||Author(s): G. Tsoumakas, D. Vrakas, N. Bassiliades, I. Vlahavas.
Title: “Lazy Adaptive Multicriteria Planning”.
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Proc. 16th European Conference on Artificial Intelligence, ECAI 2004, R. Lopez de Mantaras and L. Saitta (Eds.), IOS Press, pp. 693-697, Valencia, Spain, 2004.
Abstract: This paper describes a learning system for the automatic
configuration of domain independent planning systems, based on
measurable features of planning problems. The purpose of the Lazy
Adaptive Multicriteria Planning (LAMP) system is to
configure a planner in an optimal way, concerning two quality
metrics (i.e. execution speed and plan quality), for a given
problem according to user-specified preferences. The training data
are produced by running the planner under consideration on a set
of problems using all possible parameter configurations and
recording the planning time and the plan length. When a new
problem arises, (LAMP) extracts the values for a number of
domain-expert specified problem features and uses them to identify
the k nearest problems solved in the past. The system then
performs a multicriteria combination of the performances of the
retrieved problems according to user-specified weights that
specify the relative importance of the quality metrics and selects
the configuration with the best score. Experimental results show
that LAMP improves the performance of the default
configuration of two already well-performing planning systems in a
variety of planning problems.