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.
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 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.
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.
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.
Decision-theoretic planning: structural assumptions and computational leverage
Craig Boutilier;Thomas Dean;Steve Hanks.
Journal of Artificial Intelligence Research (1999)
The dynamics of reinforcement learning in cooperative multiagent systems
Caroline Claus;Craig Boutilier.
national conference on artificial intelligence (1998)
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)
Context-specific independence in Bayesian networks
Craig Boutilier;Nir Friedman;Moises Goldszmidt;Daphne Koller.
uncertainty in artificial intelligence (1996)
Stochastic dynamic programming with factored representations
Craig Boutilier;Richard Dearden;Moisés Goldszmidt.
Artificial Intelligence (2000)
Planning, Learning and Coordination in Multiagent Decision Processes
theoretical aspects of rationality and knowledge (1996)
Exploiting Structure in Policy Construction
Craig Boutilier;Richard Dearden;Moises Goldszmidt.
international joint conference on artificial intelligence (1995)
SPUDD: stochastic planning using decision diagrams
Jesse Hoey;Robert St-Aubin;Alan Hu;Craig Boutilier.
uncertainty in artificial intelligence (1999)
Toward a Logic for Qualitative Decision Theory
principles of knowledge representation and reasoning (1994)
Sequential Optimality and Coordination in Multiagent Systems
international joint conference on artificial intelligence (1999)
Profile was last updated on December 6th, 2021.
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