Practical Assumptions for Planning Under Uncertainty

Juan Carlos Saborío, Joachim Hertzberg


The (PO)MDP framework is a standard model in planning and decision-making under uncertainty, but the complexity of its methods makes it impractical for any reasonably large problem. In addition, task-planning demands solutions satisfying efficiency and quality criteria, often unachievable through optimizing methods. We propose an approach to planning that postpones optimality in favor of faster, satisficing behavior, supported by context-sensitive assumptions that allow an agent to reduce the dimensionality of its decision problems.We argue that a practical problem solving agent may sometimes assume full observability and determinism, based on generalizations, domain knowledge and an attentional filter obtained through a formal understanding of “relevance”, therefore exploiting the structure of problems and not just their representations.


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Paper Citation

in Harvard Style

Saborío J. and Hertzberg J. (2017). Practical Assumptions for Planning Under Uncertainty . In Proceedings of the 9th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART, ISBN 978-989-758-220-2, pages 497-502. DOI: 10.5220/0006189004970502

in Bibtex Style

author={Juan Carlos Saborío and Joachim Hertzberg},
title={Practical Assumptions for Planning Under Uncertainty},
booktitle={Proceedings of the 9th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,},

in EndNote Style

JO - Proceedings of the 9th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,
TI - Practical Assumptions for Planning Under Uncertainty
SN - 978-989-758-220-2
AU - Saborío J.
AU - Hertzberg J.
PY - 2017
SP - 497
EP - 502
DO - 10.5220/0006189004970502