# Craig Edgar Boutilier

> Ph.D. University of Toronto 1992

**Wikidata**: [Q60058272](https://www.wikidata.org/wiki/Q60058272)  
**Source**: https://4ort.xyz/entity/craig-edgar-boutilier

## Summary

Craig Edgar Boutilier received his education from the University of Toronto [1]. He has been employed by the University of Toronto from 1999 to 2015, with a concurrent appointment from 2004 to 2010, and has been with Google since 2015 [2][3][4].He is a Fellow of the Association for the Advancement of Artificial Intelligence and a Fellow of the Association for Computing Machinery [5][6]. He is also a member of the Association for Computing Machinery [6].

## Summary
Craig Edgar Boutilier is a Canadian computer scientist known for his contributions to artificial intelligence, particularly in knowledge representation, reasoning under uncertainty, and decision-theoretic foundations of AI. He earned his Ph.D. from the University of Toronto in 1992 and has held academic positions at the University of British Columbia and the University of Toronto, as well as a role as Principal Scientist at Google.

## Biography
- Born: Not specified
- Nationality: Canadian
- Education: Ph.D. in Computer Science, University of Toronto, 1992; M.Sc. in Computer Science, University of Toronto, 1988
- Known for: Contributions to default reasoning, belief revision, and decision-theoretic AI
- Employer(s): Google (Principal Scientist, since 2015), University of Toronto (Professor, 1999-2015), University of British Columbia (Associate Professor, 1991-1999)
- Field(s): Artificial Intelligence, Computer Science, Knowledge Representation, Decision Theory

## Contributions
Craig Boutilier has made significant contributions to artificial intelligence, particularly in the areas of knowledge representation and reasoning under uncertainty. His work on default reasoning and belief revision has helped advance the theoretical foundations of AI systems that can handle incomplete or uncertain information. At Google, he has applied these principles to real-world AI applications, though specific products or patents are not detailed in the source material. His research has influenced both academic theory and practical AI implementations, bridging the gap between theoretical computer science and applied machine learning.

## FAQs
### Q: What is Craig Edgar Boutilier known for in AI?
A: He is known for his contributions to default reasoning, belief revision, and decision-theoretic foundations of artificial intelligence, particularly in handling uncertainty and incomplete information.

### Q: Where did Craig Boutilier receive his education?
A: He earned his Ph.D. and M.Sc. in Computer Science from the University of Toronto, completing his doctoral degree in 1992.

### Q: What is Craig Boutilier's current role?
A: He is currently a Principal Scientist at Google, having joined the company in 2015 after a long academic career.

## Why They Matter
Craig Boutilier's work has been fundamental in advancing how AI systems reason under uncertainty, a critical challenge in real-world applications. His theoretical contributions to default reasoning and belief revision have provided the mathematical and logical frameworks that enable AI systems to make decisions when faced with incomplete or probabilistic information. This work has influenced both academic research and practical AI implementations, particularly in areas requiring robust decision-making under uncertainty. His transition from academia to industry at Google represents the application of these theoretical foundations to large-scale AI systems, helping bridge the gap between theoretical computer science and practical machine learning applications.

## Notable For
- AAAI Fellow (2006) for significant contributions to default reasoning, belief revision, and decision-theoretic foundations of AI
- ACM Fellow (2012) for contributions to knowledge representation and computational decision making
- Supervised notable doctoral students including Richard William Dearden, Robert Roy Price, and Pascal Poupart
- Principal Scientist at Google since 2015, applying AI research to industry applications
- Professor at University of Toronto (1999-2015) and Associate Professor at University of British Columbia (1991-1999)

## Body
### Academic Career
Craig Boutilier's academic career spans over two decades, beginning with his doctoral studies at the University of Toronto where he completed his Ph.D. in 1992. He then joined the University of British Columbia as an Associate Professor in 1991, where he established himself as a researcher in artificial intelligence and computer science. In 1999, he moved to the University of Toronto as a Professor, a position he held until 2015.

### Research Contributions
His research has focused on knowledge representation, reasoning under uncertainty, and decision-theoretic foundations of AI. Specifically, his work on default reasoning has addressed how AI systems can make reasonable assumptions when complete information is unavailable. His contributions to belief revision have explored how AI systems can update their knowledge bases when presented with new, potentially conflicting information.

### Industry Impact
In 2015, Boutilier joined Google as a Principal Scientist, marking a transition from pure academia to industry research. This move represents the application of his theoretical work to practical AI systems at scale. While specific Google projects are not detailed in the source material, his role suggests involvement in advanced AI research and development.

### Academic Lineage
Boutilier was a doctoral student of Raymond Reiter, a prominent Canadian computer scientist, establishing a connection to a significant academic lineage in computer science. He has supervised several doctoral students who have gone on to make their own contributions to the field, including Richard William Dearden, Robert Roy Price, and Pascal Poupart.

### Recognition
His contributions have been recognized through prestigious fellowships, including being named an AAAI Fellow in 2006 and an ACM Fellow in 2012. These honors reflect the impact and significance of his work in advancing the theoretical foundations of artificial intelligence.

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## References

1. Mathematics Genealogy Project
2. [ORCID Public Data File 2023](https://pub.orcid.org/v3.0/0000-0001-9330-4545/employment/17062750)
3. [ORCID Public Data File 2023](https://pub.orcid.org/v3.0/0000-0001-9330-4545/employment/61017)
4. [ORCID Public Data File 2023](https://pub.orcid.org/v3.0/0000-0001-9330-4545/employment/61018)
5. [ORCID Public Data File 2023](https://pub.orcid.org/v3.0/0000-0001-9330-4545/employment/61019)
6. [Source](https://aaai.org/about-aaai/aaai-awards/the-aaai-fellows-program/elected-aaai-fellows/)
7. [Source](https://www.acm.org/media-center/2012/december/acm-fellows-named-for-computing-innovations-that-advance-technologies-in-information-age)
8. [ORCID Public Data File 2020](https://pub.orcid.org/v3.0_rc1/0000-0001-9330-4545/researcher-urls/220052)
9. Virtual International Authority File