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The diagnosis of a discrete-event system is the problem of computing possible behaviors of the system given observations of the actual behavior, and testing whether the behaviors are normal or faulty. We show how the diagnosis problems can be translated into the propositional satisfiability problem (SAT) and solved by algorithms for SAT. Our experiments… (More)

Pathfinding in uniform-cost grid environments is a problem commonly found in application areas such as robotics and video games. The state-of-the-art is dominated by hierarchical pathfinding algorithms which are fast and have small memory overheads but usually return suboptimal paths. In this paper we present a novel search strategy, specific to grids,… (More)

When dealing with real systems, it is unrealistic to suppose that observations can be totally ordered according to their emission dates. The partially ordered observations and the system are thus both represented as finite-state machines (or automata) and the diagnosis formally defined as the synchronized composition of the model with the observations. The… (More)

We show how testing whether a system is diagnos-able can be reduced to the satisfiability problem and how satisfiability algorithms yield a very efficient approach to testing diagnosability. Diagnosability is the question whether it is always possible to know whether a given system has exhibited a failure behavior. This is a basic question that underlies… (More)

Any-angle pathfinding is a common problem from robotics and computer games: it requires finding a Eu-clidean shortest path between a pair of points in a grid map. Prior research has focused on approximate online solutions. A number of exact methods exist but they all require supra-linear space and preprocessing time. In this paper we describe Anya: a new… (More)

It is well-known that the size of the model is a bottleneck when using model-based approaches to diagnose complex systems. To answer this problem, decentralised/distributed approaches have been proposed. Another problem, which is far less considered, is the size of the diagnosis itself. However , it can be huge enough, especially in the case of on-line… (More)

—Recent years have witnessed significant interest in convex relaxations of the power flows, several papers showing that the second-order cone relaxation is tight for tree networks under various conditions on loads or voltages. This paper shows that AC-feasibility, i.e., to find whether some generator dispatch can satisfy a given demand, is NP-Hard for tree… (More)

We address the problem of predicting events' occurrences in partially observable timed systems modelled by timed automata. Our contribution is many-fold: 1) we give a definition of bounded predictability, namely k-predictability, that takes into account the minimum delay between the prediction and the actual event's occurrence; 2) we show that… (More)

We extend the decentralised/distributed approach of diagnosis of discrete-event systems modeled using automata. The goal is to avoid computing a global diagnosis, which is expensive, and to perform local diagnoses instead. To still ensure global consistency, we transform the topology of the system into a junction tree where each vertex represents a… (More)

In the satisfiability domain, it is well-known that a SAT algorithm may solve a problem instance easily and another instance hardly, whilst these two instances are equivalent CNF encodings of the original problem. Moreover, different algorithms may disagree on which encoding makes the problem easier to solve. In this paper, we focus on the CNF encoding of… (More)