System to establish trust between policy systems and users

Abstract

A system and method are provided to establish trust between a user and a policy system that generates recommended actions in accordance with specified policies. Trust is introduced into the policy-based system by assigning a value to each execution of each policy with respect to the policy-based system, called the instantaneous trust index. The instantaneous trust indices for each one of the policies, for the each execution of a given policy or for both are combined into the overall trust index for a given policy or for a given policy-based system. The recommended actions are processed in accordance with the level or trust associated with a given policy as expressed by the trust indices. Manual user input is provided to monitor or change the recommended actions. In addition, reinforcement learning algorithms are used to further enhance the level of trust between the user and the policy-based system.

Description

CROSS REFERENCE TO RELATED APPLICATIONS [0001] Pursuant to 35 U.S.C. §119(e), the present application claims priority to co-pending provisional application No. 60 / 686,471 filed Jun. 1, 2005. The entire disclosure of that application is incorporated herein by reference.FIELD OF THE INVENTION [0002] The present invention relates to policy-based computing systems. BACKGROUND OF THE INVENTION [0003] The use of business rules and policies to externalize business and operational logic from an application is an important concept and approach to building large business applications and to new areas such as self-managing systems or autonomic computing systems. Business rules and policies are statements that are intended to be readable and modifiable by non-technical users and executable by an underlying mechanism such as a rule engine or a Java Virtual Machine (JVM), allowing application logic to be authored and modified external to the application. [0004] One of the key aspects of using the...

AI Technical Summary

Benefits of technology

[0011] Suitable methods for computing the ITI include, for example, examining what fraction of actions suggested from the execution of a particular policy rule the user accepts unchanged or by examining the extent to which the user changes or modifies the suggested actions. In addition, reinforcement learning techniques are used in combination with the ITI and OTI so that a given policy or policy-based system can adjust its behavior to maximum its trust index, i.e. to increase the trust of the user.

Problems solved by technology

As the requirements on policy-based systems become more complex, traditional approaches to the implementation of such systems, for example relying entirely on simple “if [condition] then [actions]” rules, become insufficient.

One issue regarding the use or implementation of policy-based systems is establishing the same level of trust among users and system administrators for policy-based systems as exists for traditional systems.

Unless policy-based systems are trusted at least as much as traditional systems, increases in the acceptance level of policy-based systems will be hindered.

Unfortunately, current approaches to the design and implementation of policy-based systems do nothing to reduce administrators' skepticism towards policy-based automation.

Information Technology (IT) managers are likely to be hesitant to trust an autonomous policy-based system to run the entire IT operations without first establishing a certain level of trust in that autonomous policy-based system.

Current issues regarding trust in policy-based systems have concentrated on user interface issues.

In addition, no general tools are available that allow a policy system to earn a user's trust.

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