Chapter 24 – Model Checking

TL;DR: A comparative study focusing on the three base case techniques of stochastic analysis: exact numericalAnalysis, approximative numerical analysis, and simulation is reported on.

TL;DR: A method to learn policies from expert demonstrations that are interpretable and manipulable, and integrating this automaton into planning via Logical Value Iteration, so that changes to the automaton have predictable effects on the learned behavior.

TL;DR: Partial order reduction and symbolic model checking are combined to combating state space explosion in verifying SystemC and the efficiency is demonstrated by verifying a set of SystemC designs.

TL;DR: Two logic frameworks for compartmental epidemic models based on Linear Temporal Logic and Computation Tree Logic are introduced.

TL;DR: In this paper, the authors present a data structure for representing Boolean functions and an associated set of manipulation algorithms, which have time complexity proportional to the sizes of the graphs being operated on, and hence are quite efficient as long as the graphs do not grow too large.

TL;DR: The value of depth-first search or “backtracking” as a technique for solving problems is illustrated by two examples of an improved version of an algorithm for finding the strongly connected components of a directed graph.

TL;DR: A case study in synchronization and proof techniques, and some proofs about data structures in value-communication as a model of CCS 2.0.

TL;DR: It is argued that this technique can provide a practical alternative to manual proof construction or use of a mechanical theorem prover for verifying many finite-state concurrent systems.