Partially-observable Markov decision processes (POMDPs) provide a powerful model for sequential decision-making problems with partially-observed state and are known to have (appro...
We consider symbolic dynamic programming (SDP) for solving Markov Decision Processes (MDP) with factored state and action spaces, where both states and actions are described by se...
Aswin Raghavan, Saket Joshi, Alan Fern, Prasad Tad...
We present a dynamic programming approach for the solution of first-order Markov decisions processes. This technique uses an MDP whose dynamics is represented in a variant of the ...
Dynamically discovering likely program invariants from concrete test executions has emerged as a highly promising software engineering technique. Dynamic invariant inference has t...
Christoph Csallner, Nikolai Tillmann, Yannis Smara...
Abstract. Symbolic execution is a flexible and powerful, but computationally expensive technique to detect dynamic behaviors of a program. In this paper, we present a context-sensi...