Constraint Networks Under Conditional Uncertainty

Matteo Zavatteri, Luca Viganò

2018

Abstract

Constraint Networks (CNs) are a framework to model the constraint satisfaction problem (CSP), which is the problem of finding an assignment of values to a set of variables satisfying a set of given constraints. Therefore, CSP is a satisfiability problem. When the CSP turns conditional, consistency analysis extends to finding also an assignment to these conditions such that the relevant part of the initial CN is consistent. However, CNs fail to model CSPs expressing an uncontrollable conditional part (i.e., a conditional part that cannot be decided but merely observed as it occurs). To bridge this gap, in this paper we propose constraint networks under conditional uncertainty (CNCUs), and we define weak, strong and dynamic controllability of a CNCU. We provide algorithms to check each of these types of controllability and discuss how to synthesize (dynamic) execution strategies that drive the execution of a CNCU saying which value to assign to which variable depending on how the uncontrollable part behaves. We benchmark the approach by using ZETA, a tool that we developed for CNCUs. What we propose is fully automated from analysis to simulation.

Paper Citation

in Harvard Style

Zavatteri M. and Viganò L. (2018). Constraint Networks Under Conditional Uncertainty.In Proceedings of the 10th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART, ISBN 978-989-758-275-2, pages 41-52. DOI: 10.5220/0006553400410052

in Bibtex Style

@conference{icaart18,
author={Matteo Zavatteri and Luca Viganò},
title={Constraint Networks Under Conditional Uncertainty},
booktitle={Proceedings of the 10th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,},
year={2018},
pages={41-52},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006553400410052},
isbn={978-989-758-275-2},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 10th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,
TI - Constraint Networks Under Conditional Uncertainty
SN - 978-989-758-275-2
AU - Zavatteri M.
AU - Viganò L.
PY - 2018
SP - 41
EP - 52
DO - 10.5220/0006553400410052