Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE credits
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.
2015. , 78 p.