Carlos J. Alonso González

Possible conflicts: a compilation technique for consistency-based diagnosis

Consistency-based diagnosis is one of the most widely used approaches to model-based diagnosis within the artificial intelligence community. It is usually carried out through an iterative cycle of behavior prediction, conflict detection, candidate generation, and candidate refinement. In that process conflict detection has proven to be a nontrivial step from the theoretical point of view. For this reason, many approaches to consistency-based diagnosis have relied upon some kind of dependency-recording. These techniques have had different problems, specially when they were applied to diagnose dynamic systems. Recently, offline dependency compilation has established itself as a suitable alternative approach to online dependency-recording. In this paper we propose the possible conflict concept as a compilation technique for consistency-based diagnosis. Each possible conflict represents a subsystem within system description containing minimal analytical redundancy and being capable to become a conflict. Moreover, the whole set of possible conflicts can be computed offline with no model evaluation. Once we have formalized the possible conflict concept, we explain how possible conflicts can be used in the consistency-based diagnosis framework, and how this concept can be easily extended to diagnose dynamic systems. Finally, we analyze its relation to conflicts in the general diagnosis engine (GDE) framework and compare possible conflicts with other compilation techniques, especially with analytical redundancy relations (ARRs) obtained through structural analysis. Based on results from these comparisons we provide additional insights in the work carried out within the BRIDGE community to provide a common framework for model-based diagnosis for both artificial intelligence and control engineering approaches.

Learning First Order Logic Time Series Classifiers: Rules and Boosting

A method for learning multivariate time series classifiers by inductive logic programming is presented. Two types of background predicate that are suited for this task are introduced: interval based predicates, such as always, and distance based, such as the euclidean distance. Special purpose techniques are presented that allow these predicates to be handled efficiently when performing top-down induction. Furthermore, by employing boosting, the accuracy of the resulting classifiers can be improved significantly. Experiments on several different datasets show that the proposed method is highly competitive with previous approaches.

Applying Boosting to Similarity Literals for Time Series Classification

A supervised classification method for temporal series, even multivariate, is presented. It is based on boosting very simple classifiers, which consists only of one literal. The proposed predicates are based in similarity functions (i.e., euclidean and dynamic time warping) between time series. The experimental validation of the method has been done using different datasets, some of them obtained from the UCI repositories. The results are very competitive with the reported in previous works. Moreover, their comprehensibility is better than in other approaches with similar results, since the classifiers are formed by a weighted sequence of literals.

Basic tasks for knowledge-based supervision in process control

Abstract A new tasks taxonomy for knowledge-based global supervision (GS) of continuous industrial processes is introduced in this work. Possible required tasks are specified together with the analysis of their dimensions, which should be useful in the selection of the final capabilities of supervision. Moreover, these dimensions would help end-users and designers when comparing different systems. Several methodologies based on concepts such as generic task, generic operation or heuristic classification have been proposed to transform knowledge-based system (KBS) development in a systematic knowledge engineering activity. These approaches have been quite successful in domains such as medicine or mineral prospecting, identifying a large number of tasks that experts in the domain articulate to solve the problem. However, this was not the case in the process control area. The selection of tasks and their capabilities is the first step to be taken, even before choosing a KBS analysis and design methodology. Authors found a lack of facilities to do this selection in the aforementioned approaches when they tried to develop a global supervision tool in a beet sugar factory in Spain. Hence, this article describes an attempt to fill this gap. Moreover, it shows how this taxonomy supported the analysis and design stages of a supervision tool in the mentioned industrial application.

On Line Industrial Diagnosis: An Attempt to Apply Artificial Intelligence Techniques to Process Control

Three Knowledge Based Systems (KBSs), performing diagnosis and integrated in a Knowledge Based Supervisory System (KBSS), are presented. The systems work on line in a continuos process factory and one of them is routinely used at the control room. The paper summarises the conceptual framework that guided the design of the KBSS, describing later the fault identification module of each diagnostician. Specially relevant were the different approaches tried to deal with the dynamic nature of the problem, looking for a good trade off between expressiveness and simplicity of the final system. Some experimental results, obtained from actual system performance at a beet sugar factory, and major conclusions, are included.

DIAGNOSIS OF CONTINUOUS DYNAMIC SYSTEMS: INTEGRATING CONSISTENCY BASED DIAGNOSIS WITH MACHINE-LEARNING TECHNIQUES

Abstract This paper describes an integrated approach to diagnosis of complex dynamic systems, combining model based diagnosis with machine learning techniques, proposing a simple framework to make them cooperate, hence improving the diagnosis capabilities of each individual method. First step in the diagnosis process resorts to consistency-based diagnosis, via possible conflicts, which allows fault detection and localization without prior knowledge of the device fault modes. In the second step, a classification system, obtained via machine learning techniques, is used to propose a ranked sequence of fault modes, coherent with the previous localization step. This cycle iterates in time, generating more focused and precise diagnosis as new data are available. A laboratory plant has been built to test this proposal. Simulation results are shown for a total number of 14 different faults.

Building RBF Networks for Time Series Classification by Boosting

This work presents a learning system for the classification of multivariate time series. This classification is useful in domains such as biomedical signals [9], continuous systems diagnosis [2] or data mining in temporal databases [3] .

Learning Classification RBF Networks by Boosting

This work proposes a novel method for constructing RBF networks, based on boosting. The task assigned to the base learner is to select a RBF, while the boosting algorithm combines linearly the different RBFs. For each iteration of boosting a new neuron is incorporated into the network. The method for selecting each RBF is based on randomly selecting several examples as the centers, considering the distances to these center as attributes of the examples and selecting the best split on one of these attributes. This selection of the best split is done in the same way than in the construction of decision trees. The RBF is computed from the center (attribute) and threshold selected. This work is not about using RBFNs as base learners for boosting, but about constructing RBFNs by boosting.

Student Modelling and Interface Design in SIAL

SIAL is an intelligent system for the learning of first order logic. It has been developed as a laboratory tool for Artificial Intelligence courses of Computer Science curricula. Student modelling in this domain is a complex task, but it is necessary if we want to have a good interaction with the student. Interface design has a main role in the system, not only because it configures the environment in which the student works, but also because it becomes part of the error diagnosis process. In this paper we present how we have faced both problems in SIAL.

Diagnosis de Sistemas Dinámicos: Un Modelo de Conocimiento que Permite Integrar la Evolución del Sistema en el Proceso de Diagnosis

A KNOWLWDGE BASED MODEL FOR ON LINE DIAGNOSIS OF COMPLEX DYNAMIC SYSTEM IS PROPOSED. DOMAIN KNOWLEDGE IS MODELLED VIA CAUSAL NETWORKS WHICH CONSIDER TEMPORAL RELATIONSHIPS AMONG SYMPTOMS AND CAUSES. INTERFERENCE AND TASK KNOWLEDGES IS INCLUIDED USING THE COMMONKADS METHODOLOGY. THE MAIN FEATURE OF THE PROPOSAL IS THAT THE DIAGNOSIS TASK IS ABLE TO TRACK THE EVOLUTION OF THE SYSTEM INCORPORATING NEW SYMPTOMS TO THE DIAGNOSIS PROCESS. DIAGNOSSIS IS CONCEIVED AS A TASK TO BE CARRY OUT BY SUPERVISOR SYSTEMS, WHICH COULS SELECT THE CURRENT CAUSAL NETWORK TO PERFORM DIAGNOSIS, DEPENDING ON SYSTEM CONFIGURATION AND OPERATION POINT. ALTHOUGH THE PROPOSAL HAS BEEN DESIGN FOR DIAGNOSIS OS INDUSTRIAL CONTINUOUS PROCESSES, THE KNOWLEDGE MODEL IS DOMAIN IN