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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.
Adapting plans to changes in the environment by finding alternatives and taking advantage of opportunities is a common human behavior. The need for such behavior is often rooted in the uncertainty produced by our incomplete knowledge of the environment. While several existing planning approaches deal with such issues, artificial agents still lack the robustness that humans display in accomplishing their tasks. In this work, we address this brittleness by combining Hierarchical Task Network planning, Description Logics, and the notions of affordances and conceptual similarity. The approach allows a domestic service robot to find ways to get a job done by making substitutions. We show how knowledge is modeled, how the reasoning process is used to create a constrained planning problem, and how the system handles cases where plan generation fails due to missing/unavailable objects. The results of the evaluation for two tasks in a domestic service domain show the viability of the approach in finding and making the appropriate goal transformations.
Humans exhibit flexible and robust behavior in achieving their goals. We make suitable substitutions for objects, actions, or tools to get the job done. When opportunities that would allow us to reach our goals with less effort arise, we often take advantage of them. Robots are not nearly as robust in handling such situations. Enabling a domestic service robot to find ways to get a job done by making substitutions is the goal of our work. In this paper, we highlight the challenges faced in our approach to combine Hierarchical Task Network planning, Description Logics, and the notions of affordances and conceptual similarity. We present open questions in modeling the necessary knowledge, creating planning problems, and enabling the system to handle cases where plan generation fails due to missing/unavailable objects.