A layered abduction model of perception: Integrating bottom-up and top-down processing in a multi-sense agent

A layered-abduction model of perception is presented which unifies bottom-up and top-down processing in a single logical and information-processing framework. The process of interpreting the input from each sense is broken down into discrete layers of interpretation, where at each layer a best explanation hypothesis is formed of the data presented by the layer or layers below, with the help of information available laterally and from above. The formation of this hypothesis is treated as a problem of abductive inference, similar to diagnosis and theory formation. Thus this model brings a knowledge-based problem-solving approach to the analysis of perception, treating perception as a kind of compiled cognition. The bottom-up passing of information from layer to layer defines channels of information flow, which separate and converge in a specific way for any specific sense modality. Multi-modal perception occurs where channels converge from more than one sense. This model has not yet been implemented, though it is based on systems which have been successful in medical and mechanical diagnosis and medical test interpretation

Knowledge-based diagnosis for aerospace systems

The need for automated diagnosis in aerospace systems and the approach of using knowledge-based systems are examined. Research issues in knowledge-based diagnosis which are important for aerospace applications are treated along with a review of recent relevant research developments in Artificial Intelligence. The design and operation of some existing knowledge-based diagnosis systems are described. The systems described and compared include the LES expert system for liquid oxygen loading at NASA Kennedy Space Center, the FAITH diagnosis system developed at the Jet Propulsion Laboratory, the PES procedural expert system developed at SRI International, the CSRL approach developed at Ohio State University, the StarPlan system developed by Ford Aerospace, the IDM integrated diagnostic model, and the DRAPhys diagnostic system developed at NASA Langley Research Center

Hergebruik van kennis voor model-gebaseerde diagnose

In dit artikel wordt hergebruik van kennis ten behoeve van modellen voor model-gebaseerde diagnose besproken. Eerst wordt aandacht besteed aan de vorm van deze modellen, en aan de belangrijkste invloeden op de inhoud van modellen. Dit blijken te zijn: (1) de taak waarvoor de diagnose wordt uitgevoerd (troubleshooting), en (2) de manier waarop met het model wordt geredeneerd. Daarna worden twee kennisbronnen besproken waaruit modellen kunnen worden afgeleid voor model-gebaseerde diagnose: CAD-gegevens en simulatiemodellen. Informatie in het CAD-formaat EDIF blijkt goed bruikbaar te zijn voor modellen. Uit simulatiemodellen kan een structureel model worden afgeleid, en informatie die voor voorwaarts redeneren kan worden gebruikt. De vraag of deze informatie voldoende is om modellen voor diagnose af te leiden is nog in onderzoek

Active Ambiguity Reduction: An Experiment Design Approach to Tractable Qualitative Reasoning

Coordinated Science Laboratory was formerly known as Control Systems LaboratoryOffice of Naval Research / N00014-86-K-030

Solving the course scheduling problem by constraint programming and simulated annealing

Thesis (Master)--Izmir Institute of Technology, Computer Engineering, Izmir, 2008Includes bibliographical references (leaves: 87-62)Text in English; Abstract: Turkish and Englishix, 80 leavesIn this study it has been tackled the NP-complete problem of academic class scheduling (or timetabling). The aim of this thesis is finding a feasible solution for Computer Engineering Department of İzmir Institute of Technology. Hence, a solution method for course timetabling is presented in this thesis, consisting of two phases: a constraint programming phase to provide an initial solution and a simulated annealing phase with different neighbourhood searching algorithms. When the experimental data are obtained it is noticed that according to problem structure, whether the problem is tightened or loosen constrained, the performance of a hybrid approach can change. These different behaviours of the approach are demonstrated by two different timetabling problem instances. In addition to all these, the neighbourhood searching algorithms used in the simulated annealing technique are tested in different combinations and their performances are presented

Multiple fault diagnosis and human behavior: Diagnosing a two-bit binary adder

Thesis (B.S.) in Psychology--University of Illinois at Urbana-Champaign, 1990.Includes bibliographical references (leaves 27-29)Microfiche of typescript. [Urbana, Ill.]: Photographic Services, University of Illinois, U of I Library, [1990]. 2 microfiches (60 frames): negative.s 1990 ilu n

Model-Based Fault Diagnosis in Information Poor Processes

A theory of model-based fault diagnosis is proposed which is suitable for non-linear plants that are information poor. That is, there are a bare minimum of sensors available to operate the process without recourse to analytical redundancy, the sensors output at frequencies which are likely to be low, relative to the dynamics of the plant, and there is considerable uncertainty surrounding any mathematical models that are available. Other approaches are likely to be more suitable for information rich plants. However, it should be of, at least, philosophical interest to the diagnostician who assumes that he is dealing with such a plant, if only because it should lead him to question whether his plant actually satisfies criteria necessary to support this assumption

Real Time Fault Detection and Diagnosis in Dynamic Engineering Systems Using Constraint Analysis

This thesis describes some new ideas and a practically orientated implementation for fault detection and diagnosis in dynamic engineering systems. The method is designed for use on-line, it is model based, and is capable of coping with modelling inaccuracies, noisy measurements from the system and unmeasurable system states. The fault detection system is robust to false alarms, and the fault diagnosis system allows for the possibility that multiple faults may occur simultaneously. A number of system analysis algorithms are presented to extract various system equations from the model of the system. This means that the user need only enter one model of the whole system, and all of the analysis and equation solving is then handled by computer. The results of this analysis are then automatically encapsulated into a fault detection and diagnosis tool. This results in the automatic generation of a specific fault analysis tool for the system entered by the user. A "hypothesis prover" is developed here for the domain of dynamic systems, which is used to test hypotheses. Some of the ideas about multiple faults as developed by de Kleer & Williams and Reiter have been used, but these have been adapted to make them applicable for real-time, recursive, imprecise, diagnosis. (Diagnoses are imprecise because, due to modelling errors and noisy measurement, it is never possible to be 100% certain about anything.) When multiple faults are considered, the number of possible combinations becomes very large, 2N - 1, where N is the number of components. The computation required to prove a particular hypothesis, although not enormous, is not trivial either, making it impractical to prove a large number of hypotheses. To overcome this a method is proposed which involves just proving a subset of the possible hypotheses, and using the information obtained from these to reason about the other hypotheses. This requires much less computational power as the reasoning process is much less intensive than the proving process. This make the diagnosis of multiple faults possible in real-time. The methods developed here are tested on a real, noisy system where approximations are made when producing the systems' model. These tests show the potential of this approach to fault diagnosis