Action planning for graph transition systems

Graphs are suitable modeling formalisms for software and hardware systems involving aspects such as communication, object orientation, concurrency, mobility and distribution. State spaces of such systems can be represented by graph transition systems, which are basically transition systems whose states and transitions represent graphs and graph morphisms. In this paper, we propose the modeling of graph transition systems in PDDL and the application of heuristic search planning for their analysis. We consider different heuristics and present experimental results

On Producing Shortest Cost-Optimal Plans

Cost-optimal planning is at the heart of planning research, with many existing planners that produce provably optimal solutions. While some applications pose additional restrictions, such as producing shortest (in the number of actions) among the cost-optimal plans, standard cost-optimal planning does not provide such a guarantee. We discuss two possible approaches to produce provably the shortest among the costoptimal plans, one corresponding to an instantiation of costalgebraic A∗, the other based on a cost transformation. We formally prove that the new cost-transformation method indeed produces the shortest among the cost-optimal plans and empirically compare the performance of the approaches in different configurations

Survey on Directed Model Checking

International audienceThis article surveys and gives historical accounts to the algorithmic essentials of directed model checking, a promising bug-hunting technique to mitigate the state explosion problem. In the enumeration process, successor selection is prioritized. We discuss existing guidance and methods to automatically generate them by exploiting system abstractions. We extend the algorithms to feature partial-order reduction and show how liveness problems can be adapted by lifting the search Space. For deterministic, finite domains we instantiate the algorithms to directed symbolic, external and distributed search. For real-time domains we discuss the adaption of the algorithms to timed automata and for probabilistic domains we show the application to counterexample generation. Last but not least, we explain how directed model checking helps to accelerate finding solutions to scheduling problems

Cost-Algebraic Heuristic Search

Heuristic search is used to efficiently solve the single-node shortest path problem in weighted graphs. In practice, however, one is not only interested in finding a short path, but an optimal path, according to a certain cost notion. We propose an algebraic formalism that captures many cost notions, like typical Quality of Service attributes. We thus generalize A*, the popular heuristic search algorithm, for solving optimal-path problem. The paper provides an answer to a fundamental question for AI search, namely to which general notion of cost, heuristic search algorithms can be applied. We proof correctness of the algorithms and provide experimental results that validate the feasibility of the approach