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Beschreibung:
Artificial agents need to plan their future course of action for the purpose of autonomously and flexibly performing tasks. State-of-the-art planning techniques can provide artificial agents to a certain degree with autonomy and robustness. However, previous planning approaches are typically limited by the fact that they are based on the assumptions that all relevant information is initially available and a complete plan can be generated in a single, monolithic process prior to executing any action. Comparatively little attention has been paid to the need for planning with incomplete, open-ended domain models that enable the reasoning about the active acquisition of relevant but missing information. This thesis introduces a novel hierarchical planning approach that extends previous approaches by additionally considering decompositions that are only applicable with respect to a consistent extension of the (open-ended) domain model at hand. The introduced planning approach is integrated into a plan-based control architecture that interleaves planning and execution automatically so that missing information can be acquired by means of active knowledge acquisition. The plan-based control system can automatically determine what information is relevant for a task at hand as well as what information can be acquired by the corresponding agent. Whenever it is more reasonable, or even necessary, to acquire additional information prior to making the next planning decision, the planner postpones the overall planning process, and the execution of appropriate knowledge acquisition tasks is automatically integrated into the overall planning and execution process. Real-world and simulation-based evaluation results demonstrate that this approach enables a physical robot agent to perform tasks even if no knowledge about the dynamic aspects of the environment is available a priori. ; Künstliche Agenten müssen zukünftige Aktionen planen, um gegebene Aufgaben autonom und flexibel ausführen zu können. Bestehende Planungsansätze können ...