Thom Badings, Thiago D. Simão, Marnix Suilen, Nils Jansen
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Decision-making under uncertainty: beyond probabilities
Abstract This position paper reflects on the state-of-the-art in decision-making under uncertainty. A classical assumption is that probabilities can sufficiently capture all uncertainty in a system. In this paper, the focus is on the uncertainty that goes beyond this classical interpretation, particularly by employing a clear distinction between aleatoric and epistemic uncertainty. The paper features an overview of Markov decision processes (MDPs) and extensions to account for partial observability and adversarial behavior. These models sufficiently capture aleatoric uncertainty, but fail to account for epistemic uncertainty robustly. Consequently, we present a thorough overview of so-called uncertainty models that exhibit uncertainty in a more robust interpretation. We show several solution techniques for both discrete and continuous models, ranging from formal verification, over control-based abstractions, to reinforcement learning. As an integral part of this paper, we list and discuss several key challenges that arise when dealing with rich types of uncertainty in a model-based fashion.
期刊介绍:
The International Journal on Software Tools for Technology Transfer (STTT) provides a forum for the discussion of all aspects of tools supporting the development of computer systems. It offers, above all, a tool-oriented link between academic research and industrial practice.
Tool support for the development of reliable and correct computer-based systems is of growing importance, and a wealth of design methodologies, algorithms, and associated tools have been developed in different areas of computer science. However, each area has its own culture and terminology, preventing researchers from taking advantage of the results obtained by colleagues in other fields. Tool builders are often unaware of the work done by others, and thus unable to apply it. The situation is even more critical when considering the transfer of new technology into industrial practice.