We are proud to announce a new workshop for Open Source Software for Decision Making, the scope of which includes reinforcement learning, planning and learning in fully- and partially-observable Markov decision processes, decision system certification and validation, advanced algorithms and data structures for decision making, and human-robot interface languages. Special emphasis is placed on open-sourced software and open research - tools which are openly accessible for use in industry and academia alike.
The decision systems used in academia and industry are often proprietary, and open-sourced software suites such as BURLAP, APPL, and MADP are neither well-known, nor widely circulated. These open-source software projects would benefit from broader community involvement and coordination. Furthermore, while there are high quality datasets and machine learning packages such as scikit-learn, dlib, and Weka, these do not address most decision making problems, such as the control of dynamic systems under uncertainty.
Please see the OSS4DM website.