The use of multiple models in case-based diagnosis

The work described in this paper has as its goal the integration of a number of reasoning techniques into a unified intelligent information system that will aid flight crews with malfunction diagnosis and prognostication. One of these approaches involves using the extensive archive of information contained in aircraft accident reports along with various models of the aircraft as the basis for case-based reasoning about malfunctions. Case-based reasoning draws conclusions on the basis of similarities between the present situation and prior experience. We maintain that the ability of a CBR program to reason about physical systems is significantly enhanced by the addition to the CBR program of various models. This paper describes the diagnostic concepts implemented in a prototypical case based reasoner that operates in the domain of in-flight fault diagnosis, the various models used in conjunction with the reasoner's CBR component, and results from a preliminary evaluation

An integration of case-based and model-based reasoning and its application to physical system faults

Case-Based Reasoning (CBR) systems solve new problems by finding stored instances of problems similar to the current one, and by adapting previous solutions to fit the current problem, taking into consideration any differences between the current and previous situations. CBR has been proposed as a more robust and plausible model of expert reasoning than the better-known rule-based systems.;Current CBR systems have been used in planning, engineering design, and memory organization. There has been minimal work, however, in the area of reasoning about physical systems. This type of reasoning is a difficult task, and every attempt to automate the process must overcome the problems of modeling normal behavior, diagnosing faults, and predicting future behavior.;CBR systems are currently quite difficult to compare and evaluate, because there is currently no common mathematical framework in which the systems can be described. The only avenue available at present for comparison and evaluation of CBR systems requires an intellectual synthesis of the semantics of the program sources. Important constraints on the operation of a CBR system are often hidden in obscure programming tricks in the system\u27s source code.;This thesis presents a hybrid methodology for reasoning about physical systems in operation. This methodology is based on retrieval and adaptation of previously experienced problems similar to the problem at hand. In this methodology the ability of a CBR to reason about a physical system is significantly enhanced by the addition to the Case-Based Reasoner of a model of the physical system. The model describes the physical system\u27s structural, functional, and causal behavior.;Additionally, this thesis presents a mathematical formalization of the case-based reasoning paradigm and a formal specification of the interaction of the CBR component with the model-based component of a case-based system. to prove the feasibility and the merit of such methodology, a prototypical system for dealing with the faults of a physical system has been designed and implemented. Through testing has been proved that this hybrid methodology allows the generation of diagnoses and prognoses that are beyond the capabilities of current reasoning systems

A path-oriented matrix-based knowledge representation system

Experience has shown that designing a good representation is often the key to turning hard problems into simple ones. Most AI (Artificial Intelligence) search/representation techniques are oriented toward an infinite domain of objects and arbitrary relations among them. In reality much of what needs to be represented in AI can be expressed using a finite domain and unary or binary predicates. Well-known vector- and matrix-based representations can efficiently represent finite domains and unary/binary predicates, and allow effective extraction of path information by generalized transitive closure/path matrix computations. In order to avoid space limitations a set of abstract sparse matrix data types was developed along with a set of operations on them. This representation forms the basis of an intelligent information system for representing and manipulating relational data

A path-oriented knowledge representation system: Defusing the combinatorial system

LIMAP is a programming system oriented toward efficient information manipulation over fixed finite domains, and quantification over paths and predicates. A generalization of Warshall's Algorithm to precompute paths in a sparse matrix representation of semantic nets is employed to allow questions involving paths between components to be posed and answered easily. LIMAP's ability to cache all paths between two components in a matrix cell proved to be a computational obstacle, however, when the semantic net grew to realistic size. The present paper describes a means of mitigating this combinatorial explosion to an extent that makes the use of the LIMAP representation feasible for problems of significant size. The technique we describe radically reduces the size of the search space in which LIMAP must operate; semantic nets of more than 500 nodes have been attacked successfully. Furthermore, it appears that the procedure described is applicable not only to LIMAP, but to a number of other combinatorially explosive search space problems found in AI as well

Sensitivity analysis of neural network parameters for identifying the factors for college student success

Predicting student graduation rates in institutes of higher education is of great value to the institution and an enormous potential utility for targeted intervention. During the past decade a number of researchers applied various methodologies in order to predict enrollment rates, persistence rates, and/or graduation rates. In this paper we present the development and performance of an Artificial Neural Network (ANN) for predicting community college graduation outcomes as well as the results of applying sensitivity analysis on the ANN parameters in order to identify the factors that result into a successful graduation outcome. A sample of 1,407 student profiles was used to train and test our ANN. The average predictability rate for the ANN\u27s training and test sets were higher than any other reported in the literature (77% and 68%, respectively). The need for disability services, the need for support services, and the student\u27s age at the time of application to the college were identified as the three factors most contributory to a successful/unsuccessful graduation outcome. © 2008 IEEE