101 research outputs found
Contribuições de aprendizado por reforço em escolha de rota e controle semafórico
A área de sistemas inteligentes de transporte há muito investiga como empregar tecnologias da informação e comunicação a fim de melhorar a eficiência do sistema como um todo. Isso se traduz basicamente em monitorar e gerenciar a oferta (rede viária, semáforos etc.) e a demanda (deslocamentos de pessoas e mercadorias). A esse esforço, mais recentemente, estão sendo adicionadas técnicas de inteligência artificial. Essa tem o potencial de melhorar a utilização da infraestrutura existente, a fim de melhor atender a demanda. Neste trabalho é fornecido um panorama focado especificamente em duas tarefas onde a inteligência artificial tem contribuições relevantes, a saber, controle semafórico e escolha de rotas. Os trabalhos aqui discutidos objetivam otimizar a oferta e/ou distribuir a demanda.The field of of intelligent transportation systems has long investigated how to employ information and communication technologies to improve the efficiency of the system as a whole. This basically means to monitor and manage both supply (traffic network, traffic signals etc.) and demand (vehicles, people and goods). More recently, artificial intelligence techniques are being added to this effort, as they have the potential to improve the usage of existing infrastructure to meet the corresponding demand. In this paper, an overview is given, focusing specifically on two tasks where artificial intelligence has made relevant contributions, namely, traffic signal controls and route choices. The works discussed here aim at optimize the supply and/or distribute the demand
Exploiting Domain Knowledge in Making Delegation Decisions
@inproceedings{conf/admi/EmeleNSP11, added-at = {2011-12-19T00:00:00.000+0100}, author = {Emele, Chukwuemeka David and Norman, Timothy J. and Sensoy, Murat and Parsons, Simon}, biburl = {http://www.bibsonomy.org/bibtex/20a08b683088443f1fd36d6ef28bf6615/dblp}, booktitle = {ADMI}, crossref = {conf/admi/2011}, editor = {Cao, Longbing and Bazzan, Ana L. C. and Symeonidis, Andreas L. and Gorodetsky, Vladimir and Weiss, Gerhard and Yu, Philip S.}, ee = {http://dx.doi.org/10.1007/978-3-642-27609-5_9}, interhash = {1d7e7f8554e8bdb3d43c32e02aeabcec}, intrahash = {0a08b683088443f1fd36d6ef28bf6615}, isbn = {978-3-642-27608-8}, keywords = {dblp}, pages = {117-131}, publisher = {Springer}, series = {Lecture Notes in Computer Science}, timestamp = {2011-12-19T00:00:00.000+0100}, title = {Exploiting Domain Knowledge in Making Delegation Decisions.}, url = {http://dblp.uni-trier.de/db/conf/admi/admi2011.html#EmeleNSP11}, volume = 7103, year = 2011 }Postprin
Quantifying the Impact of Non-Stationarity in Reinforcement Learning-Based Traffic Signal Control
In reinforcement learning (RL), dealing with non-stationarity is a
challenging issue. However, some domains such as traffic optimization are
inherently non-stationary. Causes for and effects of this are manifold. In
particular, when dealing with traffic signal controls, addressing
non-stationarity is key since traffic conditions change over time and as a
function of traffic control decisions taken in other parts of a network. In
this paper we analyze the effects that different sources of non-stationarity
have in a network of traffic signals, in which each signal is modeled as a
learning agent. More precisely, we study both the effects of changing the
\textit{context} in which an agent learns (e.g., a change in flow rates
experienced by it), as well as the effects of reducing agent observability of
the true environment state. Partial observability may cause distinct states (in
which distinct actions are optimal) to be seen as the same by the traffic
signal agents. This, in turn, may lead to sub-optimal performance. We show that
the lack of suitable sensors to provide a representative observation of the
real state seems to affect the performance more drastically than the changes to
the underlying traffic patterns.Comment: 13 page
Temporal Network Analysis of Literary Texts
We study temporal networks of characters in literature focusing on "Alice's
Adventures in Wonderland" (1865) by Lewis Carroll and the anonymous "La Chanson
de Roland" (around 1100). The former, one of the most influential pieces of
nonsense literature ever written, describes the adventures of Alice in a
fantasy world with logic plays interspersed along the narrative. The latter, a
song of heroic deeds, depicts the Battle of Roncevaux in 778 A.D. during
Charlemagne's campaign on the Iberian Peninsula. We apply methods recently
developed by Taylor and coworkers \cite{Taylor+2015} to find time-averaged
eigenvector centralities, Freeman indices and vitalities of characters. We show
that temporal networks are more appropriate than static ones for studying
stories, as they capture features that the time-independent approaches fail to
yield.Comment: 17 pages, 11 figure
Sample-Efficient Multi-Objective Learning via Generalized Policy Improvement Prioritization
Multi-objective reinforcement learning (MORL) algorithms tackle sequential
decision problems where agents may have different preferences over (possibly
conflicting) reward functions. Such algorithms often learn a set of policies
(each optimized for a particular agent preference) that can later be used to
solve problems with novel preferences. We introduce a novel algorithm that uses
Generalized Policy Improvement (GPI) to define principled, formally-derived
prioritization schemes that improve sample-efficient learning. They implement
active-learning strategies by which the agent can (i) identify the most
promising preferences/objectives to train on at each moment, to more rapidly
solve a given MORL problem; and (ii) identify which previous experiences are
most relevant when learning a policy for a particular agent preference, via a
novel Dyna-style MORL method. We prove our algorithm is guaranteed to always
converge to an optimal solution in a finite number of steps, or an
-optimal solution (for a bounded ) if the agent is limited
and can only identify possibly sub-optimal policies. We also prove that our
method monotonically improves the quality of its partial solutions while
learning. Finally, we introduce a bound that characterizes the maximum utility
loss (with respect to the optimal solution) incurred by the partial solutions
computed by our method throughout learning. We empirically show that our method
outperforms state-of-the-art MORL algorithms in challenging multi-objective
tasks, both with discrete and continuous state and action spaces.Comment: Accepted to AAMAS 202
"A Nova Eletricidade: Aplica\c{c}\~oes, Riscos e Tend\^encias da IA Moderna -- "The New Electricity": Applications, Risks, and Trends in Current AI
The thought-provoking analogy between AI and electricity, made by computer
scientist and entrepreneur Andrew Ng, summarizes the deep transformation that
recent advances in Artificial Intelligence (AI) have triggered in the world.
This chapter presents an overview of the ever-evolving landscape of AI, written
in Portuguese. With no intent to exhaust the subject, we explore the AI
applications that are redefining sectors of the economy, impacting society and
humanity. We analyze the risks that may come along with rapid technological
progress and future trends in AI, an area that is on the path to becoming a
general-purpose technology, just like electricity, which revolutionized society
in the 19th and 20th centuries.
A provocativa compara\c{c}\~ao entre IA e eletricidade, feita pelo cientista
da computa\c{c}\~ao e empreendedor Andrew Ng, resume a profunda
transforma\c{c}\~ao que os recentes avan\c{c}os em Intelig\^encia Artificial
(IA) t\^em desencadeado no mundo. Este cap\'itulo apresenta uma vis\~ao geral
pela paisagem em constante evolu\c{c}\~ao da IA. Sem pretens\~oes de exaurir o
assunto, exploramos as aplica\c{c}\~oes que est\~ao redefinindo setores da
economia, impactando a sociedade e a humanidade. Analisamos os riscos que
acompanham o r\'apido progresso tecnol\'ogico e as tend\^encias futuras da IA,
\'area que trilha o caminho para se tornar uma tecnologia de prop\'osito geral,
assim como a eletricidade, que revolucionou a sociedade dos s\'eculos XIX e XX.Comment: In Portugues
Minimal information for studies of extracellular vesicles (MISEV2023): From basic to advanced approaches
Extracellular vesicles (EVs), through their complex cargo, can reflect the state of their cell of origin and change the functions and phenotypes of other cells. These features indicate strong biomarker and therapeutic potential and have generated broad interest, as evidenced by the steady year-on-year increase in the numbers of scientific publications about EVs. Important advances have been made in EV metrology and in understanding and applying EV biology. However, hurdles remain to realising the potential of EVs in domains ranging from basic biology to clinical applications due to challenges in EV nomenclature, separation from non-vesicular extracellular particles, characterisation and functional studies. To address the challenges and opportunities in this rapidly evolving field, the International Society for Extracellular Vesicles (ISEV) updates its 'Minimal Information for Studies of Extracellular Vesicles', which was first published in 2014 and then in 2018 as MISEV2014 and MISEV2018, respectively. The goal of the current document, MISEV2023, is to provide researchers with an updated snapshot of available approaches and their advantages and limitations for production, separation and characterisation of EVs from multiple sources, including cell culture, body fluids and solid tissues. In addition to presenting the latest state of the art in basic principles of EV research, this document also covers advanced techniques and approaches that are currently expanding the boundaries of the field. MISEV2023 also includes new sections on EV release and uptake and a brief discussion of in vivo approaches to study EVs. Compiling feedback from ISEV expert task forces and more than 1000 researchers, this document conveys the current state of EV research to facilitate robust scientific discoveries and move the field forward even more rapidly
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