Strategies for partially observable Markov decision processes (POMDP) typically require
memory. One way to represent this memory is via automata. We present a method to learn …

Precondition inference is an important problem with many applications. Existing
precondition inference techniques for programs with arrays have limited ability to find and …

The synthesis problem for partially observable Markov decision processes (POMDPs) is to
compute a policy that provably adheres to one or more specifications. Yet, the general …

Partially Observable Markov Decision Processes (POMDPs) are powerful models for
sequential decision making under transition and observation uncertainties. This paper …

This three-volume proceedings contains the papers presented at the 30th International
Conference on Tools and Algorithms for the Construction and Analysis of Systems (TACAS …

This paper marries two state-of-the-art controller synthesis methods for partially observable
Markov decision processes (POMDPs), a prominent model in sequential decision making …

Partially observable Markov decision process is an important model for autonomous
planning used in many areas, such as robotics and biology. This work focuses on the …

A significant challenge in sequential decision-making involves dealing with uncertainty,
which arises from inaccurate sensors or only a partial knowledge of the agent's environment …