Integrating Context Inference and Planning in a Network Robot System
Abstract: Context Inference and Planning are becoming more and more valuable in robot oriented technology and several artificial intelligence techniques exist for solving both context inference and planning problems. However, not many combinations of context inference and planning solving have been tried and evaluated as well as comparison between these combinations. This thesis aims to compare two different algorithms, using two different approaches to the problems of context inference and planning. The algorithms studied are Graphplan, which is a classical planning approach to context inference and planning, and SAM, a framework created by the Örebro University, that uses a temporal constraint-based approach. It will also evaluate the expressiveness of these two algorithms applied to the system. To do so an implementation and test of the two approaches is evaluated on a real robot system. This evaluation will show that SAM is much more expressive in terms of domain definition than Graphplan and that reasoning about temporal constraints could become crucial for achieving a system that can succesfully recognize context inference and plan accordingly. The decision on whether to apply one or another is just depending on the kind of system the user needs. If temporal constraints are mandatory, then SAM is the choice to make; in case the only thing the system needs is a fast algorithm able to always find a plan, if it exists, then Graphplan is a better choice.
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