The Decision Theory & Adaptive Systems Group (DTAS) is focused on extending the flexibility and responsiveness of operating systems, databases, information systems, and user interfaces. Areas of attention include automated reasoning and inference under uncertainty, data mining and knowledge discovery in databases, learning models from data, , information retrieval, automated diagnosis and decision support, and automated learning for custom-tailoring software to user work patterns and preferences.

The group is focused on investigating the use of probability and utility theory to enhance computer applications and platforms. Explicit consideration of user preferences and key uncertainties associated with particular tasks and contexts is a central element of DTAS projects. 

• Information access and management. We are pursuing principles and applications of technologies that allow users to access, filter, and manage information. As part of our early work in this area, DTAS developed the algorithms and assessment methods used in the Answer Wizard, a free-text help facility unveiled in Office '95 products.
• Intelligent user interfaces. DTAS is working on methods, languages, and architectures for integrating multiple sources of information to enhance user interfaces. DTAS' Lumiere Project has focused on the construction and integration of Bayesian models of a user's needs for assistance. Lumiere research led to the Office Assistant, a Bayesian help system in Office '97. Ongoing work on intelligent user interfaces includes integrating consideration of acoustic and visual events into analyses of user goals.
  
• Data mining and knowledge discovery in databases. We are investigating methods, tools, and applications of data mining and discovery in databases (KDD) for discovering useful relationships in large datasets.
  
• Diagnostics and troubleshooting. We have developed and applied diagnostic reasoning methods to a range of problems, extending from software debugging to troubleshooting software and hardware systems. In our collaboration with Microsoft Technical Support, we have developed decision-theoretic troubleshooters that are available via the worldwide web. Visit Microsoft Technical Support Troubleshooters to access several decision-theoretic troubleshooters that have been deployed in an operational setting. Another diagnostics project, named Aladdin, has explored the application of decision-theoretic case-based reasoning to troubleshooting and customer support, the result of a collaboration between DTAS and Microsoft Technical Support.
  
• Learning models from data. Several research and development efforts focus on the development of methods for building predictive models from data. Some of this work builds on foundations of Bayesian statistics.
  
• Optimization of computational processes. We are exploring the use of flexible computational methods and decision theory to identify bottlenecks, and to optimize the functionality of operating systems and applications.

• Special Issue on Bayesian Networks: Communications of the ACM., March, 1995, vol 38, no. 3.
• Special Issue on Data Mining: Communications of the ACM., November, 1996, vol 39, no. 11.
• Data Mining and Knowledge Discovery, a technical journal, Kluwer Academic Publishers.
• J. Breese, D. Heckerman. Decision-theoretic case-based reasoning. Technical Report MSR-TR-95-03, Microsoft Research, November, 1994.
• D. Heckerman, D. Geiger, D. Chickering. Learning Bayesian networks: The Combination of Knowledge and Statistical Data. Technical Report MSR-TR-94-09, Microsoft Research, March, 1994 (revised December, 1994).
• D. Heckerman. A tutorial on learning Bayesian networks. Technical Report MSR-TR-95-06, Microsoft Research, March, 1995.
• E. Horvitz and M. Barry. Display of Information for Time-Critical Decision Making. Proceedings of the Eleventh Conference on Uncertainty in Artificial Intelligence, August 1995.
• E. Horvitz, J. Breese, D. Heckerman, D. Hovel, and K. Rommelse. The Lumiere Project: Bayesian User Modeling for Inferring the Goals and Needs of Software Users. Proceedings of the Fourteenth Conference on Uncertainty in Artificial Intelligence, July 1998.

The Decision Theory & Adaptive Systems Group at Microsoft Research has created a Windows application for Bayesian belief network construction and inference tool called Microsoft Belief Networks, or MSBN. This software is free for non-commercial purposes.

• Bayesian Network Interchange Format. A standardization effort to define an interchangable text file format for belief networks and influence diagrams.

DTAS researchers are active in several research communities including Uncertainty and Artificial Intelligence (UAI), Institute for Operations Research and the Management Sciences (INFORMS), American Association for Artificial Intelligence (AAAI), International Society for Bayesian Analysis (ISBA), Knowledge Discovery and Datamining (KDD), and American Statistics Association (ASA). Here are some relevant links to these communities:

• Uncertainty and Artificial Intelligence (UAI)
• Institute for Operations Research and the Management Sciences (INFORMS)
• American Association for Artificial Intelligence (AAAI)
• Decision Analysis Society of INFORMS
• International Society for Bayesian Analysis (ISBA)
• Data Mining and Knowledge Discovery in Databases (KDD)
• ACM SIGKDD: Special Interest Group on Knowledge Discovery and Data Mining
• American Statistics Association (ASA)
• ASA Section on Bayesian Statistical Sciences
• Society for Medical Decision Making (SMDM)

• "Mr. Gates Builds His Brain Trust" (Fortune Magazine, Dec. 1997)
• "Artificial Intelligence Gets Real" (ZDnet, August 1997)
• "Spam Killers" (Forbes, September 1998)
• "Is AI Going Mainstream at Last: A Look Inside Microsoft Research" (IEEE Intelligent Systems, March-April 1998, .pdf file: requires Acrobat viewer or plug-in).

Last updated: August 1998

Microsoft Research Decision Theory & Adaptive Systems Group / dtg@microsoft.com