White box specification of intervention policies for prescriptive process monitoring

IF 2.7 3区 计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Mahmoud Shoush, Marlon Dumas
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引用次数: 0

Abstract

Prescriptive process monitoring methods seek to enhance business process performance by triggering real-time interventions, such as offering discounts to increase the likelihood of a positive outcome (e.g., a purchase). At the core of a prescriptive process monitoring method lies an intervention policy, which determines under which conditions and when to trigger an intervention. While state-of-the-art prescriptive process monitoring approaches rely on black-box intervention policies derived through reinforcement learning, algorithmic decision-making requirements sometimes dictate that the business stakeholders must be able to understand, justify, and adjust these intervention policies manually. To address this requirement, this article proposes WB-PrPM (White-Box Prescriptive Process Monitoring), a framework that enables stakeholders to define intervention policies in business processes. WB-PrPM is a rule-based system that helps decision-makers balance the demand for effective interventions with the imperatives of limited resource capacity. The framework incorporates an automated method for tuning the parameters of the intervention policies to optimize a total gain function. An evaluation is presented using real-life datasets to examine the tradeoffs among various parameters. The evaluation reveals that different variants of the proposed framework outperform existing baselines in terms of total gain, even when default parameter values are used. Additionally, the automated parameter optimization approach further enhances the total gain.
规定性流程监控干预政策的白盒规范
规范性流程监控方法旨在通过触发实时干预来提高业务流程性能,如提供折扣以提高积极结果(如购买)的可能性。规定性流程监控方法的核心是干预策略,它决定在什么条件下和什么时候触发干预。虽然最先进的规范性流程监控方法依赖于通过强化学习得出的黑箱干预策略,但算法决策要求有时决定了业务利益相关者必须能够理解、证明和手动调整这些干预策略。为了满足这一要求,本文提出了 WB-PrPM(白盒规范性流程监控),这是一个能让利益相关者在业务流程中定义干预策略的框架。WB-PrPM 是一个基于规则的系统,可帮助决策者在有效干预的需求与有限资源能力之间取得平衡。该框架采用自动方法调整干预政策参数,以优化总收益函数。利用现实生活中的数据集进行了评估,以检查各种参数之间的权衡。评估结果表明,即使使用默认参数值,拟议框架的不同变体在总增益方面也优于现有基线。此外,自动参数优化方法进一步提高了总增益。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Data & Knowledge Engineering
Data & Knowledge Engineering 工程技术-计算机:人工智能
CiteScore
5.00
自引率
0.00%
发文量
66
审稿时长
6 months
期刊介绍: Data & Knowledge Engineering (DKE) stimulates the exchange of ideas and interaction between these two related fields of interest. DKE reaches a world-wide audience of researchers, designers, managers and users. The major aim of the journal is to identify, investigate and analyze the underlying principles in the design and effective use of these systems.
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