What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Programming language
- Machine learning
Edmund H. Durfee mainly focuses on Artificial intelligence, Distributed computing, Negotiation, Control and Theoretical computer science.
The Representation research Edmund H. Durfee does as part of his general Artificial intelligence study is frequently linked to other disciplines of science, such as Term, therefore creating a link between diverse domains of science.
His studies in Distributed computing integrate themes in fields like Marshalling, Variety and Knowledge engineering.
The Control study combines topics in areas such as Real-time operating system, Systems architecture and State.
Edmund H. Durfee has researched Theoretical computer science in several fields, including Distributed constraint optimization, Constraint learning, Backtracking, Constraint satisfaction and Constraint satisfaction problem.
His Knowledge management research integrates issues from Process, Flexibility and Effective team.
His most cited work include:
- The distributed constraint satisfaction problem: formalization and algorithms (619 citations)
- Trends in cooperative distributed problem solving (398 citations)
- Distributed constraint satisfaction for formalizing distributed problem solving (332 citations)
What are the main themes of his work throughout his whole career to date?
His scientific interests lie mostly in Artificial intelligence, Mathematical optimization, Distributed computing, Multi-agent system and Operations research.
The various areas that he examines in his Artificial intelligence study include Domain, Machine learning, State and Action.
He focuses mostly in the field of Mathematical optimization, narrowing it down to topics relating to Resource and, in certain cases, Resource allocation.
His work focuses on many connections between Distributed computing and other disciplines, such as Scheduling, that overlap with his field of interest in Theoretical computer science.
His Multi-agent system research incorporates elements of Intelligent agent, Autonomous agent, Risk analysis and Knowledge management.
His Knowledge management research includes elements of Human–computer interaction, Task and Process.
He most often published in these fields:
- Artificial intelligence (26.56%)
- Mathematical optimization (17.50%)
- Distributed computing (16.56%)
What were the highlights of his more recent work (between 2010-2020)?
- Mathematical optimization (17.50%)
- Probabilistic logic (7.50%)
- Artificial intelligence (26.56%)
In recent papers he was focusing on the following fields of study:
His primary areas of study are Mathematical optimization, Probabilistic logic, Artificial intelligence, Scheduling and State.
His Mathematical optimization research is multidisciplinary, relying on both Phase, Partially observable Markov decision process, Markov decision process, Resource and Exploit.
His specific area of interest is Artificial intelligence, where he studies Autonomous agent.
Edmund H. Durfee has included themes like Distributed algorithm and Speedup in his Scheduling study.
His study with Distributed algorithm involves better knowledge in Distributed computing.
His Distributed computing research is multidisciplinary, incorporating elements of Key, Constraint satisfaction problem and Prediction market.
Between 2010 and 2020, his most popular works were:
- Distributed reasoning for multiagent simple temporal problems (20 citations)
- Using hybrid scheduling for the semi-autonomous formation of expert teams (19 citations)
- Towards a unifying characterization for quantifying weak coupling in dec-POMDPs (19 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Programming language
- Machine learning
Edmund H. Durfee focuses on Mathematical optimization, Artificial intelligence, Theoretical computer science, Scheduling and State.
His Mathematical optimization study combines topics in areas such as Exploit, Partially observable Markov decision process, Markov decision process and Dynamic Bayesian network.
His research integrates issues of Class and Function in his study of Artificial intelligence.
Edmund H. Durfee combines subjects such as Computational complexity theory and Schedule with his study of Theoretical computer science.
His work deals with themes such as Teamwork, Distributed computing and Software engineering, which intersect with Scheduling.
His work in State addresses subjects such as Machine learning, which are connected to disciplines such as Action and Human–robot interaction.
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