4,136 research outputs found
Optimizing city-scale traffic through modeling observations of vehicle movements
The capability of traffic-information systems to sense the movement of
millions of users and offer trip plans through mobile phones has enabled a new
way of optimizing city traffic dynamics, turning transportation big data into
insights and actions in a closed-loop and evaluating this approach in the real
world. Existing research has applied dynamic Bayesian networks and deep neural
networks to make traffic predictions from floating car data, utilized dynamic
programming and simulation approaches to identify how people normally travel
with dynamic traffic assignment for policy research, and introduced Markov
decision processes and reinforcement learning to optimally control traffic
signals. However, none of these works utilized floating car data to suggest
departure times and route choices in order to optimize city traffic dynamics.
In this paper, we present a study showing that floating car data can lead to
lower average trip time, higher on-time arrival ratio, and higher
Charypar-Nagel score compared with how people normally travel. The study is
based on optimizing a partially observable discrete-time decision process and
is evaluated in one synthesized scenario, one partly synthesized scenario, and
three real-world scenarios. This study points to the potential of a "living
lab" approach where we learn, predict, and optimize behaviors in the real
world
Diagnosis of an EPS module
Dissertação apresentada na Faculdade de Ciências e Tecnologia da Universidade Nova
de Lisboa para obtenção do grau de Mestre em Engenharia Electrotécnica e ComputadoresThis thesis addresses and contextualizes the problem of diagnostic of an Evolvable
Production System (EPS). An EPS is a complex and lively entity composed of intelligent modules that interact through bio-inspired mechanisms, to ensure high system availability and seamless reconfiguration.
The actual economic situation together with the increasing demand of high quality and low
priced customized products imposed a shift in the production policies of enterprises. Shop floors have to become more agile and flexible to accommodate the new production paradigms. Rather than selling products enterprises are establishing a trend of offering services to explore business
opportunities.
The new production paradigms, potentiated by the advances in Information Technologies
(IT), especially in web related standards and technologies as well as the progressive acceptance of the multi-agent systems (MAS) concept and related technologies, envision collections of modules whose individual and collective function adapts and evolves ensuring the fitness and adequacy of the shop
floor in tackling profitable but volatile business opportunities. Despite the richness of the interactions and the effort set in modelling them, their potential to favour fault propagation and interference, in
these complex environments, has been ignored from a diagnostic point of view.
With the increase of distributed and autonomous components that interact in the execution of processes current diagnostic approaches will soon be insufficient. While current system dynamics are complex and to a certain extent unpredictable the adoption of the next generation of approaches and technologies comes at the cost of a yet increased complexity.Whereas most of the research in such distributed industrial systems is focused in the study and establishment of control structures, the problem of diagnosis has been left relatively unattended.
There are however significant open challenges in the diagnosis of such modular systems including:
understanding fault propagation and ensuring scalability and co-evolution.
This work provides an implementation of a state-of-the-art agent-based interaction-oriented architecture compliant with the EPS paradigm that supports the introduction of a new developed diagnostic algorithm that has the ability to cope with the modern manufacturing paradigm challenges and to provide diagnostic analysis that explores the network dimension of multi-agent systems
Bounding Mean First Passage Times in Population Continuous-Time Markov Chains
We consider the problem of bounding mean first passage times and reachability probabilities for the class of population continuous-time Markov chains, which capture stochastic interactions between groups of identical agents. The quantitative analysis of such models is notoriously difficult since typically neither state-based numerical approaches nor methods based on stochastic sampling give efficient and accurate results. Here, we propose a novel approach that leverages techniques from martingale theory and stochastic processes to generate constraints on the statistical moments of first passage time distributions. These constraints induce a semi-definite program that can be used to compute exact bounds on reachability probabilities and mean first passage times without numerically solving the transient probability distribution of the process or sampling from it. We showcase the method on some test examples and tailor it to models exhibiting multimodality, a class of particularly challenging scenarios from biology
Music as complex emergent behaviour : an approach to interactive music systems
Access to the full-text thesis is no longer available at the author's request, due to 3rd party copyright restrictions. Access removed on 28.11.2016 by CS (TIS).Metadata merged with duplicate record (http://hdl.handle.net/10026.1/770) on 20.12.2016 by CS (TIS).This is a digitised version of a thesis that was deposited in the University Library. If you are the author please contact PEARL Admin ([email protected]) to discuss options.This thesis suggests a new model of human-machine interaction in the domain of non-idiomatic
musical improvisation. Musical results are viewed as emergent phenomena
issuing from complex internal systems behaviour in relation to input from a single
human performer. We investigate the prospect of rewarding interaction whereby a
system modifies itself in coherent though non-trivial ways as a result of exposure to a
human interactor. In addition, we explore whether such interactions can be sustained
over extended time spans. These objectives translate into four criteria for evaluation;
maximisation of human influence, blending of human and machine influence in the
creation of machine responses, the maintenance of independent machine motivations
in order to support machine autonomy and finally, a combination of global emergent
behaviour and variable behaviour in the long run. Our implementation is heavily
inspired by ideas and engineering approaches from the discipline of Artificial Life.
However, we also address a collection of representative existing systems from the
field of interactive composing, some of which are implemented using techniques of
conventional Artificial Intelligence. All systems serve as a contextual background and
comparative framework helping the assessment of the work reported here.
This thesis advocates a networked model incorporating functionality for listening,
playing and the synthesis of machine motivations. The latter incorporate dynamic
relationships instructing the machine to either integrate with a musical context
suggested by the human performer or, in contrast, perform as an individual musical
character irrespective of context. Techniques of evolutionary computing are used to
optimise system components over time. Evolution proceeds based on an implicit
fitness measure; the melodic distance between consecutive musical statements made
by human and machine in relation to the currently prevailing machine motivation.
A substantial number of systematic experiments reveal complex emergent behaviour
inside and between the various systems modules. Music scores document how global
systems behaviour is rendered into actual musical output. The concluding chapter
offers evidence of how the research criteria were accomplished and proposes
recommendations for future research
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