D-Index & Metrics Best Publications

D-Index & Metrics

Discipline name D-index D-index (Discipline H-index) only includes papers and citation values for an examined discipline in contrast to General H-index which accounts for publications across all disciplines. Citations Publications World Ranking National Ranking
Computer Science D-index 60 Citations 16,945 173 World Ranking 1512 National Ranking 841

Research.com Recognitions

Awards & Achievements

2014 - Fellow of the Royal Society of Canada Academy of Science

2012 - ACM Fellow For contributions to knowledge representation and computational decision making.

2006 - Fellow of the Association for the Advancement of Artificial Intelligence (AAAI) For significant contributions to default reasoning, belief revision, and decision-theoretic foundations of AI.

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Programming language
  • Machine learning

His primary areas of study are Artificial intelligence, Mathematical optimization, Markov decision process, Machine learning and Partially observable Markov decision process. His Artificial intelligence research integrates issues from Independence and Action. His Constrained optimization study in the realm of Mathematical optimization connects with subjects such as Observable.

His Markov decision process study integrates concerns from other disciplines, such as Automated planning and scheduling, Representation, Dynamic programming, Markov chain and Bayesian network. His Machine learning research includes themes of Preference, Social choice theory, Limit, Conditional dependence and Key. Craig Boutilier has included themes like Decision quality, Decision theory and Parameterized complexity in his Partially observable Markov decision process study.

His most cited work include:

  • Decision-theoretic planning: structural assumptions and computational leverage (1017 citations)
  • The dynamics of reinforcement learning in cooperative multiagent systems (911 citations)
  • CP-nets: a tool for representing and reasoning with conditional ceteris paribus preference statements (822 citations)

What are the main themes of his work throughout his whole career to date?

His primary areas of investigation include Artificial intelligence, Mathematical optimization, Machine learning, Markov decision process and Regret. His Artificial intelligence study frequently links to other fields, such as Set. The concepts of his Mathematical optimization study are interwoven with issues in Resource allocation, Representation and Set.

His Machine learning study incorporates themes from Probabilistic logic, Key and Pairwise comparison. His work in Markov decision process addresses issues such as Bayesian network, which are connected to fields such as Variable. His work is dedicated to discovering how Regret, Preference elicitation are connected with Social choice theory and Decision theory and other disciplines.

He most often published in these fields:

  • Artificial intelligence (38.00%)
  • Mathematical optimization (33.67%)
  • Machine learning (21.67%)

What were the highlights of his more recent work (between 2014-2021)?

  • Mathematical optimization (33.67%)
  • Artificial intelligence (38.00%)
  • Recommender system (10.00%)

In recent papers he was focusing on the following fields of study:

Craig Boutilier spends much of his time researching Mathematical optimization, Artificial intelligence, Recommender system, Regret and Reinforcement learning. His research in Mathematical optimization intersects with topics in Q-learning, Markov decision process and Parameterized complexity. His study in Markov decision process is interdisciplinary in nature, drawing from both Budget constraint, Dynamic programming and Knapsack problem.

His work focuses on many connections between Artificial intelligence and other disciplines, such as Machine learning, that overlap with his field of interest in Value of information and Expected utility hypothesis. The study incorporates disciplines such as Preference elicitation, Key, State and Human–computer interaction in addition to Recommender system. Craig Boutilier combines subjects such as Representation, Decomposition and Residual with his study of Reinforcement learning.

Between 2014 and 2021, his most popular works were:

  • Data Center Cooling using Model-predictive Control (80 citations)
  • Preference-oriented Social Networks: Group Recommendation and Inference (43 citations)
  • Optimal social choice functions (31 citations)

In his most recent research, the most cited papers focused on:

  • Artificial intelligence
  • Programming language
  • Machine learning

His primary scientific interests are in Mathematical optimization, Regret, Recommender system, Reinforcement learning and Social choice theory. His biological study spans a wide range of topics, including Markov decision process, Complement and Mechanism design. He has researched Regret in several fields, including Artificial neural network, Key, Online algorithm and Generalized linear model.

His research investigates the connection between Recommender system and topics such as Set that intersect with issues in Preference and Data mining. Reinforcement learning is the subject of his research, which falls under Artificial intelligence. His Social choice theory study combines topics from a wide range of disciplines, such as Preference elicitation, Preference and Risk analysis.

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

Best Publications

Decision-theoretic planning: structural assumptions and computational leverage

Craig Boutilier;Thomas Dean;Steve Hanks.
Journal of Artificial Intelligence Research (1999)

1481 Citations

The dynamics of reinforcement learning in cooperative multiagent systems

Caroline Claus;Craig Boutilier.
national conference on artificial intelligence (1998)

1381 Citations

CP-nets: a tool for representing and reasoning with conditional ceteris paribus preference statements

Craig Boutilier;Ronen I. Brafman;Carmel Domshlak;Holger H. Hoos.
Journal of Artificial Intelligence Research (2004)

1172 Citations

Context-specific independence in Bayesian networks

Craig Boutilier;Nir Friedman;Moises Goldszmidt;Daphne Koller.
uncertainty in artificial intelligence (1996)

801 Citations

Stochastic dynamic programming with factored representations

Craig Boutilier;Richard Dearden;Moisés Goldszmidt.
Artificial Intelligence (2000)

562 Citations

Planning, Learning and Coordination in Multiagent Decision Processes

Craig Boutilier.
theoretical aspects of rationality and knowledge (1996)

556 Citations

Exploiting Structure in Policy Construction

Craig Boutilier;Richard Dearden;Moises Goldszmidt.
international joint conference on artificial intelligence (1995)

541 Citations

SPUDD: stochastic planning using decision diagrams

Jesse Hoey;Robert St-Aubin;Alan Hu;Craig Boutilier.
uncertainty in artificial intelligence (1999)

499 Citations

Toward a Logic for Qualitative Decision Theory

Craig Boutilier.
principles of knowledge representation and reasoning (1994)

466 Citations

Sequential Optimality and Coordination in Multiagent Systems

Craig Boutilier.
international joint conference on artificial intelligence (1999)

463 Citations

Best Scientists Citing Craig Boutilier

Jérôme Lang

Jérôme Lang

Paris Dauphine University

Publications: 107

Didier Dubois

Didier Dubois

Centre national de la recherche scientifique, CNRS

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Henri Prade

Henri Prade

Paul Sabatier University

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Piotr Faliszewski

Piotr Faliszewski

AGH University of Science and Technology

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Shlomo Zilberstein

Shlomo Zilberstein

University of Massachusetts Amherst

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David C. Parkes

David C. Parkes

Harvard University

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Ronen I. Brafman

Ronen I. Brafman

Ben-Gurion University of the Negev

Publications: 60

Francesca Rossi

Francesca Rossi

IBM (United States)

Publications: 59

Tuomas Sandholm

Tuomas Sandholm

Carnegie Mellon University

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Edmund H. Durfee

Edmund H. Durfee

University of Michigan–Ann Arbor

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Leendert van der Torre

Leendert van der Torre

University of Luxembourg

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Nicholas R. Jennings

Nicholas R. Jennings

Loughborough University

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Joseph Y. Halpern

Joseph Y. Halpern

Cornell University

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David Poole

David Poole

University of British Columbia

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Moshe Tennenholtz

Moshe Tennenholtz

Technion – Israel Institute of Technology

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Michael L. Littman

Michael L. Littman

Brown University

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Profile was last updated on December 6th, 2021.
Research.com Ranking is based on data retrieved from the Microsoft Academic Graph (MAG).
The ranking d-index is inferred from publications deemed to belong to the considered discipline.

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