417 research outputs found
Evolution of a supply chain management game for the trading agent competition
TAC SCM is a supply chain management game for the Trading Agent Competition (TAC). The purpose of TAC is to spur high quality research into realistic trading agent problems. We discuss TAC and TAC SCM: game and competition design, scientific impact, and lessons learnt
Petri Net Plans A framework for collaboration and coordination in multi-robot systems
Programming the behavior of multi-robot systems is a challenging task which has a key role in developing effective systems in many application domains. In this paper, we present Petri Net Plans (PNPs), a language based on Petri Nets (PNs), which allows for intuitive and effective robot and multi-robot behavior design. PNPs are very expressive and support a rich set of features that are critical to develop robotic applications, including sensing, interrupts and concurrency. As a central feature, PNPs allow for a formal analysis of plans based on standard PN tools. Moreover, PNPs are suitable for modeling multi-robot systems and the developed behaviors can be executed in a distributed setting, while preserving the properties of the modeled system. PNPs have been deployed in several robotic platforms in different application domains. In this paper, we report three case studies, which address complex single robot plans, coordination and collaboration
Information theoretic stochastic search
The MAP-i Doctoral Programme in Informatics, of the Universities of Minho, Aveiro and PortoOptimization is the research field that studies the design of algorithms for finding the
best solutions to problems we may throw at them. While the whole domain is practically
important, the present thesis will focus on the subfield of continuous black-box
optimization, presenting a collection of novel, state-of-the-art algorithms for solving
problems in that class. In this thesis, we introduce two novel general-purpose
stochastic search algorithms for black box optimisation. Stochastic search algorithms
aim at repeating the type of mutations that led to fittest search points in a population.
We can model those mutations by a stochastic distribution. Typically the stochastic
distribution is modelled as a multivariate Gaussian distribution. The key idea is to
iteratively change the parameters of the distribution towards higher expected fitness.
However we leverage information theoretic trust regions and limit the change of the
new distribution. We show how plain maximisation of the fitness expectation without
bounding the change of the distribution is destined to fail because of overfitting
and the results in premature convergence. Being derived from first principles, the
proposed methods can be elegantly extended to contextual learning setting which allows
for learning context dependent stochastic distributions that generates optimal
individuals for a given context, i.e, instead of learning one task at a time, we can
learn multiple related tasks at once. However, the search distribution typically uses
a parametric model using some hand-defined context features. Finding good context
features is a challenging task, and hence, non-parametric methods are often preferred
over their parametric counter-parts. Therefore, we further propose a non-parametric
contextual stochastic search algorithm that can learn a non-parametric search distribution
for multiple tasks simultaneously.Otimização é área de investigação que estuda o projeto de algoritmos para encontrar
as melhores soluções, tendo em conta um conjunto de critérios, para problemas
complexos. Embora todo o domínio de otimização tenha grande importância,
este trabalho está focado no subcampo da otimização contínua de caixa preta,
apresentando uma coleção de novos algoritmos novos de última geração para resolver
problemas nessa classe. Nesta tese, apresentamos dois novos algoritmos de
pesquisa estocástica de propósito geral para otimização de caixa preta. Os algoritmos
de pesquisa estocástica visam repetir o tipo de mutações que levaram aos
melhores pontos de pesquisa numa população. Podemos modelar essas mutações
por meio de uma distribuição estocástica e, tipicamente, a distribuição estocástica
é modelada como uma distribuição Gaussiana multivariada. A ideia chave é mudar
iterativamente os parâmetros da distribuição incrementando a avaliação. No entanto,
alavancamos as regiões de confiança teóricas de informação e limitamos a mudança
de distribuição. Deste modo, demonstra-se como a maximização simples da expectativa
de “fitness”, sem limites da mudança da distribuição, está destinada a falhar
devido ao “overfitness” e à convergência prematura resultantes. Sendo derivado dos
primeiros princípios, as abordagens propostas podem ser ampliadas, de forma elegante,
para a configuração de aprendizagem contextual que permite a aprendizagem
de distribuições estocásticas dependentes do contexto que geram os indivíduos ideais
para um determinado contexto. No entanto, a distribuição de pesquisa geralmente usa
um modelo paramétrico linear em algumas das características contextuais definidas
manualmente. Encontrar uma contextos bem definidos é uma tarefa desafiadora e,
portanto, os métodos não paramétricos são frequentemente preferidos em relação às
seus semelhantes paramétricos. Portanto, propomos um algoritmo não paramétrico
de pesquisa estocástica contextual que possa aprender uma distribuição de pesquisa
não-paramétrica para várias tarefas simultaneamente.FCT - Fundação para a Ciência e a Tecnologia. As well as fundings by European Union’s
FP7 under EuRoC grant agreement CP-IP 608849 and by LIACC (UID/CEC/00027/2015)
and IEETA (UID/CEC/00127/2015)
Experiments in artificial theory of mind: From safety to story-telling
© 2018 Winfield. Theory of mind is the term given by philosophers and psychologists for the ability to form a predictive model of self and others. In this paper we focus on synthetic models of theory of mind. We contend firstly that such models-especially when tested experimentally-can provide useful insights into cognition, and secondly that artificial theory of mind can provide intelligent robots with powerful new capabilities, in particular social intelligence for human-robot interaction. This paper advances the hypothesis that simulation-based internal models offer a powerful and realisable, theory-driven basis for artificial theory of mind. Proposed as a computational model of the simulation theory of mind, our simulation-based internal model equips a robot with an internal model of itself and its environment, including other dynamic actors, which can test (i.e., simulate) the robot's next possible actions and hence anticipate the likely consequences of those actions both for itself and others. Although it falls far short of a full artificial theory of mind, our model does allow us to test several interesting scenarios: in some of these a robot equipped with the internal model interacts with other robots without an internal model, but acting as proxy humans; in others two robots each with a simulation-based internal model interact with each other. We outline a series of experiments which each demonstrate some aspect of artificial theory of mind
Bayesian Optimization for Learning Gaits under Uncertainty
© 2015, Springer International Publishing Switzerland.Designing gaits and corresponding control policies is a key challenge in robot locomotion. Even with a viable controller parametrization, finding near-optimal parameters can be daunting. Typically, this kind of parameter optimization requires specific expert knowledge and extensive robot experiments. Automatic black-box gait optimization methods greatly reduce the need for human expertise and time-consuming design processes. Many different approaches for automatic gait optimization have been suggested to date. However, no extensive comparison among them has yet been performed. In this article, we thoroughly discuss multiple automatic optimization methods in the context of gait optimization. We extensively evaluate Bayesian optimization, a model-based approach to black-box optimization under uncertainty, on both simulated problems and real robots. This evaluation demonstrates that Bayesian optimization is particularly suited for robotic applications, where it is crucial to find a good set of gait parameters in a small number of experiments
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