Autonomous, Model-based Diagnosis Agents
(Preliminary Version)
Michael Schroeder
Abstract
Autonomous, Model-Based Diagnosis Agents defines and describes the
implementation of an architecture for autonomous, model-based
diagnosis agents. It does this by developing a logic programming
approach for model-based diagnosis and introducing strategies to deal
with more complex diagnosis problems, and then embedding the diagnosis
framework into the agent architecture of vivid agents.
Autonomous, Model-Based Diagnosis Agents surveys extended logic
programming and shows how this expressive language is used to model
diagnosis problems stemming from applications such as digital
circuits, traffic control, integrity checking of a chemical database,
alarm-correlation in cellular phone networks, diagnosis of an
automatic mirror furnace, and diagnosis of communication
protocols. The book reviews a bottom-up algorithm to remove
contradiction from extended logic programs and substantially improves
it by top-down evaluation of extended logic programs. Both algorithms
are evaluated in the circuit domain including some of the ISCAS85
benchmark circuits.
This comprehensive in-depth study of concepts, architectures, and
implementation of autonomous, model-based diagnosis agents will be of
great value for researchers, engineers, and graduate students with a
background in artificial intelligence. For practitioners, it provides
three main contributions: first, it provides many examples from
diverse areas such as alarm correlation in phone networks to
inconsistency checking in databases; second, it describes an
architecture to develop agents; and third, it describes a
sophisticated and declarative implementation of the concepts and
architectures introduced.
The final version appeared as a book
published by Kluwer Academic Publisher, a preliminary version is
available as Postscript File