726 research outputs found
AdaGraph: Unifying Predictive and Continuous Domain Adaptation through Graphs
The ability to categorize is a cornerstone of visual intelligence, and a key
functionality for artificial, autonomous visual machines. This problem will
never be solved without algorithms able to adapt and generalize across visual
domains. Within the context of domain adaptation and generalization, this paper
focuses on the predictive domain adaptation scenario, namely the case where no
target data are available and the system has to learn to generalize from
annotated source images plus unlabeled samples with associated metadata from
auxiliary domains. Our contributionis the first deep architecture that tackles
predictive domainadaptation, able to leverage over the information broughtby
the auxiliary domains through a graph. Moreover, we present a simple yet
effective strategy that allows us to take advantage of the incoming target data
at test time, in a continuous domain adaptation scenario. Experiments on three
benchmark databases support the value of our approach.Comment: CVPR 2019 (oral
Técnicas de Inteligência Artificial Aplicadas ao Controlo Preditivo de Baterias Estacionárias
A tendência para a descida das chamadas tarifas feed-in que se espera que ocorra nos próximos anos vem ao encontro da necessidade de criar uma rede elétrica mais sustentável, mais autónoma e com maior capacidade de integração de energia vinda de fontes renováveis. Tornar-se-á assim de enorme relevância para os chamados prosumers, consumidores que possuem pequenas unidades de produção distribuída, nomeadamente ao nível doméstico e da pequena indústria, praticarem o chamado auto-consumo. Com os crescentes avanços na tecnologia de baterias estacionários que se refletem acima de tudo na sua viabilidade económica, as baterias estacionárias apresentam-se como uma das melhores soluções, a par dos veículos elétricos, para maximizar os níveis de auto-consumo dos prosumers.Os controladores que são hoje utilizados na gestão das ações de carga e descarga destas baterias têm, contudo, uma atuação reativa e imediata. Tornar-se-ia interessante para um prosumer que estes controladores tivessem uma ação que por um lado fosse preditiva, isto é, capaz de perceber de que forma irão evoluir os consumos e a produção para maximizar os níveis de auto-consumo. Se, por outro lado, considerarmos que o prosumer se encontra contratualizado num regime de mercado, o controlador deverá ter também uma atuação oportunista, jogando com os preços do mercado para que a requisição de energia à rede fosse feita, sempre que possível, em horas onde o preço fosse mais barato.Este problema enquadra-se matematicamente nas definições multi-objetivo e multi-temporal. Associando-lhe o elevado número de variáveis de estado que, no caso de serem previsões, virão afetas de erros torna-o de tal ordem complexo que apenas pode ser endereçado por agentes de inteligência artificial.No presente trabalho é avaliada a capacidade de técnicas de inteligência artificial no controlo preditivo de baterias estacionárias acopladas a unidades de produção fotovoltaica. Nomeadamente é avaliado o método de Proximal Policy Gradient disponibilizado pela OpenAI, inserido na categoria das metodologias de Deep Reinforcement Learning, que combinam redes neuronais com o treino de agentes artificiais através de Reinfocement Learning. É efetuada a sua comparação com algoritmos genéticos de modo a inferir a viabilidade desta metodologia na resolução do problema em questão.The expected trend of decreasing feed-in tariffs in the upcoming years meets the current necessity to secure a more sustainable and autonomous electric power grid, capable of integrating more renewable energy resources. This trend turns self-consumption particularly relevant for prosumers (consumers that own small distributed generation units), namely at the household and small industry levels. With the growing advances in stationary storage technologies, reflected utmost at their economic viability, stationary batteries along with electric vehicles are viewed as one of the best solutions to maximize such self-consumption levels of prosumers.Today's storage controllers, used on the management of charging and discharging these batteries present a reactive and immediate response. It can although be more interesting, for a prosumer, that such controllers could present a more predictive action, i.e., capable of understanding how consumption and production profiles will evolve, in order to maximize the self-consumption. If we also consider the prosumer to be involved in a market dynamic pricing scheme, the controller should also behave opportunistically, taking into account the market prices so that the energy requirements made to grid would be deviated to time windows were prices were cheaper.This problem can be mathematically framed on the definitions of multi-objective and multi-temporal. Associating the elevated number of state variables and the error possibilities inherent to the data's forecasting nature makes this problem extremely complex, narrowing its resolution to techniques based on artificial intelligence.In the present work, the capability of artificial intelligence techniques in predictively controlling stationary storage when coupled with photovoltaic generation units, is evaluated. Namely it is used the Proximal Policy Gradient method, made available by OpenAI and inserted in the category of Deep Reinforcement Learning which combine neural networks with the training of artificial intelligence agents through Reinforcement Learning. The comparison with genetic algorithms is made in order to infer the viability of this methodology in the resolutions of the problem at hand
Bio-inspired optimization algorithms for multi-objective problems
Orientador : Aurora Trinidad Ramirez PozoCoorientador : Roberto Santana HermidaTese (doutorado) - Universidade Federal do Paraná, Setor de Ciências Exatas, Programa de Pós-Graduação em Informática. Defesa: Curitiba, 06/03/2017Inclui referências : f. 161-72Área de concentração : Computer ScienceResumo: Problemas multi-objetivo (MOPs) são caracterizados por terem duas ou mais funções objetivo a serem otimizadas simultaneamente. Nestes problemas, a meta é encontrar um conjunto de soluções não-dominadas geralmente chamado conjunto ótimo de Pareto cuja imagem no espaço de objetivos é chamada frente de Pareto. MOPs que apresentam mais de três funções objetivo a serem otimizadas são conhecidos como problemas com muitos objetivos (MaOPs) e vários estudos indicam que a capacidade de busca de algoritmos baseados em Pareto é severamente deteriorada nesses problemas. O desenvolvimento de otimizadores bio-inspirados para enfrentar MOPs e MaOPs é uma área que vem ganhando atenção na comunidade, no entanto, existem muitas oportunidades para inovar. O algoritmo de enxames de partículas multi-objetivo (MOPSO) é um dos algoritmos bio-inspirados adequados para ser modificado e melhorado, principalmente devido à sua simplicidade, flexibilidade e bons resultados. Para melhorar a capacidade de busca de MOPSOs, seguimos duas linhas de pesquisa diferentes: A primeira foca em métodos de líder e arquivamento. Trabalhos anteriores apontaram que esses componentes podem influenciar no desempenho do algoritmo, porém a seleção desses componentes pode ser dependente do problema. Uma alternativa para selecioná-los dinamicamente é empregando hiper-heurísticas. Ao combinar hiper-heurísticas e MOPSO, desenvolvemos um novo framework chamado H-MOPSO. A segunda linha de pesquisa também é baseada em trabalhos anteriores do grupo que focam em múltiplos enxames. Isso é feito selecionando como base o framework multi-enxame iterado (I-Multi), cujo procedimento de busca pode ser dividido em busca de diversidade e busca com múltiplos enxames, e a última usa agrupamento para dividir um enxame em vários sub-enxames. Para melhorar o desempenho do I-Multi, exploramos duas possibilidades: a primeira foi investigar o efeito de diferentes características do mecanismo de agrupamento do I-Multi. A segunda foi investigar alternativas para melhorar a convergência de cada sub-enxame, como hibridizá-lo com um algoritmo de estimativa de distribuição (EDA). Este trabalho com EDA aumentou nosso interesse nesta abordagem, portanto seguimos outra linha de pesquisa, investigando alternativas para criar versões multi-objetivo de um dos EDAs mais poderosos da literatura, chamado estratégia de evolução baseada na adaptação da matriz de covariância (CMA-ES). Para validar o nosso trabalho, vários estudos empíricos foram conduzidos para investigar a capacidade de busca das abordagens propostas. Em todos os estudos, nossos algoritmos investigados alcançaram resultados competitivos ou melhores do que algoritmos bem estabelecidos da literatura. Palavras-chave: multi-objetivo, algoritmo de estimativa de distribuição, otimização por enxame de partículas, multiplos enxames, híper-heuristicas.Abstract: Multi-Objective Problems (MOPs) are characterized by having two or more objective functions to be simultaneously optimized. In these problems, the goal is to find a set of non-dominated solutions usually called Pareto optimal set whose image in the objective space is called Pareto front. MOPs presenting more than three objective functions to be optimized are known as Many-Objective Problems (MaOPs) and several studies indicate that the search ability of Pareto-based algorithms is severely deteriorated in such problems. The development of bio-inspired optimizers to tackle MOPs and MaOPs is a field that has been gaining attention in the community, however there are many opportunities to innovate. Multi-objective Particle Swarm Optimization (MOPSO) is one of the bio-inspired algorithms suitable to be modified and improved, mostly due to its simplicity, flexibility and good results. To enhance the search ability of MOPSOs, we followed two different research lines: The first focus on leader and archiving methods. Previous works have pointed that these components can influence the algorithm performance, however the selection of these components can be problem-dependent. An alternative to dynamically select them is by employing hyper-heuristics. By combining hyper-heuristics and MOPSO, we developed a new framework called H-MOPSO. The second research line, is also based on previous works of the group that focus on multi-swarm. This is done by selecting as base framework the iterated multi swarm (I-Multi) algorithm, whose search procedure can be divided into diversity and multi-swarm searches, and the latter employs clustering to split a swarm into several sub-swarms. In order to improve the performance of I-Multi, we explored two possibilities: the first was to further investigate the effect of different characteristics of the clustering mechanism of I-Multi. The second was to investigate alternatives to improve the convergence of each sub-swarm, like hybridizing it to an Estimation of Distribution Algorithm (EDA). This work on EDA increased our interest in this approach, hence we followed another research line by investigating alternatives to create multi-objective versions of one of the most powerful EDAs from the literature, the Covariance Matrix Adaptation Evolution Strategy (CMA-ES). In order to validate our work, several empirical studies were conducted to investigate the search ability of the approaches proposed. In all studies, our investigated algorithms have reached competitive or better results than well established algorithms from the literature. Keywords: multi-objective, estimation of distribution algorithms, particle swarm optimization, multi-swarm, hyper-heuristics
Learning to represent surroundings, anticipate motion and take informed actions in unstructured environments
Contemporary robots have become exceptionally skilled at achieving specific tasks in structured environments. However, they often fail when faced with the limitless permutations of real-world unstructured environments. This motivates robotics methods which learn from experience, rather than follow a pre-defined set of rules. In this thesis, we present a range of learning-based methods aimed at enabling robots, operating in dynamic and unstructured environments, to better understand their surroundings, anticipate the actions of others, and take informed actions accordingly
Scalable Transfer Evolutionary Optimization: Coping with Big Task Instances
In today's digital world, we are confronted with an explosion of data and
models produced and manipulated by numerous large-scale IoT/cloud-based
applications. Under such settings, existing transfer evolutionary optimization
frameworks grapple with satisfying two important quality attributes, namely
scalability against a growing number of source tasks and online learning
agility against sparsity of relevant sources to the target task of interest.
Satisfying these attributes shall facilitate practical deployment of transfer
optimization to big source instances as well as simultaneously curbing the
threat of negative transfer. While applications of existing algorithms are
limited to tens of source tasks, in this paper, we take a quantum leap forward
in enabling two orders of magnitude scale-up in the number of tasks; i.e., we
efficiently handle scenarios with up to thousands of source problem instances.
We devise a novel transfer evolutionary optimization framework comprising two
co-evolving species for joint evolutions in the space of source knowledge and
in the search space of solutions to the target problem. In particular,
co-evolution enables the learned knowledge to be orchestrated on the fly,
expediting convergence in the target optimization task. We have conducted an
extensive series of experiments across a set of practically motivated discrete
and continuous optimization examples comprising a large number of source
problem instances, of which only a small fraction show source-target
relatedness. The experimental results strongly validate the efficacy of our
proposed framework with two salient features of scalability and online learning
agility.Comment: 12 pages, 5 figures, 2 tables, 2 algorithm pseudocode
Maximum Entropy RL (Provably) Solves Some Robust RL Problems
Many potential applications of reinforcement learning (RL) require guarantees
that the agent will perform well in the face of disturbances to the dynamics or
reward function. In this paper, we prove theoretically that standard maximum
entropy RL is robust to some disturbances in the dynamics and the reward
function. While this capability of MaxEnt RL has been observed empirically in
prior work, to the best of our knowledge our work provides the first rigorous
proof and theoretical characterization of the MaxEnt RL robust set. While a
number of prior robust RL algorithms have been designed to handle similar
disturbances to the reward function or dynamics, these methods typically
require adding additional moving parts and hyperparameters on top of a base RL
algorithm. In contrast, our theoretical results suggest that MaxEnt RL by
itself is robust to certain disturbances, without requiring any additional
modifications. While this does not imply that MaxEnt RL is the best available
robust RL method, MaxEnt RL does possess a striking simplicity and appealing
formal guarantees.Comment: Blog post and videos:
https://bair.berkeley.edu/blog/2021/03/10/maxent-robust-rl/. arXiv admin
note: text overlap with arXiv:1910.0191
Search-Based Software Maintenance and Testing
2012 - 2013In software engineering there are many expensive tasks that are performed during development
and maintenance activities. Therefore, there has been a lot of e ort to try to automate these
tasks in order to signi cantly reduce the development and maintenance cost of software, since
the automation would require less human resources. One of the most used way to make such
an automation is the Search-Based Software Engineering (SBSE), which reformulates traditional
software engineering tasks as search problems. In SBSE the set of all candidate solutions to the
problem de nes the search space while a tness function di erentiates between candidate solutions
providing a guidance to the optimization process. After the reformulation of software engineering
tasks as optimization problems, search algorithms are used to solve them. Several search algorithms
have been used in literature, such as genetic algorithms, genetic programming, simulated annealing,
hill climbing (gradient descent), greedy algorithms, particle swarm and ant colony.
This thesis investigates and proposes the usage of search based approaches to reduce the e ort
of software maintenance and software testing with particular attention to four main activities: (i)
program comprehension; (ii) defect prediction; (iii) test data generation and (iv) test suite optimiza-
tion for regression testing. For program comprehension and defect prediction, this thesis provided
their rst formulations as optimization problems and then proposed the usage of genetic algorithms
to solve them. More precisely, this thesis investigates the peculiarity of source code against textual
documents written in natural language and proposes the usage of Genetic Algorithms (GAs) in
order to calibrate and assemble IR-techniques for di erent software engineering tasks. This thesis
also investigates and proposes the usage of Multi-Objective Genetic Algorithms (MOGAs) in or-
der to build multi-objective defect prediction models that allows to identify defect-prone software
components by taking into account multiple and practical software engineering criteria.
Test data generation and test suite optimization have been extensively investigated as search-
based problems in literature . However, despite the huge body of works on search algorithms
applied to software testing, both (i) automatic test data generation and (ii) test suite optimization
present several limitations and not always produce satisfying results. The success of evolutionary
software testing techniques in general, and GAs in particular, depends on several factors. One of
these factors is the level of diversity among the individuals in the population, which directly a ects
the exploration ability of the search. For example, evolutionary test case generation techniques that
employ GAs could be severely a ected by genetic drift, i.e., a loss of diversity between solutions,
which lead to a premature convergence of GAs towards some local optima. For these reasons,
this thesis investigate the role played by diversity preserving mechanisms on the performance of
GAs and proposed a novel diversity mechanism based on Singular Value Decomposition and linear
algebra. Then, this mechanism has been integrated within the standard GAs and evaluated for
evolutionary test data generation. It has been also integrated within MOGAs and empirically
evaluated for regression testing. [edited by author]XII n.s
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