ProjectProbabilistic Tree Search

Basic data

Probabilistic Tree Search
6/1/2020 to 5/31/2023
Abstract / short description:
Tree search is a classic computational task and yet still relevant for machine learning applications. Phrased as (sequential) active and reinforcement learning, it is arguably the most extreme case of “small-data AI”, since it requires learning in an exponentially large decision space from linearly limited data. This research project will develop a probabilistic formalism for tree search. It will be able to leverage structure of the search domain, and use probabilistic decision theory to locally improve search efficiency. Microsoft Research has agreed to fund the project through their open grant program, which selects based on scientific merit. The contents of the project were actively developed and proposed by Chair for the Methods of Machine Learning in Tübingen. There are no obligations by the research team to produce economic value for Microsoft.
machine learning
Maschinelles Lernen

Involved staff


Wilhelm Schickard Institute of Computer Science (WSI)
Department of Informatics, Faculty of Science

Local organizational units

Wilhelm Schickard Institute of Computer Science (WSI)
Department of Informatics
Faculty of Science


Cambridge, United Kingdom

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