In discrete dynamical systems change results from actions. As such, given a set of observations, diagnoses often take the form of posited events that result in the observed behaviour. In this paper we revisit formal characterizations of diagnosis, and their relationship to planning. We do so from both a theoretical and a computational perspective. In particular, we extend the characterization of diagnosis to deal with the case of incomplete information, and rich preferences. We also explore the use of state-of-the-art planning technology for the automated generation of diagnoses. Examining several classes of diagnosis problems, we provide both proof of concept and benchmark experiments, the latter showing superior performance to a leading diagnosis engine. Our findings help support the hypothesis that planning technology holds great promise for efficient generation of diagnoses.

Sequential diagnosis takes measurements of an abnormal system to identify faulty components, where the goal is to reduce the diagnostic cost , defined here as the number of measurements. To propose measurement points, previous work employs a heuristic based on reducing the entropy over a set of diagnoses , which can be impractical when the set of diagnoses is too large. Focusing on a smaller set of probable diagnoses scales the approach but generally leads to increased diagnostic cost. We propose a new diagnostic framework employing three new techniques — a more efficient heuristic for measurement point selection, abstraction-based sequential diagnosis, and component cloning — which scales to large systems with good performance in terms of diagnostic cost.

This paper extends model-based diagnosis (MBD) (de Kleer and Williams 1987; Reiter 1987) to systems with hidden interaction faults. An interaction fault is present if an interaction among a set of components leads to an observable failure, even though each individual component individually meets the specifications. A naive approach to address interaction faults is to simply account for all possible interaction faults in the system model. However, the naive approach presumes that all possible faults, both component and interaction faults, are known and addressed in the model. This assumption is violated by most real world systems, such as shorts in circuits (Davis 1984) or unmodeled connections (de Kleer 2007). That leads to incomplete system models, hence possibly hidden interaction faults. The problem of hidden interactions has been known for a long time (Davis 1984), but until now no general solution has been proposed. Instead of pushing for complete models (Preist and Welham 1990) or relying on additional structural information (Davis 1984; Bottcher 1995; de Kleer 2007) we approach the challenge differently. We allow system models to be incomplete and introduce a general, domain independent extension to model-based diagnosis to account for resulting hidden interaction faults. This extends model-based diagnosis to systems with incomplete models, in particular to models with incomplete structural information. In the paper, we demonstrate the proposed diagnosis framework on a logic circuit with a hidden interaction fault.

Virtual teams that span multiple geographic and cultural boundaries have become commonplace in numerous organizations due to the competitive advantages they provide in human resources, products, financial means, knowledge sharing and many others. However, the promises of multinational and multicultural (MNMC) distributed teams are accompanied by a number of challenges. Many research studies have suggested that one of the most challenging barriers to the effective implementation of MNMC distributed teams is culture. In this study, data collected from the experiment conducted by the NATO RTO Human Factors and Medicine Panel Research Task Group (HFM-138/RTG) on “Adapatability in Multinational Coalitions” has been analyzed to study the effects of cultural diversity on team planning and communication. Fuzzy decision trees have been derived to model the effects, and information visualization techniques are used to facilitate understanding of the derived classification patterns. Results of the research suggest that there are no single and straightforward conclusions on how cultural diversity affects team planning and communication. Different dimensions of culture values interact in influencing team behaviors. However, diversities in power distance and masculinity seem to play more influential roles than others.

The failure diagnosis for {\it discrete event systems} (DESs) has been given considerable attention in recent years. Both on-line and off-line diagnostics in the framework of DESs was first considered by Lin Feng in 1994, and particularly an algorithm for diagnosability of DESs was presented. Motivated by some existing problems to be overcome in previous work, in this paper, we investigate the minimal cost algorithm for diagnosability of DESs. More specifically: (i) we give a generic method for judging a system's off-line diagnosability, and the complexity of this algorithm is polynomial-time; (ii) and in particular, we present an algorithm of how to search for the minimal set in all observable event sets, whereas the previous algorithm may find {\it non-minimal} one.

The estimation of linear causal models (also known as structural equation models) from data is a well-known problem which has received much attention in the past. Most previous work has, however, made an explicit or implicit assumption of gaussianity, limiting the identifiability of the models. We have recently shown (Shimizu et al, 2005; Hoyer et al, 2006) that for non-gaussian distributions the full causal model can be estimated in the no hidden variables case. In this contribution, we discuss the estimation of the model when confounding latent variables are present. Although in this case uniqueness is no longer guaranteed, there is at most a finite set of models which can fit the data. We develop an algorithm for estimating this set, and describe numerical simulations which confirm the theoretical arguments and demonstrate the practical viability of the approach. Full Matlab code is provided for all simulations.

Model-based planning often presumes a static system model, while in a practice physical system may evolve or drift over time. This paper proposes the idea of pervasive model adaptation in a production system, where the model is dynamically updated using observation of production output. The core idea is the interplay between model adaptation and production planning. We seek plans which simultaneously serve the goals of achieving high productivity for production, and information gathering for model adaptation. We use a modular printing example to illustrate issues such as formulation of the information criterion and search strategy for informative plans. The idea of pervasive adaptation can be further extended to improve long term productivity in production systems.

Several explanation and interpretation tasks, such as diagnosis, plan recognition and image interpretation, can be formalized as abductive and consistency reasoning. The interpretation task is usually executed for the purpose of performing actions, e.g., in diagnosis, repair actions or therapy. In some cases actions are the only or the main way for discriminating among alternative explanations. Some proposals address the problem based on a task-independent representation of a domain which includes an ontology or taxonomy of hypotheses and actions. In this paper we rely on the same type of representation, and we point out the role of abstractions in an iterative process where, like in model-based diagnosis and troubleshooting, further observations or actions are proposed to achieve the overall goal of discriminating among candidate hypotheses and, more importantly, performing the appropriate actions for the case at hand. Discrimination is performed up to an appropriate level which depends on the cost of actions (e.g. repair actions or therapy) to be taken based on the results of abduction, and on the cost of additional observations, which should be balanced with the benefits, in terms of more suitable actions, of better discrimination. Abstractions have a significant impact on this trade-off, given that the cost of observing the same phenomenon at different levels of abstraction may be quite different, and choosing a generic action, without information on which specific instance is most appropriate, is, in general, suboptimal.

Generating minimal hitting sets of a collection of sets is known to be NP-hard, necessitating heuristic approaches to handle large problems. In this paper a low-cost, approximate minimal hitting set (MHS) algorithm, coined Staccato, is presented. Staccato uses a heuristic function, borrowed from a lightweight, statistics-based software fault localization approach, to guide the MHS search. Given the nature of the heuristic function, Staccato is specially tailored to model-based diagnosis problems (where each MHS solution is a diagnosis to the problem), although well-suited for other application domains as well. We apply Staccato in the context of model-based diagnosis and show that even for small problems our approach is orders of magnitude faster than the brute-force approach, while still capturing all important solutions. Furthermore, due to its low cost complexity, we also show that Staccato is amenable to large problems including millions of variables.

In Model-Based Diagnosis (MBD), the problem of computing a diagnosis in a strong-fault model (SFM) is computationally much harder than in a weak-fault model (WFM). For example, in propositional Horn models, computing the first minimal diagnosis in a weak-fault model (WFM) is in P but is NP-hard for strong-fault models. As a result, SFM problems of practical significance have not been studied in great depth within the MBD community. In this paper we describe an algorithm that renders the problem of computing a diagnosis in several important SFM subclasses no harder than a similar computation in a WFM. We propose an approach for efficiently computing minimal diagnoses for these subclasses of SFM that extends existing conflict-based algorithms like GDE (Sherlock) and CDA*. Experiments on ISCAS85 combinational circuits show (1) inference speedups with CDA* of up to a factor of 8, and (2) an average of 28% reduction in the average conflict size, at the price of an extra low-polynomial-time consistency check for a candidate diagnosis.