Beschreibung:
Abstract: In privacy-preserving planning, multiple agents engage in a distributed planning process in order to solve a given problem cooperatively. They communicate with one another to exchange information and to coordinate their actions and they do so while maintaining private information.<br><br>In this thesis, we present a new algorithmic framework for the specification of privacy-preserving planning algorithms, called DMT. We discuss theoretical properties, like privacy preservation, soundness, and completeness, and empirically evaluate the presented approach, comparing it to a multi-agent forward search baseline. We develop a technique that extends search by explorative trials, and show that it significantly improves search performance. This technique is also transferred to multi-agent forward search and likewise increased the search performance considerably.<br><br>To further improve privacy-preserving planning algorithms and the presented search approaches, in particular, we develop a partial order reduction technique based on stubborn sets. Again, we discuss theoretical properties and evaluate the approach empirically. The evaluation shows that stubborn sets pruning can have a profound positive effect on the number of problems that a solution algorithm solves