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).

El Musteriense de la cueva de Harregi en Aussurucq, Soule (Excavaciones: Pierre Roucher, 1954-1960)

[ES] La cueva de Harregi (Aussurucq, Pays de Soule), excavada por P. Boucher entre 1954 y 1960, contiene testimonios prehistóricos del Paleolítico medio, superior y Postpaleolítico. En este artículo se analizan las evidencias de fauna e industria lítica asimiladas al Paleolítico medio. Importantes alteraciones estratigráficas postdeposicionales modificaron la original posición topográfica de la mayor parte de los depósitos musterienses. La composición global del lote industrial y de la serie faunística recuerdan intensamente a los controlados en los niveles musterienses Cj y Cjr de la muy cercana cueva de Gatzarria. Ello hace, bien plantear la posibilidad de una antigua organización estratigráfica y arqueológica de los depósitos musterienses como la de Gatzarria, lamentablemente perturbada por serios procesos hídricos erosivos, bien contemplar el hecho de una secuencia diferente, propia y, en cierta forma, contradictoria con la dinámica reconocida en Gatzarria.

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.