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.
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