Abstract Partially observable Markov Decision Processes (POMDPs) are a standard model for agents making decisions in uncertain environments. Most work on POMDPs focuses on synthesizing strategies based on the available capabilities. However, system designers can often control an agent’s observation capabilities, e.g. by placing or selecting sensors. This raises the question of how one should select an agent’s sensors cost-effectively such that it achieves the desired goals. In this paper, we study the noveloptimal observability problem(oop): Given a POMDP $$\mathscr {M}$$ M , how should one change $$\mathscr {M}$$ M ’s observation capabilities within a fixed budget such that its (minimal) expected reward remains below a given threshold? We show that the problem is undecidable in general and decidable when considering positional strategies only. We present two algorithms for a decidable fragment of theoop: one based on optimal strategies of $$\mathscr {M}$$ M ’s underlying Markov decision process and one based on parameter synthesis with SMT. We report promising results for variants of typical examples from the POMDP literature.

Graphical security models constitute a well-known, user-friendly way to represent the security of a system. These classes of models are used by security experts to identify vulnerabilities and assess the security of a system. The manual construction of these models can be tedious, especially for large enterprises. Consequently, the research community is trying to address this issue by proposing methods for the automatic generation of such models. In this work, we present a survey illustrating the current status of the automatic generation of two popular kinds of graphical security models: Attack Trees and Attack Graphs. The goal of this survey is to present the current methodologies used in the field, compare them, and present the challenges and future directions to the research community.