What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Algorithm
Shlomo Zilberstein mainly investigates Mathematical optimization, Markov decision process, Decentralised system, Dynamic programming and Partially observable Markov decision process.
His work in the fields of Mathematical optimization, such as Incremental heuristic search, intersects with other areas such as Scalability.
The various areas that Shlomo Zilberstein examines in his Markov decision process study include Function and Heuristic.
The study incorporates disciplines such as NEXPTIME, Distributed computing and Decision problem in addition to Decentralised system.
His research in Dynamic programming focuses on subjects like Computation, which are connected to Bayesian network and Exploit.
His Partially observable Markov decision process research includes elements of Representation and Nondeterministic algorithm.
His most cited work include:
- The Complexity of Decentralized Control of Markov Decision Processes (812 citations)
- Using anytime algorithms in intelligent systems (583 citations)
- Dynamic programming for partially observable stochastic games (359 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of investigation include Mathematical optimization, Artificial intelligence, Markov decision process, Partially observable Markov decision process and Algorithm.
His work in the fields of Dynamic programming overlaps with other areas such as Scalability and Observable.
Shlomo Zilberstein combines subjects such as Quality, Machine learning, Distributed computing and Plan with his study of Artificial intelligence.
His studies deal with areas such as Multi-agent system and Heuristic as well as Markov decision process.
His research integrates issues of Control theory, Decision theory and Decentralised system in his study of Partially observable Markov decision process.
The concepts of his Algorithm study are interwoven with issues in Range, Control and Message passing.
He most often published in these fields:
- Mathematical optimization (41.72%)
- Artificial intelligence (21.38%)
- Markov decision process (18.62%)
What were the highlights of his more recent work (between 2018-2021)?
- Human–computer interaction (4.14%)
- Markov decision process (18.62%)
- Mathematical optimization (41.72%)
In recent papers he was focusing on the following fields of study:
The scientist’s investigation covers issues in Human–computer interaction, Markov decision process, Mathematical optimization, Autonomy and Artificial intelligence.
Shlomo Zilberstein works mostly in the field of Human–computer interaction, limiting it down to topics relating to Variety and, in certain cases, Closed loop, Intelligent decision support system and Dimension.
His study on Partially observable Markov decision process is often connected to Imperfect as part of broader study in Markov decision process.
Shlomo Zilberstein has included themes like Graphical model, Control theory, Decision-making models and Approximation algorithm in his Partially observable Markov decision process study.
His study in the field of Heuristics also crosses realms of Objective approach.
His Artificial intelligence research is multidisciplinary, incorporating elements of Machine learning and Multi-agent planning.
Between 2018 and 2021, his most popular works were:
- A Multi-Objective Approach to Mitigate Negative Side Effects (8 citations)
- Balancing the Tradeoff Between Clustering Value and Interpretability (6 citations)
- Belief Space Metareasoning for Exception Recovery (5 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Algorithm
Shlomo Zilberstein focuses on Human–computer interaction, Autonomy, Competence, Risk analysis and Introspection.
His research links Plan with Human–computer interaction.
Autonomy is integrated with Space, Artificial intelligence, Set and Domain in his research.
Shlomo Zilberstein integrates several fields in his works, including Competence, State representation, Exploit and Overall performance.
His work in Risk analysis incorporates the disciplines of Objective approach, Fidelity, Ai systems, Reliability and Autonomous agent.
Introspection is connected with Robot and Markov decision process in his research.
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