This thesis work concerns the area of automated acquisition
of planning domain models from one or more examples of
plans within the domain under study. It assumes that an adequate
domain model for a domain can be composed of objects
arranged in collections called object sorts. Recently, two
systems have had success in using this underlying assumption:
the Opmaker2 system (McCluskey et al. 2009), and
the LOCM system (Cresswell, McCluskey, and West 2009).
The former requires only one solution plan as input, as long
as it contains at least one instance of each operator schema
to be synthesized. It does require a partial domain model as
well as the example plan, and the initial and goal states of
the plan. In contrast LOCM requires no background information,
but requires many instances of plans before it can
synthesize domain models. Our aim is to build on these systems,
and establish an experimental and theoretical basis for
using object - centred assumptions to underlie the automated
acquisition of planning domain models.
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