Integrating Planning, Execution, and Learning to Improve Plan Execution

@article{Celorrio2013IntegratingPE,
  title={Integrating Planning, Execution, and Learning to Improve Plan Execution},
  author={Sergio Jim{\'e}nez Celorrio and Fernando Fern{\'a}ndez and Daniel Borrajo},
  journal={Computational Intelligence},
  year={2013},
  volume={29},
  pages={1-36}
}
Algorithms for planning under uncertainty require accurate action models that explicitly capture the uncertainty of the environment. Unfortunately, obtaining these models is usually complex. In environments with uncertainty, actions may produce countless outcomes and hence, specifying them and their probability is a hard task. As a consequence, when implementing agents with planning capabilities, practitioners frequently opt for architectures that interleave classical planning and execution… CONTINUE READING

References

Publications referenced by this paper.
Showing 1-10 of 56 references

PDDL2.1: An Extension to PDDL for Expressing Temporal Planning Domains

J. Artif. Intell. Res. • 2003
View 18 Excerpts
Highly Influenced

Themetric-FF planning system: Translating ignoring delete lists to numerical state variables

J. HOFFMANN
Journal of Artificia Intelligence Research, 20:291–341. • 2003
View 3 Excerpts
Highly Influenced

Combined task andmotion planning formobile manipulation

WOLFE, JASON, BHASKARAMARTHI, STUARTRUSSELL.
International Conference on Automated Planning and Scheduling, Toronto, Canada. • 2010

Combining planning and motion planning

2009 IEEE International Conference on Robotics and Automation • 2009

A Simple Model for Sequences of Relational State Descriptions

THON, INGO, NIELS LANDWEHR, LUC DE RAEDT.
European Conference on Machine Learning. • 2008

Similar Papers

Loading similar papers…