2019 - AAAI Distinguished Service Award For his sustained and conscientious service and leadership both to AAAI as a councilor and conference committee chair, and to the broader AI community, as the president of ICAPS.
2011 - Fellow of the Association for the Advancement of Artificial Intelligence (AAAI) For significant contributions to decision-theoretic reasoning, resource-bounded reasoning, automated planning, decentralized decision making and multiagent systems.
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 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.
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.
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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The Complexity of Decentralized Control of Markov Decision Processes
Daniel S. Bernstein;Robert Givan;Neil Immerman;Shlomo Zilberstein.
Mathematics of Operations Research (2002)
Using anytime algorithms in intelligent systems
Ai Magazine (1996)
Dynamic programming for partially observable stochastic games
Eric A. Hansen;Daniel S. Bernstein;Shlomo Zilberstein.
national conference on artificial intelligence (2004)
LAO: a heuristic search algorithm that finds solutions with loops
Eric A. Hansen;Shlomo Zilberstein.
Artificial Intelligence (2001)
Solving transition independent decentralized Markov decision processes
Raphen Becker;Shlomo Zilberstein;Victor Lesser;Claudia V. Goldman.
Decentralized control of cooperative systems: categorization and complexity analysis
Claudia V. Goldman;Shlomo Zilberstein.
Journal of Artificial Intelligence Research (2004)
The complexity of decentralized control of Markov decision processes
Daniel S. Bernstein;Shlomo Zilberstein;Neil Immerman.
uncertainty in artificial intelligence (2000)
Optimizing information exchange in cooperative multi-agent systems
Claudia V. Goldman;Shlomo Zilberstein.
adaptive agents and multi-agents systems (2003)
Communication decisions in multi-agent cooperation: model and experiments
Ping Xuan;Victor Lesser;Shlomo Zilberstein.
Optimal composition of real-time systems
Shlomo Zilberstein;Stuart Russell.
Artificial Intelligence (1996)
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