1,529 research outputs found
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Explanation-based learning for diagnosis
Diagnostic expert systems constructed using traditional knowledge-engineering techniques identify malfunctioning components using rules that associate symptoms with diagnoses. Model-based diagnosis (MBD) systems use models of devices to find faults given observations of abnormal behavior. These approaches to diagnosis are complementary. We consider hybrid diagnosis systems that include both associational and model-based diagnostic components. We present results on explanation-based learning (EBL) methods aimed at improving the performance of hybrid diagnostic problem solvers. We describe two architectures called EBL_IA and EBL(p). EBL_IA is a form fo "learning in advance" that pre-compiles models into associations. At run-time the diagnostic system is purely associational. In EBL(p), the run-time diagnosis system contains associational, MBD, and EBL components. Learned associational rules are preferred but when they are incomplete they may produce too many incorrect diagnoses. When errors cause performance to dip below a give threshold p, EBL(p) activates MBD and explanation-based "learning while doing". We present results of empirical studies comparing MBD without learning versus EBL_IA and EBL(p). The main conclusions are as follows. EBL_IA is superior when it is feasible but it is not feasible for large devices. EBL(p) can speed-up MBD and scale-up to larger devices in situations where perfect accuracy is not required
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Learning multiple fault diagnosis
This paper describes two methods for integrating model-based diagnosis (MBD) and explanation-based learning. The first method (EBL) uses a generate-test-debug paradigm, generating diagnostic hypotheses using learned associational rules that summarize model-based diagnostic experiences. This strategy is a form of "learning while doing" model-based troubleshooting and could be called "online learning." The second diagnosis and learning method described here (EEL-STATIC) involves ''learning in advance." Learning begins in a training phase prior to performance or testing. Empirical results of computational experiments comparing the learning methods with MBD on two devices (the polybox and the binary full adder) are reported. For the same diagnostic performance, EBL-STATIC is several orders of magnitude faster than MBD while EBL can cause performance slow-down
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Learning approximate diagnosis
Model-based diagnosis (MBD) provides several advantages over experiential rule-based systems. A principal shortcoming of MBD is that MBD learns nothing from any given example. An MBD system facing the same task a second time will incur the same computational effort as that incurred the first time. Our earlier work on incorporating explanation-based learning (EBL) in MBD [4] suggested a diagnostic architecture integrating EBL and MBD components. In this architecture, EBL was used to learn diagnostic rules. But the diagnoses proposed by the rules could be erroneous. So constraint suspension testing was used to check all proposed diagnoses. Insisting on perfect accuracy causes the performance of this scheme for "learning while doing" to deteriorate rapidly with the size of the device to be diagnosed. In this paper, we describe a method for trading off accuracy for efficiency. In this approach, most diagnosis problems are handled by the associational rules learned from previous problems. Model-based reasoning and learning are activated only when performance drops below a given threshold. We present empirical results on circuits of increasing number of components illustrating how this approach scales up
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A qualitative logic of decision
An important aspect of intelligent behavior is the ability to reason, make decisions, and act in spite of uncertainty. This paper presents a qualitative logic of decision that supports decision-making under uncertainty. To be specific, the paper presents a knowledge representation language based upon subjective Bayesian decision theory that aims to capture some aspects of common-sense reasoning associated with making decisions about actions. The language addresses the problem of describing justifications of rational choices in situations where the alternatives involve trading off potential losses and gains. The logic and an associated qualitative arithmetic are implemented in an efficient PROLOG program. Examples illustrate their use in several concrete decision-making situations
Finite element modelling of shear connection for steel-concrete composite girders
The main objective of this thesis is to develop effective 3-dimensional finite element models to trace the behaviour of headed stud shear connectors in composite girders with
solid slabs and precast hollow core slabs. The finite element package ABAQUS was used to conduct the analysis. Push-off tests with both types of slabs were simulated
taking into consideration all material nonlinearities of the components. The models are able to predict the headed shear stud capacity, the load-slip characteristic of the shear connection and modes of failure. The results obtained show good agreement with specified data from Codes of practice and results of available numerical and
experimental literature. Parametric studies were carried out using both models to investigate the effects of the change in different parameters on the behaviour of shear
connections.
Full-scale push-off tests with solid and precast hollow core slabs have been carried out to verify the finite element models. The shear connection capacity, load-slip curves and modes of failure were detected from experimental investigation. Both numerical and experimental results were compared and good agreement has been achieved. The
comparison has shown that the model is able to predict accurately the behaviour of headed studs in composite girders with both types of slabs.
The non-linear load-slip characteristics of the headed shear stud connector obtained from FE models of push-off tests were used in modelling the structural behaviour of
composite steel-solid slab concrete and steel-precast hollow core slab girders. A finite element model has been developed for the analysis of each type. The models took into account the non-linear behaviour of concrete slab, steel beam and shear connectors. The accuracy and efficiency of the models have been demonstrated by comparing finite element results with available published experimental and numerical research. An effective parametric study for the evaluation of the effective width for steel-precast concrete slab composite girders is presented
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