Randomized, double-blind, placebo-controled clinical trial of sublingual immunotherapy in natural rubber latex allergic patients

Trial registration number: CTRN12611000543987

Simple Coalitional Strategy Profiles in Repeated Games

This paper is a version of the discussion paper titled "Simple coalitional strategy profiles"

Gehiago Pierre Urteren gramatikaren iturriez: Lilyren gramatika

[EN] Pierre Urte wrote Grammaire cantabrique circa 1714, when he was exiled in England. In this article we want to prove that the main source for Urte’s work was the socalled “Lily’s grammar”, which was the oficial grammar to learn Latin language in England from the 16th to the 19th century. The indentification of that source allows us to support the claim that Urte’s grammar must be included in the tradition of language teaching, as was already pointed out by Oyharçabal (1989). In this article, we first offer a brief history of Lily’s grammar. Then, we provide some clues in order to identify the exact edition used by Urte. Finally, in the main section of the article, we confront the two grammatical works; our aim is to ensure Urte’s debt to Lily’s grammar, and to show in detail the principal parts which Urte took from his source (mainly grammatical clasifications and examples).

Effect of the A cation size disorder and synthesis conditions on the properties of an iron perovskite series

Póster presentado en The Energy and Materials Research Conference - EMR2015 celebrado en Madrid (España) entre el 25-27 de febrero de 2015

Learning Multirobot Hose Transportation and Deployment by Distributed Round-Robin Q-Learning

Multi-Agent Reinforcement Learning (MARL) algorithms face two main difficulties: the curse of dimensionality, and environment non-stationarity due to the independent learning processes carried out by the agents concurrently. In this paper we formalize and prove the convergence of a Distributed Round Robin Q-learning (D-RR-QL) algorithm for cooperative systems. The computational complexity of this algorithm increases linearly with the number of agents. Moreover, it eliminates environment non sta tionarity by carrying a round-robin scheduling of the action selection and execution. That this learning scheme allows the implementation of Modular State-Action Vetoes (MSAV) in cooperative multi-agent systems, which speeds up learning convergence in over-constrained systems by vetoing state-action pairs which lead to undesired termination states (UTS) in the relevant state-action subspace. Each agent's local state-action value function learning is an independent process, including the MSAV policies. Coordination of locally optimal policies to obtain the global optimal joint policy is achieved by a greedy selection procedure using message passing. We show that D-RR-QL improves over state-of-the-art approaches, such as Distributed Q-Learning, Team Q-Learning and Coordinated Reinforcement Learning in a paradigmatic Linked Multi-Component Robotic System (L-MCRS) control problem: the hose transportation task. L-MCRS are over-constrained systems with many UTS induced by the interaction of the passive linking element and the active mobile robots.