A second generation expert system for checking and diagnosing AXAF's electric power system

AXAF - Advanced X-ray Astrophysics Facility - is a third NASA's great space observatory. Each of these observatories is intended to cover different parts of the electromagnetic spectrum (x-ray for AXAF) and to provide high resolution images of celestial sources in our universe. While the spacecraft is in orbit, the electric power system (EPS) performance is monitored via sensors measuring voltages, currents, pressures, and temperatures. The sensor data are sent from the spacecraft to the ground station as telemetry and analyzed on arrival. Monitoring, diagnosis and maintenance of such EPS is an arduous task which requires expertise and constant attention of the ground personnel. To help the ground crew in this task, much of it should be automated and delegated to expert systems, which draw engineer's attention to possible malfunctions and allows him to review the telemetry to determine the source of the trouble, diagnose the suspected fault and to propose a corrective action. Those systems are built on assumptions such as: (1) domain knowledge is available and can be represented as a set of rules; (2) domain knowledge is circumscribed, static, and monotonic; and (3) expert decision making can be emulated by a logical inference mechanism

An Empirical Comparison of Three Inference Methods

In this paper, an empirical evaluation of three inference methods for uncertain reasoning is presented in the context of Pathfinder, a large expert system for the diagnosis of lymph-node pathology. The inference procedures evaluated are (1) Bayes' theorem, assuming evidence is conditionally independent given each hypothesis; (2) odds-likelihood updating, assuming evidence is conditionally independent given each hypothesis and given the negation of each hypothesis; and (3) a inference method related to the Dempster-Shafer theory of belief. Both expert-rating and decision-theoretic metrics are used to compare the diagnostic accuracy of the inference methods.Comment: Appears in Proceedings of the Fourth Conference on Uncertainty in Artificial Intelligence (UAI1988

A distributed agent architecture for real-time knowledge-based systems: Real-time expert systems project, phase 1

We propose a distributed agent architecture (DAA) that can support a variety of paradigms based on both traditional real-time computing and artificial intelligence. DAA consists of distributed agents that are classified into two categories: reactive and cognitive. Reactive agents can be implemented directly in Ada to meet hard real-time requirements and be deployed on on-board embedded processors. A traditional real-time computing methodology under consideration is the rate monotonic theory that can guarantee schedulability based on analytical methods. AI techniques under consideration for reactive agents are approximate or anytime reasoning that can be implemented using Bayesian belief networks as in Guardian. Cognitive agents are traditional expert systems that can be implemented in ART-Ada to meet soft real-time requirements. During the initial design of cognitive agents, it is critical to consider the migration path that would allow initial deployment on ground-based workstations with eventual deployment on on-board processors. ART-Ada technology enables this migration while Lisp-based technologies make it difficult if not impossible. In addition to reactive and cognitive agents, a meta-level agent would be needed to coordinate multiple agents and to provide meta-level control

Theory formation by abduction : initial results of a case study based on the chemical revolution

Abduction is the process of constructing explanations. This chapter suggests that automated abduction is a key to advancing beyond the "routine theory revision" methods developed in early AI research towards automated reasoning systems capable of "world model revision" — dramatic changes in systems of beliefs such as occur in children's cognitive development and in scientific revolutions. The chapter describes a general approach to automating theory revision based upon computational methods for theory formation by abduction. The approach is based on the idea that, when an anomaly is encountered, the best course is often simply to suppress parts of the original theory thrown into question by the contradiction and to derive an explanation of the anomalous observation based on relatively solid, basic principles. This process of looking for explanations of unexpected new phenomena can lead by abductive inference to new hypotheses that can form crucial parts of a revised theory. As an illustration, the chapter shows how some of Lavoisier's key insights during the Chemical Revolution can be viewed as examples of theory formation by abduction

A Model for an Intelligent Support Decision System in Aquaculture

The paper purpose an intelligent software system agents–based to support decision in aquculture and the approach of fish diagnosis with informatics methods, techniques and solutions. A major purpose is to develop new methods and techniques for quick fish diagnosis, treatment and prophyilaxis at infectious and parasite-based known disorders, that may occur at fishes raised in high density in intensive raising systems. But, the goal of this paper is to presents a model of an intelligent agents-based diagnosis method will be developed for a support decision system.support decision system, diagnosis, multi-agent system, fish diseases