cognitive theory. Behavioral andBrain Sciences, 18, 628-629.
Chown, E., Booker, L.B., & Kaplan, S. (2002). Perception, Action Planning, and CognitiveMaps. To appear in Behavioral and Brain Sciences.
Chown, E. & Kaplan, S. (1992). Active symbols, limited storage and the power of naturalintelligence. Behavioral and Brain Sciences, 15:3, 442-443.
Other
Chown, E., Foil, G., Work, H., & Zhuang, Y. (2006). AiboConnect: A simple programming environment for robotics. To appear in The proceedings of the 19th International FLAIRS Conference.
PDF preprint »
Henninger, A.E., Jones, R.M. & Chown, E. (2003). Behaviors that emerge from emotion and cognition: Implementation and evaluation of a symbolic-connectionist architecture. To appear in The proceedings of Autonomous Agents and Multi-Agent Systems '03.
PDF preprint »
Chown, E., (2002). Reminiscence and arousal: a connectionist model. In The proceedings of the 24th annual meeting of the Cognitive Science Society, 234-239.
PDF preprint »
Chown, E., Jones, R.M., & Henninger, A.E. (2002). An architecture for emotional decision-making agents. In The proceedings of Autonomous Agents and Multi-Agent Systems '02.
PDF preprint »
Jones, R.M., Henninger, A.E., & Chown, E. (2002). Interfacing emotional behavior moderators with intelligent synthetic forces. Proceedings of the 11th Computer Generated Forces and Behavior Representation Conference.
PDF preprint »
Henninger, A.E., Jones, R.M. & Chown, E. (2002). Behaviors that emerge from emotion and cognition: A first evaluation. Proceedings of the '02 Interservice/Industry Training, Simulation, and Education Conference (I/ITSEC). Orlando, FL.
PDF preprint »
Henninger, A.E., Jones, R.M., & Chown, E. (2001). A symbolic-connectionist framework for representing emotions in computer generated forces. Proceedings of the '01 Interservice/Industry Training, Simulation, and Education Conference (I/ITSEC). Orlando, FL.
PDF preprint »
Chown, E., & Dietterich, T.G. (2000). Learning in the Presence of Prior Knowledge: A Case Study Using Model Calibration. Poscript preprint. In the Proceedings of the Seventeenth International Conference on Machine Learning. P. Langley (ed.). Morgan Kauffman, pp 143-150.
Chown, E. (2000). Gateways: An approach to parsing spatial domains. In ICML-2000 Workshop on Machine Learning of Spatial Knowledge, 1-6.
Jones, R.M., Chown, E., & Henninger, A.E. (2001). A hybrid symbolic-connectionist approachto modeling emotions. In AAAI Fall Symposium Emotional and Intelligent II: The Tangled Knot of Social Cognition. Technical Report FS-01-02. AAAI Press.
Henninger, A.E., Jones, R.M., and Chown, E. (2002). Behaviors that emerge from emotion andcognition: A first evaluation. In the Proceedings of Interservice/Industry Training Simulation and Education Conference (I/ITSEC), 2002. Orlando, FL
Henninger, A.E., Jones, R.M., and Chown, E. (2001). Framework for Attention, Cognition andEmotion in Synthetic Forces. In the Proceedings of Interservice/Industry Training Simulation and Education Conference (I/ITSEC), November 26-29, 2001. Orlando, FL.
Chown, E. (1994). Consolidation and Learning: A Connectionist Model of Human Credit Assignment. Doctoral dissertation. The University of Michigan.
(1999). Fitting Parameters to Ecosystem Models Using Surface Data. 1999 NASA Workshop onData Mining and Data Fusion
(2000). Gateways: An approach to parsing spatial domains. ICML-2000 Workshop on MachineLearning of Spatial Knowledge.
Undergraduate Honors Thesis Supervision
Eric Forbell, Bowdoin College
Anthony Roy, Bowdoin College
Doug Vail, Bowdoin College
Byron Boots, Bowdoin College
Masters Committee Membership
Russ Tedrake, University of Michigan
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