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Keeping planning problems as small as possible is a must in order to cope with complex tasks and environments. Earlier, we have described a method for cascading Description Logic (dl) representation and reasoning on the one hand, and Hierarchical Task Network (htn) action planning on the other. The planning domain description as well as the fundamental htn planning concepts are represented in dl and can therefore be subject to dl reasoning. From these representations, concise planning problems are generated for htn planners. We show by way of case study that this method yields significantly smaller planning problem descriptions than regular representations do in htn planning. The method is presented through a case study of a robot navigation domain and the blocks world domain. We present the benefits of using this approach in comparison with a pure htn planning approach.
The goal of this work is to develop an integration framework for a robotic software system which enables robotic learning by experimentation within a distributed and heterogeneous setting. To meet this challenge, the authors specified, defined, developed, implemented and tested a component-based architecture called XPERSIF. The architecture comprises loosely-coupled, autonomous components that offer services through their well-defined interfaces and form a service-oriented architecture. The Ice middleware is used in the communication layer. Additionally, the successful integration of the XPERSim simulator into the system has enabled simultaneous quasi-realtime observation of the simulation by numerous, distributed users.