Mandis Beigi, Murthy Devarakonda, et al.
POLICY 2005
Policy-based systems are becoming increasingly common; the emerging areas of autonomie [5,6] and on demand [7] computing are accelerating the adoption of such systems. As the requirements on policy-based systems become more complex, traditional approaches to the implementation of such systems, such as relying entirely on simple "if [condition] then [actions]" rules, become insufficient. New approaches to the design and implementation of policy-based systems have emerged, including goal policies[7,11], utility functions [7], data mining, reinforcement learning, and planning. Unfortunately, these new approaches do nothing to reduce administrators' skepticism towards policy-based automation - how is an administrator to believe that a policy-based system will help his systems perform better? Unless policy-based systems are trusted at least as much as traditional systems, it is unlikely that the acceptance of the policy-based systems will increase. In this report, we describe an approach by which a policy-based system can win the trust of its users, and can continuously adjust itself to make better decisions based on the users' preferences. © 2005 IEEE.
Mandis Beigi, Murthy Devarakonda, et al.
POLICY 2005
Christopher S. Campbell, Eser Kandogan, et al.
POLICY 2005
Ali Anwar, Anca Sailer, et al.
IC2E 2015
Jeffrey O. Kephart, Hoi Chan, et al.
ICAC 2007