Constrained CP-nets Similarity

Hassan Alkhiri, Malek Mouhoub

2022

Abstract

The Conditional Preference Network (CP-net) is one of the widely used graphical models for representing and reasoning with qualitative preferences under ceteris paribus (“all else being equal”) assumptions. CP-nets have been extended to Constrained CP-nets (CCP-nets) in order to consider constraints between attributes. Adding constraints will restrict agent preferences, as some of the outcomes become infeasible. Aggregating CCP-nets (representing different agents) can be very relevant for multi-agent and recommender systems. We address this task by defining the notion of similarity between CCP-nets. The similarity is computed using the Hamming distance (between the outcomes of the related pair of CCP-nets) and the number of preference statements shared by both CCP-nets. We propose an algorithm to compute the distance between a pair of CCP-nets, based on the similarity we defined. In order to evaluate the time performance of our proposed algorithm, we conduct several experiments and report the related results.

Paper Citation

in Bibtex Style

@conference{icaart22,
author={Hassan Alkhiri and Malek Mouhoub},
title={Constrained CP-nets Similarity},
booktitle={Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART,},
year={2022},
pages={226-233},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010802900003116},
isbn={978-989-758-547-0},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART,
TI - Constrained CP-nets Similarity
SN - 978-989-758-547-0
AU - Alkhiri H.
AU - Mouhoub M.
PY - 2022
SP - 226
EP - 233
DO - 10.5220/0010802900003116

in Harvard Style

Alkhiri H. and Mouhoub M. (2022). Constrained CP-nets Similarity. In Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART, ISBN 978-989-758-547-0, pages 226-233. DOI: 10.5220/0010802900003116