125 research outputs found

    Scaling Ant Colony Optimization with Hierarchical Reinforcement Learning Partitioning

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    This research merges the hierarchical reinforcement learning (HRL) domain and the ant colony optimization (ACO) domain. The merger produces a HRL ACO algorithm capable of generating solutions for both domains. This research also provides two specific implementations of the new algorithm: the first a modification to Dietterich\u27s MAXQ-Q HRL algorithm, the second a hierarchical ACO algorithm. These implementations generate faster results, with little to no significant change in the quality of solutions for the tested problem domains. The application of ACO to the MAXQ-Q algorithm replaces the reinforcement learning, Q-learning and SARSA, with the modified ant colony optimization method, Ant-Q. This algorithm, MAXQ-AntQ, converges to solutions not significantly different from MAXQ-Q in 88% of the time. This research then transfers HRL techniques to the ACO domain and traveling salesman problem (TSP). To apply HRL to ACO, a hierarchy must be created for the TSP. A data clustering algorithm creates these subtasks, with an ACO algorithm to solve the individual and complete problems. This research tests two clustering algorithms, k-means and G-means. The results demonstrate the algorithm with data clustering produces solutions 85-95% faster but with 5-10% decrease in solution quality

    A Bird’s Eye View of Human Language Evolution

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    Comparative studies of linguistic faculties in animals pose an evolutionary paradox: language involves certain perceptual and motor abilities, but it is not clear that this serves as more than an input–output channel for the externalization of language proper. Strikingly, the capability for auditory–vocal learning is not shared with our closest relatives, the apes, but is present in such remotely related groups as songbirds and marine mammals. There is increasing evidence for behavioral, neural, and genetic similarities between speech acquisition and birdsong learning. At the same time, researchers have applied formal linguistic analysis to the vocalizations of both primates and songbirds. What have all these studies taught us about the evolution of language? Is the comparative study of an apparently species-specific trait like language feasible? We argue that comparative analysis remains an important method for the evolutionary reconstruction and causal analysis of the mechanisms underlying language. On the one hand, common descent has been important in the evolution of the brain, such that avian and mammalian brains may be largely homologous, particularly in the case of brain regions involved in auditory perception, vocalization, and auditory memory. On the other hand, there has been convergent evolution of the capacity for auditory–vocal learning, and possibly for structuring of external vocalizations, such that apes lack the abilities that are shared between songbirds and humans. However, significant limitations to this comparative analysis remain. While all birdsong may be classified in terms of a particularly simple kind of concatenation system, the regular languages, there is no compelling evidence to date that birdsong matches the characteristic syntactic complexity of human language, arising from the composition of smaller forms like words and phrases into larger ones

    Proceedings of the 21st Conference on Formal Methods in Computer-Aided Design – FMCAD 2021

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    The Conference on Formal Methods in Computer-Aided Design (FMCAD) is an annual conference on the theory and applications of formal methods in hardware and system verification. FMCAD provides a leading forum to researchers in academia and industry for presenting and discussing groundbreaking methods, technologies, theoretical results, and tools for reasoning formally about computing systems. FMCAD covers formal aspects of computer-aided system design including verification, specification, synthesis, and testing

    Technology 2003: The Fourth National Technology Transfer Conference and Exposition, volume 2

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    Proceedings from symposia of the Technology 2003 Conference and Exposition, Dec. 7-9, 1993, Anaheim, CA, are presented. Volume 2 features papers on artificial intelligence, CAD&E, computer hardware, computer software, information management, photonics, robotics, test and measurement, video and imaging, and virtual reality/simulation

    Computer Aided Verification

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    This open access two-volume set LNCS 13371 and 13372 constitutes the refereed proceedings of the 34rd International Conference on Computer Aided Verification, CAV 2022, which was held in Haifa, Israel, in August 2022. The 40 full papers presented together with 9 tool papers and 2 case studies were carefully reviewed and selected from 209 submissions. The papers were organized in the following topical sections: Part I: Invited papers; formal methods for probabilistic programs; formal methods for neural networks; software Verification and model checking; hyperproperties and security; formal methods for hardware, cyber-physical, and hybrid systems. Part II: Probabilistic techniques; automata and logic; deductive verification and decision procedures; machine learning; synthesis and concurrency. This is an open access book

    Route choice and traffic equilibrium modeling in multi-modal and activity-based networks

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    Que ce soit pour aller au travail, faire du magasinage ou participer Ă  des activitĂ©s sociales, la mobilitĂ© fait partie intĂ©grante de la vie quotidienne. Nous bĂ©nĂ©ficions Ă  cet Ă©gard d'un nombre grandissant de moyens de transports, ce qui contribue tant Ă  notre qualitĂ© de vie qu'au dĂ©veloppement Ă©conomique. NĂ©anmoins, la demande croissante de mobilitĂ©, Ă  laquelle s'ajoutent l'expansion urbaine et l'accroissement du parc automobile, a Ă©galement des rĂ©percussions nĂ©gatives locales et globales, telles que le trafic, les nuisances sonores, et la dĂ©gradation de l'environnement. Afin d'attĂ©nuer ces effets nĂ©fastes, les autoritĂ©s cherchent Ă  mettre en oeuvre des politiques de gestion de la demande avec le meilleur rĂ©sultat possible pour la sociĂ©tĂ©. Pour ce faire, ces derniĂšres ont besoin d'Ă©valuer l'impact de diffĂ©rentes mesures. Cette perspective est ce qui motive le problĂšme de l'analyse et la prĂ©diction du comportement des usagers du systĂšme de transport, et plus prĂ©cisĂ©ment quand, comment et par quel itinĂ©raire les individus dĂ©cident de se dĂ©placer. Cette thĂšse a pour but de dĂ©velopper et d'appliquer des modĂšles permettant de prĂ©dire les flux de personnes et/ou de vĂ©hicules dans des rĂ©seaux urbains comportant plusieurs modes de transport. Il importe que de tels modĂšles soient supportĂ©s par des donnĂ©es, gĂ©nĂšrent des prĂ©dictions exactes, et soient applicables Ă  des rĂ©seaux rĂ©els. Dans la pratique, le problĂšme de prĂ©diction de flux se rĂ©sout en deux Ă©tapes. La premiĂšre, l'analyse de choix d'itinĂ©raire, a pour but d'identifier le chemin que prendrait un voyageur dans un rĂ©seau pour effectuer un trajet entre un point A et un point B. Pour ce faire, on estime Ă  partir de donnĂ©es les paramĂštres d'une fonction de coĂ»t multi-attribut reprĂ©sentant le comportement des usagers du rĂ©seau. La seconde Ă©tape est celle de l'affectation de trafic, qui distribue la demande totale dans le rĂ©seau de façon Ă  obtenir un Ă©quilibre, c.-Ă -d. un Ă©tat dans lequel aucun utilisateur ne souhaite changer d'itinĂ©raire. La difficultĂ© de cette Ă©tape consiste Ă  modĂ©liser la congestion du rĂ©seau, qui dĂ©pend du choix de route de tous les voyageurs et affecte simultanĂ©ment la fonction de coĂ»t de chacun. Cette thĂšse se compose de quatre articles soumis Ă  des journaux internationaux et d'un chapitre additionnel. Dans tous les articles, nous modĂ©lisons le choix d'itinĂ©raire d'un individu comme une sĂ©quence de choix d'arcs dans le rĂ©seau, selon une approche appelĂ©e modĂšle de choix d'itinĂ©raire rĂ©cursif. Cette mĂ©thodologie possĂšde d'avantageuses propriĂ©tĂ©s, comme un estimateur non biaisĂ© et des procĂ©dures d'affectation rapides, en Ă©vitant de gĂ©nĂ©rer des ensembles de chemins. NĂ©anmoins, l'estimation de tels modĂšles pose une difficultĂ© additionnelle puisqu'elle nĂ©cessite de rĂ©soudre un problĂšme de programmation dynamique imbriquĂ©, ce qui explique que cette approche ne soit pas encore largement utilisĂ©e dans le domaine de la recherche en transport. Or, l'objectif principal de cette thĂšse est de rĂ©pondre des dĂ©fis liĂ©s Ă  l'application de cette mĂ©thodologie Ă  des rĂ©seaux multi-modaux. La force de cette thĂšse consiste en des applications Ă  Ă©chelle rĂ©elle qui soulĂšvent des dĂ©fis computationnels, ainsi que des contributions mĂ©thodologiques. Le premier article est un tutoriel sur l'analyse de choix d'itinĂ©raire Ă  travers les modĂšles rĂ©cursifs susmentionnĂ©s. Les contributions principales sont de familiariser les chercheur.e.s avec cette mĂ©thodologie, de donner une certaine intuition sur les propriĂ©tĂ©s du modĂšle, d'illustrer ses avantages sur de petits rĂ©seaux, et finalement de placer ce problĂšme dans un contexte plus large en tissant des liens avec des travaux dans les domaines de l'optimisation inverse et de l'apprentissage automatique. Deux articles et un chapitre additionnel appartiennent Ă  la catĂ©gorie de travaux appliquant la mĂ©thodologie prĂ©cĂ©demment dĂ©crite sur des rĂ©seaux rĂ©els, de grande taille et multi-modaux. Ces applications vont au-delĂ  des prĂ©cĂ©dentes Ă©tudes dans ce contexte, qui ont Ă©tĂ© menĂ©es sur des rĂ©seaux routiers simples. PremiĂšrement, nous estimons des modĂšles de choix d'itinĂ©raire rĂ©cursifs pour les trajets de cyclistes, et nous soulignons certains avantages de cette mĂ©thodologie dans le cadre de la prĂ©diction. Nous Ă©tendons ensuite ce premier travail afin de traiter le cas d'un rĂ©seau de transport public comportant plusieurs modes. Enfin, nous considĂ©rons un problĂšme de prĂ©diction de demande plus large, oĂč l'on cherche Ă  prĂ©dire simultanĂ©ment l'enchaĂźnement des trajets quotidiens des voyageurs et leur participation aux activitĂ©s qui motivent ces dĂ©placements. Finalement, l'article concluant cette thĂšse concerne la modĂ©lisation d'affectation de trafic. Plus prĂ©cisĂ©ment, nous nous intĂ©ressons au calcul d'un Ă©quilibre dans un rĂ©seau oĂč chaque arc peut possĂ©der une capacitĂ© finie, ce qui est typiquement le cas des rĂ©seaux de transport public. Cet article apporte d'importantes contributions mĂ©thodologiques. Nous proposons un modĂšle markovien d'Ă©quilibre de trafic dit stratĂ©gique, qui permet d'affecter la demande sur les arcs du rĂ©seau sans en excĂ©der la capacitĂ©, tout en modĂ©lisant comment la probabilitĂ© qu'un arc atteigne sa capacitĂ© modifie le choix de route des usagers.Traveling is an essential part of daily life, whether to attend work, perform social activities, or go shopping among others. We benefit from an increasing range of available transportation services to choose from, which supports economic growth and contributes to our quality of life. Yet the growing demand for travel, combined with urban sprawl and increasing vehicle ownership rates, is also responsible for major local and global externalities, such as degradation of the environment, congestion and noise. In order to mitigate the negative impacts of traveling while weighting benefits to users, transportation planners seek to design policies and improve infrastructure with the best possible outcome for society as a whole. Taking effective actions requires to evaluate the impact of various measures, which necessitates first to understand and predict travel behavior, i.e., how, when and by which route individuals decide to travel. With this background in mind, this thesis has the objective of developing and applying models to predict flows of persons and/or vehicles in multi-modal transportation networks. It is desirable that such models be data-driven, produce accurate predictions, and be applicable to real networks. In practice, the problem of flow prediction is addressed in two separate steps, and this thesis is concerned with both. The first, route choice analysis, is the problem of identifying the path a traveler would take in a network. This is achieved by estimating from data a parametrized cost function representing travelers' behavior. The second step, namely traffic assignment, aims at distributing all travelers on the network's paths in order to find an equilibrium state, such that no traveler has an interest in changing itinerary. The challenge lies in taking into account the effect of generated congestion, which depends on travelers' route choices while simultaneously impacting their cost of traveling. This thesis is composed of four articles submitted to international journals and an additional chapter. In all the articles of the thesis, we model an individual's choice of path as a sequence of link choices, using so-called recursive route choice models. This methodology is a state-of-the-art framework which is known to possess the advantage of unbiased parameter estimates and fast assignment procedures, by avoiding to generate choice sets of paths. However, it poses the additional challenge of requiring one to solve embedded dynamic programming problems, and is hence not widely used in the transportation community. This thesis addresses practical and theoretical challenges related to applying this methodological framework to real multi-modal networks. The strength of this thesis consists in large-scale applications which bear computational challenges, as well as some methodological contributions to this modeling framework. The first article in this thesis is a tutorial on predicting and analyzing path choice behavior using recursive route choice models. The contribution of this article is to familiarize researchers with this methodology, to give intuition on the model properties, to illustrate its advantages through examples, and finally to position this modeling framework within a broader context, by establishing links with recently published work in the inverse optimization and machine learning fields. Two articles and an additional chapter can be categorized as applications of the methodology to estimate parameters of travel demand models in several large, real, and/or multi-dimensional networks. These applications go beyond previous studies on small physical road networks. First, we estimate recursive models for the route choice of cyclists and we demonstrate some advantages of the recursive models in the context of prediction. We also provide an application to a time-expanded public transportation networks with several modes. Then, we consider a broader travel demand problem, in which decisions regarding daily trips and participation in activities are made jointly. The latter is also modeled with recursive route choice models by considering sequences of activity, destination and mode choices as paths in a so-called supernetwork. Finally, the subject of the last article in this thesis is traffic assignment. More precisely, we address the problem of computing a traffic equilibrium in networks with strictly limited link capacities, such as public transport networks. This article provides important methodological contributions. We propose a strategic Markovian traffic equilibrium model which assigns flows to networks without exceeding link capacities while realistically modeling how the risk of not being able to access an arc affects route choice behavior

    Proceedings of the Second Joint Technology Workshop on Neural Networks and Fuzzy Logic, volume 2

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    Documented here are papers presented at the Neural Networks and Fuzzy Logic Workshop sponsored by NASA and the University of Texas, Houston. Topics addressed included adaptive systems, learning algorithms, network architectures, vision, robotics, neurobiological connections, speech recognition and synthesis, fuzzy set theory and application, control and dynamics processing, space applications, fuzzy logic and neural network computers, approximate reasoning, and multiobject decision making
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