Real-time Regular Expression Matching

This paper is devoted to finite state automata, regular expression matching, pattern recognition, and the exponential blow-up problem, which is the growing complexity of automata exponentially depending on regular expression length. This paper presents a theoretical and hardware solution to the exponential blow-up problem for some complicated classes of regular languages, which caused severe limitations in Network Intrusion Detection Systems work. The article supports the solution with theorems on correctness and complexity.Comment: 17 pages, 11 figure

A Semi-automatic and Low Cost Approach to Build Scalable Lemma-based Lexical Resources for Arabic Verbs

International audienceThis work presents a method that enables Arabic NLP community to build scalable lexical resources. The proposed method is low cost and efficient in time in addition to its scalability and extendibility. The latter is reflected in the ability for the method to be incremental in both aspects, processing resources and generating lexicons. Using a corpus; firstly, tokens are drawn from the corpus and lemmatized. Secondly, finite state transducers (FSTs) are generated semi-automatically. Finally, FSTsare used to produce all possible inflected verb forms with their full morphological features. Among the algorithm’s strength is its ability to generate transducers having 184 transitions, which is very cumbersome, if manually designed. The second strength is a new inflection scheme of Arabic verbs; this increases the efficiency of FST generation algorithm. The experimentation uses a representative corpus of Modern Standard Arabic. The number of semi-automatically generated transducers is 171. The resulting open lexical resources coverage is high. Our resources cover more than 70% Arabic verbs. The built resources contain 16,855 verb lemmas and 11,080,355 fully, partially and not vocalized verbal inflected forms. All these resources are being made public and currently used as an open package in the Unitex framework available under the LGPL license

Multi-agent persistent surveillance under temporal logic constraints

This thesis proposes algorithms for the deployment of multiple autonomous agents for persistent surveillance missions requiring repeated, periodic visits to regions of interest. Such problems arise in a variety of domains, such as monitoring ocean conditions like temperature and algae content, performing crowd security during public events, tracking wildlife in remote or dangerous areas, or watching traffic patterns and road conditions. Using robots for surveillance is an attractive solution to scenarios in which fixed sensors are not sufficient to maintain situational awareness. Multi-agent solutions are particularly promising, because they allow for improved spatial and temporal resolution of sensor information. In this work, we consider persistent monitoring by teams of agents that are tasked with satisfying missions specified using temporal logic formulas. Such formulas allow rich, complex tasks to be specified, such as "visit regions A and B infinitely often, and if region C is visited then go to region D, and always avoid obstacles." The agents must determine how to satisfy such missions according to fuel, communication, and other constraints. Such problems are inherently difficult due to the typically infinite horizon, state space explosion from planning for multiple agents, communication constraints, and other issues. Therefore, computing an optimal solution to these problems is often infeasible. Instead, a balance must be struck between computational complexity and optimality. This thesis describes solution methods for two main classes of multi-agent persistent surveillance problems. First, it considers the class of problems in which persistent surveillance goals are captured entirely by TL constraints. Such problems require agents to repeatedly visit a set of surveillance regions in order to satisfy their mission. We present results for agents solving such missions with charging constraints, with noisy observations, and in the presence of adversaries. The second class of problems include an additional optimality criterion, such as minimizing uncertainty about the location of a target or maximizing sensor information among the team of agents. We present solution methods and results for such missions with a variety of optimality criteria based on information metrics. For both classes of problems, the proposed algorithms are implemented and evaluated via simulation, experiments with robots in a motion capture environment, or both

Automata-theoretic and bounded model checking for linear temporal logic

In this work we study methods for model checking the temporal logic LTL. The focus is on the automata-theoretic approach to model checking and bounded model checking. We begin by examining automata-theoretic methods to model check LTL safety properties. The model checking problem can be reduced to checking whether the language of a finite state automaton on finite words is empty. We describe an efficient algorithm for generating small finite state automata for so called non-pathological safety properties. The presented implementation is the first tool able to decide whether a formula is non-pathological. The experimental results show that treating safety properties can benefit model checking at very little cost. In addition, we find supporting evidence for the view that minimising the automaton representing the property does not always lead to a small product state space. A deterministic property automaton can result in a smaller product state space even though it might have a larger number states. Next we investigate modular analysis. Modular analysis is a state space reduction method for modular Petri nets. The method can be used to construct a reduced state space called the synchronisation graph. We devise an on-the-fly automata-theoretic method for model checking the behaviour of a modular Petri net from the synchronisation graph. The solution is based on reducing the model checking problem to an instance of verification with testers. We analyse the tester verification problem and present an efficient on-the-fly algorithm, the first complete solution to tester verification problem, based on generalised nested depth-first search. We have also studied propositional encodings for bounded model checking LTL. A new simple linear sized encoding is developed and experimentally evaluated. The implementation in the NuSMV2 model checker is competitive with previously presented encodings. We show how to generalise the LTL encoding to a more succint logic: LTL with past operators. The generalised encoding compares favourably with previous encodings for LTL with past operators. Links between bounded model checking and the automata-theoretic approach are also explored.reviewe