Abstract. Many complex control problems are not amenable to traditional controller design. Not only is it difficult to model real systems, but often it is unclear what kind of beha...
In this work we extend the work of Dean, Kaelbling, Kirman and Nicholson on planning under time constraints in stochastic domains to handle more complicated scheduling problems. I...
Partial order methods have been introduced to avoid the state explosion problem in veri cation resulting from the representation of multiple interleavings of concurrent transitions...
Partially Observable Markov Decision Processes (POMDPs) provide a general framework for AI planning, but they lack the structure for representing real world planning problems in a...
Stochastic performance models provide a powerful way of capturing and analysing the behaviour of complex concurrent systems. Traditionally, performance measures for these models ar...
In the past, several attempts have been made to deal with the state space explosion problem by equipping a depth-first search (DFS) algorithm with a state cache, or by avoiding co...