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

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

On the construction of hierarchic models

One of the main problems in the field of model-based diagnosis of technical systems today is finding the most useful model or models of the system being diagnosed. Often, a model showing the physical components and the connections between them is all that is available. As systems grow larger and larger, the run-time performance of diagnostic algorithms decreases considerably when using these detailed models. A solution to this problem is using a hierarchic model. This allows us to first diagnose the system using an abstract model, and then use this solution to guide the diagnostic process using a more detailed model. The main problem with this approach is acquiring the hierarchic model. We give a generic hierarchic diagnostic algorithm and show how the use of certain classes of hierarchic models can increase the performance of this algorithm. We then present linear time algorithms for the automatic construction of these hierarchic models, using the detailed model and extra information about cost of probing points and invertibility of components

Recommending treatments for comorbid patients using word-based and phrase-based alignment methods

The problem of finding treatments for patients diagnosed with multiple diseases (i.e.~a comorbidity) is an important research topic in the medical literature. In this paper, we propose a new data driven approach to recommend treatments for these comorbidities using word-based and phrase-based alignment methods. The most popular methods currently rely on combining specific information from individual diseases (e.g.~procedures, tests, etc.), then aim to detect and repair the conflicts that arise in the combined treatments. This proves to be a challenge especially in the cases where the studied comorbidities contain large numbers of diseases. In contrast, our methods rely on training a translation model using previous medical records to find treatments for newly diagnosed comorbidities. We also explore the use of additional criteria in the form of a drug interactions penalty and a treatment popularity score to select the best treatment in the case where multiple valid translations for a single comorbidity are available

A Topological-Based Method for Allocating Sensors by Using CSP Techniques

Model-based diagnosis enables isolation of faults of a system. The diagnosis process uses a set of sensors (observations) and a model of the system in order to explain a wrong behaviour. In this work, a new approach is proposed with the aim of improving the computational complexity for isolating faults in a system. The key idea is the addition of a set of new sensors which allows the improvement of the diagnosability of the system. The methodology is based on constraint programming and a greedy method for improving the computational complexity of the CSP resolution. Our approach maintains the requirements of the user (detectability, diagnosability,. . .).Ministerio de Ciencia y Tecnología DPI2003-07146-C02-0