In defense of compilation: A response to Davis' form and content in model-based reasoning

In a recent paper entitled 'Form and Content in Model Based Reasoning', Randy Davis argues that model based reasoning research aimed at compiling task specific rules from underlying device models is mislabeled, misguided, and diversionary. Some of Davis' claims are examined and his basic conclusions are challenged about the value of compilation research to the model based reasoning community. In particular, Davis' claim is refuted that model based reasoning is exempt from the efficiency benefits provided by knowledge compilation techniques. In addition, several misconceptions are clarified about the role of representational form in compilation. It is concluded that techniques have the potential to make a substantial contribution to solving tractability problems in model based reasoning

Functional reasoning in diagnostic problem solving

This work is one facet of an integrated approach to diagnostic problem solving for aircraft and space systems currently under development. The authors are applying a method of modeling and reasoning about deep knowledge based on a functional viewpoint. The approach recognizes a level of device understanding which is intermediate between a compiled level of typical Expert Systems, and a deep level at which large-scale device behavior is derived from known properties of device structure and component behavior. At this intermediate functional level, a device is modeled in three steps. First, a component decomposition of the device is defined. Second, the functionality of each device/subdevice is abstractly identified. Third, the state sequences which implement each function are specified. Given a functional representation and a set of initial conditions, the functional reasoner acts as a consequence finder. The output of the consequence finder can be utilized in diagnostic problem solving. The paper also discussed ways in which this functional approach may find application in the aerospace field

The Galileo PPS expert monitoring and diagnostic prototype

The Galileo PPS Expert Monitoring Module (EMM) is a prototype system implemented on the SUN workstation that will demonstrate a knowledge-based approach to monitoring and diagnosis for the Galileo spacecraft Power/Pyro subsystems. The prototype will simulate an analysis module functioning within the SFOC Engineering Analysis Subsystem Environment (EASE). This document describes the implementation of a prototype EMM for the Galileo spacecraft Power Pyro Subsystem. Section 2 of this document provides an overview of the issues in monitoring and diagnosis and comparison between traditional and knowledge-based solutions to this problem. Section 3 describes various tradeoffs which must be considered when designing a knowledge-based approach to monitoring and diagnosis, and section 4 discusses how these issues were resolved in constructing the prototype. Section 5 presents conclusions and recommendations for constructing a full-scale demonstration of the EMM. A Glossary provides definitions of terms used in this text

Model compilation: An approach to automated model derivation

An approach is introduced to automated model derivation for knowledge based systems. The approach, model compilation, involves procedurally generating the set of domain models used by a knowledge based system. With an implemented example, how this approach can be used to derive models of different precision and abstraction is illustrated, and models are tailored to different tasks, from a given set of base domain models. In particular, two implemented model compilers are described, each of which takes as input a base model that describes the structure and behavior of a simple electromechanical device, the Reaction Wheel Assembly of NASA's Hubble Space Telescope. The compilers transform this relatively general base model into simple task specific models for troubleshooting and redesign, respectively, by applying a sequence of model transformations. Each transformation in this sequence produces an increasingly more specialized model. The compilation approach lessens the burden of updating and maintaining consistency among models by enabling their automatic regeneration

Modelling domain knowledge using explicit conceptualization

Applications are characterized by the tasks and domains involved. Knowledge modeling can be divided into two conceptual subactivities: modeling the task and modeling the domain knowledge. An explicit conceptualization of the domain knowledge at the heart of its organization is discussed. A conceptualization is the objects presumed to exist and the relationships and functions among them. The annotations and the conceptualization guide the construction of applications and support flexible reasoning during problem solving. It also lets domain knowledge be reused

An Architecture for Dynamic Meta-Level Process Control for Model-Based Troubleshooting

There are numerous methods used for troubleshooting devices. Each method has certain domains, knowledge requirements, and assumptions required for it to perform well. However, oftentimes no one method by itself is sufficient to completely solve a troubleshooting problem. Therefore, an architecture is required to control the combined use of many problem solving methods. The combination of multiple problem solving methods makes the troubleshooting process more robust in terms of device domains that can be dealt with and quality of diagnoses produced. Troubleshooting has two tasks: diagnosis and problem resolution. This research provides an architecture that allows dynamic method selection during diagnosis. Dynamic method selection factors the current state of the diagnosis process along with other method parameters to determine which method to use to advance the diagnosis process. The architecture was developed by combining themes from diagnosis research that focused on dynamic multimethod diagnosis and its control. This work has produced several results. It provides an architecture to organize the methods and a basis for making control decisions concerning method use during diagnosis. It identifies a generous number of methods useful to perform diagnosis. It identifies the knowledge these methods require

Fourth Conference on Artificial Intelligence for Space Applications

Proceedings of a conference held in Huntsville, Alabama, on November 15-16, 1988. The Fourth Conference on Artificial Intelligence for Space Applications brings together diverse technical and scientific work in order to help those who employ AI methods in space applications to identify common goals and to address issues of general interest in the AI community. Topics include the following: space applications of expert systems in fault diagnostics, in telemetry monitoring and data collection, in design and systems integration; and in planning and scheduling; knowledge representation, capture, verification, and management; robotics and vision; adaptive learning; and automatic programming

Prospect patents, data markets, and the commons in data-driven medicine : openness and the political economy of intellectual property rights

Scholars who point to political influences and the regulatory function of patent courts in the USA have long questioned the courts’ subjective interpretation of what ‘things’ can be claimed as inventions. The present article sheds light on a different but related facet: the role of the courts in regulating knowledge production. I argue that the recent cases decided by the US Supreme Court and the Federal Circuit, which made diagnostics and software very difficult to patent and which attracted criticism for a wealth of different reasons, are fine case studies of the current debate over the proper role of the state in regulating the marketplace and knowledge production in the emerging information economy. The article explains that these patents are prospect patents that may be used by a monopolist to collect data that everybody else needs in order to compete effectively. As such, they raise familiar concerns about failure of coordination emerging as a result of a monopolist controlling a resource such as datasets that others need and cannot replicate. In effect, the courts regulated the market, primarily focusing on ensuring the free flow of data in the emerging marketplace very much in the spirit of the ‘free the data’ language in various policy initiatives, yet at the same time with an eye to boost downstream innovation. In doing so, these decisions essentially endorse practices of personal information processing which constitute a new type of public domain: a source of raw materials which are there for the taking and which have become most important inputs to commercial activity. From this vantage point of view, the legal interpretation of the private and the shared legitimizes a model of data extraction from individuals, the raw material of information capitalism, that will fuel the next generation of data-intensive therapeutics in the field of data-driven medicine