Real-World Cognitive Multi-Taskings and Problem Solving:

A Large-Scale Cognitive Architecture Simulation Through High Performance Computing - Project CASIE

Sponsor: Air Force Research Laboratory

Modern problem solving tasks range from the mundane everyday elements of life such as deciding what to eat for dinner and what to buy while driving home from work to highly specialized activities such as planning and executing a large-scale air operations campaign in the desert.  From a computational standpoint, both of these problems are extremely difficult to model, let alone have ready algorithmic solutions that are effective or even efficient.  While both of these problems have a myriad of challenges arising from vagueness, uncertainty, domain scope, etc., they share a fundamental core. There are numerous tasks and subtasks at varying levels of complexity that are either working in cooperation, or, just as likely, in competition with other individual tasks, groups of tasks, or even organizations of tasks.  

This two-year project addresses the preliminary investigation on the fundamental problem of modeling and solving these "communities" of tasks from a cognitive point of view different from existing approaches such as multi-agent systems, parallel and distributed task decomposition, neuro-inspired cognition systems, and cellular automata.  For example, using purely autonomous agents incurs a massive burden of coordination and communication due to their distributed philosophy.  All decisions are made locally and coordination and communication must be negotiated between the agents. 

Project Personnel: 

PIs:

• Prof. Eugene Santos (Dartmouth College)

• Prof. Nael Abu-Ghazaleh(SUNY Binghamton)

• Prof. Zhongfei (Mark) Zhang(SUNY Binghamton)

PhD student:

• Zhen Guo

• Vinay Kolar (advised by Prof. Abu-Ghazaleh)

• Kiley McEvoy (advised by Prof. Santos)

AFRL Program Managers:

• Mr. Duane Gilmour

• Mr. William McKeever

• Mr. John Graniero

Collaborating Organizations:

• Air Force Research Laboratory (Mr. Martin Walter, Dr. Paul Bello)

• Univ. of Texas School of Health Information Sciences at Houston (Prof. Hongbin Wang)

Publications:

• Duane Gilmour and Zhongfei (Mark) Zhang, Determining Course of Action Alignment with Operational Objectives, Proc. the 11th International Command and Control Research and Technology Symposium, Cambridge, UK, September, 2006, (accepted)

• Ruofei Zhang and Zhongfei (Mark) Zhang, Effective Image Retrieval Based on Hidden Concept Discovery in Image Database, IEEE Transaction on Image Processing, Volume 16, Number 2, February, 2006, pp 562 -- 572

• Arif Ghafoor, Zhongfei (Mark) Zhang, Michael S. Lew, and Zhi-Hua Zhou, Guest Editors' Introduction to Machine Learning Approaches to Multimedia Information Retrieval, ACM Multimedia Systems Journal, Springer, 2006

• Zhongfei (Mark) Zhang, Querying Non-Uniform Image Databases for Biometrics-Related Identification Applications, Sensor Review, Emerald Publishers, Volume 26, Number 2, April, 2006, pp 122-126

• Ruofei Zhang and Zhongfei (Mark) Zhang, Empirical Bayesian Learning in the Relevance Feedback of Image Retrieval, Image and Vision Computing, Elsevier Science, Volume 24, Issue 3, March, 2006, pp 211-2233

• Ruofei Zhang, Zhongfei (Mark) Zhang, Mingjing Li, Wei-Ying Ma, and Hong-Jiang Zhang, A Probabilistic Semantic Model for Image Annotation and Multi-Modal Image Retrieval, ACM Multimedia Systems Journal, the special issue of Using Machine Learning Approaches to Multimedia Information Retrieval, Springer, 2006

• Jian Yao and Zhongfei (Mark) Zhang, Hierarchical Shadow Detection for Color Aerial Images, Computer Vision and Image Understanding, Elsevier Science, Volume 102, Issue 1, April, 2006, pp 60-69

• Bo Long, Xiaoyun Wu, Zhongfei (Mark) Zhang, and Philip S. Yu, Unsupervised Learning on K-partite Graphs, Proc. ACM International Conference on Knowledge Discovery and Data Mining, ACM Press, Philadelphia, PA, USA, August, 2006[pdf]

• Bo Long, Zhongfei (Mark) Zhang, Xiaoyun Wu, and Philip S. Yu, Spectral Clustering for Multi-Type Relational Data, Proc. International Conference on Machine Learning, ACM Press, Pittsburgh, PA, USA, June, 2006[pdf]

• Xiao-Bing Xue, Zhi-Hua Zhou, and Zhongfei (Mark) Zhang, Improve Web Search Using Image Snippets, Proc. the 21st National Conference on Artificial Intelligence, AAAI Press, Boston, MA, USA, July, 2006

• Jian Yao, Zhongfei (Mark) Zhang, Sameer Antani, Rodney Long, and George Thoma, Automatic Medical Image Annotation and Retrieval Using SEMI-SECC, Proc. IEEE International Conference on Multimedia and Expo, IEEE Computer Society Press, Toronto, Canada, July, 2006

• Jian Yao, Sameer Antani, Rodney Long, and George Thoma, and Zhongfei (Mark) Zhang, Automatic Medical Image Annotation and Retrieval Using SECC, Proc. IEEE International Symposium on Computer Based Medical Systems, IEEE Computer Society Press, Salt Lake City, Utah, USA, June, 2006[pdf]

• Bo Long, Zhongfei (Mark) Zhang, and Philip S. Yu, Combining Multiple Clusterings by Soft Correspondence, Proc. IEEE International Conference on Data Mining, IEEE Computer Society Press, New Orleans, LA, November, 2005[pdf]

• Bo Long, Zhongfei (Mark) Zhang, and Philip S. Yu, Co-clustering by Block Value Decomposition, Proc. ACM International Conference on Knowledge Discovery and Data Mining, ACM Press, Chicago, IL, August, 2005[pdf]

This material is based upon the work supported by the Air Force Research Lab under project No. FA8750-05-2-0284.

Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the Air Force Research Lab.

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