Authors:
Yazmin S. Villegas-Hernandez
and
Federico Guedea-Elizalde
Affiliation:
Tecnologico de Monterrey, Mexico
Keyword(s):
Bayesian Networks, Planning, and Planning Search.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Formal Methods
;
Informatics in Control, Automation and Robotics
;
Intelligent Control Systems and Optimization
;
Planning and Scheduling
;
Simulation and Modeling
;
Symbolic Systems
Abstract:
In planning search, there are different approaches to guide the search, where all of them are focused in have a
plan (solution) in less time. Most of the researches are not admissible heuristics, but they have good results in
time. For example, using the heuristic-search planning approach plans can be generated in less time than other
approaches, but the plans generated by all heuristic planners are sub-optimal, or could have dead ends (states
from which the goals get unreachable). We present an approach to guide the search in a probabilistic way in
order to do not have the problems of the not admissible approaches. We extended the Bayesian network and
Bayesian inferences ideas to our work. Furthermore, we present our way to make Bayesian inferences in order
to guide the search in a better way. The results of our experiments of our approach with different well-known
benchmarks are presented. The benchmarks used in our experiments are: Driverlog, Zenotravel, Satellite,
Rovers, and Fre
ecell.
(More)