Alleniella aegaea Blockeel & Hugonnot (Neckeraceae), a new moss species from the Aegean islands of Greece

A new moss species, Alleniella aegaea (Neckeraceae) is described and illustrated from the Greek islands of Crete, Samos and Evvia based on morphological and molecular evidence. According to phylogenetic analysis of nuclear ITS and plastid trnS-F sequences it is sister to A. complanata, which it also morphologically resembles, but it differs from that species e.g. in its distinctly flattened habit, lack of caducous branchlets, more rounded leaf apices, and shorter median laminal cells. Alleniella aegaea is dioicous and the sporophytes remain unknow

Hygrohypnum subeugyrium (Renauld & Cardot) Broth. (Hypnales), a neglected British moss, with a note on its occurrence in the Himalayas

Hygrohypnum subeugyrium was first reported for Britain in 1976, but it has been ignored in subsequent British floras and identification guides. Morphologically it is a distinct species. New localities were discovered during a meeting of the British Bryological Society in Scotland in 2017. A subsequent revision of herbarium material has shown that H. subeugyrium is widely distributed in Scotland and occurs southwards to a few localities in England and Wales. Its extra-European distribution is extended to include the Himalayan region (Sikkim and Yunnan). It is described and illustrated, and its diagnostic characters are discussed

Learning a Tsume-Go heuristic with Tilde

In Go, an important factor that hinders search is the large branching factor, even in local problems. Human players are strong at recognizing frequently occurring shapes and vital points. This allows them to select the most promising moves and to prune the search tree. In this paper we argue that many of these shapes can be represented as relational concepts. We present an application of the relational learner TILDE in which we learn a heuristic that gives values to candidate-moves in tsume-go (life and death) problems. Such a heuristic can be used to limit the number of evaluated moves. Even if all moves are evaluated, alpha-beta search can be sped up considerably when the candidate-moves are approximately ordered from good to bad.We validate our approach with experiments and analysis.status: publishe

Learning a Go heuristic with TILDE

status: publishe

Model-assisted approaches for relational reinforcement learning: Some challenges for the SRL community

status: publishe

Model-assisted approaches for relational reinforcement learning

In recent years, there has been a growing interest in using rich representations such as relational languages for reinforcement learning. However, while expressive languages have many advantages in terms of generalization and reasoning, extending existing approaches to such a relational setting is a non-trivial problem. For a relational reinforcement learning (RRL)-agent, learning a model of the world can be very helpful. However, in many situations learning a perfect model is not possible. Therefore, only probabilistic methods capable of taking uncertainty into account can be used to exploit the collected knowledge. We present a first step towards the online learning and exploitation of relational models. We propose a representation for the transition and reward function that can be learned online and present a method that exploits these models by augmenting Relational Reinforcement Learning algorithms with planning techniques.status: publishe