Enabling Selective Automation of Human Decision-Making Using Rules as Preferences in a Service-industry Application

Biplav Srivastava, Sivakiran Yallamraju

Rules, in their various forms, has been a prominent AI tool in automating the processing of real-world applications. In this paper, we explore how rules can be effectively used as preferences to expedite processing on behalf of humans in the labor-intensive service industry. The new challenges we handle stem from human behavior and audit/ quality considerations that impose design considerations for the rules and the rule system. We consider the domain of Information Technology (IT) change management which seeks to control and reduce the risk of any alteration made to an IT infrastructure in its hardware, software or attached network. We apply rules to selectively automate the decision-making to proceed with a proposed change (called approval) while respecting the considerations of multiple (human) role players. We piloted our system, called Approval Accelerator, in a live environment with about 150 servers for a month and obtained up to 93% reduction in effort and 44% reduction in end-to-end duration. We identify hither-to unknown issues about the impact of the rules on the application process and how rules can be better managed (e.g., storage, elicitation) to address them.

Subjects: 1. Applications; 1.7 Expert Systems

Submitted: May 1, 2008

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