A practical planning integration framework for ontology-driven applications
Despite the many clear advantages that ontology has to offer as a standardized knowledge representation language, many intelligent system developers still find it difficult to jump on the band wagon and represent all their application knowledge using ontology. This difficulty and hesitation stems primarily from the fact that, while most ontology languages provide native support for reasoning about the domain's structures, they do not provide adequate support for computational planning -- the kind of reasoning used by many intelligent systems to derive their purposeful behaviors. To overcome this challenge, a lot of work has been done to discover a practical way to seamlessly incorporate planning into ontology languages. As it has been well-established in the literature however, this is a very challenging task from both a theoretical and practical stand point, and many of the reported works in this direction either have had very limited success, or have been done in ad hoc and less reusable manners. In this thesis, we report our pursuit of a new approach to integrating planning into ontology-driven applications. This approach promises to overcome the difficulties faced by many of the existing approaches. In addition to producing a reusable and extensible framework for doing computational planning in ontology-driven applications, our pursuit also raises and answers some interesting ontology research questions that could have potential impacts on several application domains beyond the integration of planning and ontological modeling.