Lukás Chrpa

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We review the 2014 International Planning Competition (IPC-2014), the eighth in a series of competitions starting in 1998. IPC-2014 was held in three separate parts to assess state-of-the-art in three prominent areas of planning research: the deterministic (classical) part (IPCD), the learning part (IPCL), and the probabilistic part (IPPC). Each part(More)
Formulating knowledge for use in AI Planning engines is currently something of an ad-hoc process, where the skills of knowledge engineers and the tools they use may significantly influence the quality of the resulting planning application. There is little in the way of guidelines or standard procedures, however, for knowledge engineers to use when(More)
There are a lot of approaches for solving planning problems. Many of these approaches are based on ‘brute force‘ search methods and do not care about structures of plans previously computed in certain planning domains. By analyzing these structures we can obtain useful knowledge that can help in finding solutions for more complex planning problems. Methods(More)
There are many approaches for solving planning problems. Many of these approaches are based on ‘brute force’ search methods and they usually do not care about structures of plans previously computed in particular planning domains. By analyzing these structures, we can obtain useful knowledge that can help us find solutions to more complex planning problems.(More)
Description of planning domains and problems is the first critical task when using planning technology. It naturally belongs to the area of Knowledge Engineering as it involves knowledge extraction (from the user) and schematic formulation of problems. To make the task more comprehensive for non-experts in planning we propose to use graphical representation(More)
Much progress has been made in the research and development of automated planning algorithms in recent years. Though incremental improvements in algorithm design are still desirable, complementary approaches such as problem reformulation are important in tackling the high computational complexity of planning. While machine learning and adaptive techniques(More)
The development of domain-independent planners within the AI Planning community is leading to “off the shelf” technology that can be used in a wide range of applications. Moreover, it allows a modular approach – in which planners and domain knowledge are modules of larger software applications – that facilitates substitutions or improvements of individual(More)
Research into techniques that reformulate problems to make general solvers more efficiently derive solutions has attracted much attention, in particular when the reformulation process is to some degree solver and domain independent. There are major challenges to overcome when applying such techniques to automated planning, however: reformulation methods(More)
  • Lukás Chrpa
  • 2010 22nd IEEE International Conference on Tools…
  • 2010
Planning techniques recorded a significant progress during recent years. However, many planning problems remain still hard even for modern planners. One of the most promising approaches is gathering additional knowledge by using learning techniques. Well known sort of knowledge - macro-operators, formalized like `normal` planning operators, represent a(More)