Efficient algorithm for estimation of qualitative expected utility in possibilistic case-based reasoning

TL;DR

This paper introduces a linear-complexity algorithm for estimating qualitative expected utility in possibilistic case-based reasoning, significantly improving decision-making efficiency in multi-attribute scenarios.

Contribution

The paper presents a novel, theoretically proven algorithm that reduces computational complexity from exponential to linear for possibilistic expected utility estimation.

Findings

Algorithm exhibits linear computational complexity.

Successfully applied in multi-party negotiation scenarios.

Enables efficient decision-making with high-dimensional attribute data.

Abstract

We propose an efficient algorithm for estimation of possibility based qualitative expected utility. It is useful for decision making mechanisms where each possible decision is assigned a multi-attribute possibility distribution. The computational complexity of ordinary methods calculating the expected utility based on discretization is growing exponentially with the number of attributes, and may become infeasible with a high number of these attributes. We present series of theorems and lemmas proving the correctness of our algorithm that exibits a linear computational complexity. Our algorithm has been applied in the context of selecting the most prospective partners in multi-party multi-attribute negotiation, and can also be used in making decisions about potential offers during the negotiation as other similar problems.