Contributions in Merlin

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Semantic caching is a technique used for optimizing the evaluation of database queries by caching results of old queries and using them when answering new queries. CoopSC is a cooperative database caching architecture, which extends the classic semantic caching approach by allowing clients to share their local caches in a cooperative matter. Cache entries of all clients are indexed in a distributed data structure constructed on top of a Peer-to-Peer (P2P) overlay network. This distributed index is used for determining those cache entries that can be used for answering a specific query. Thus, this approach decreases the response time of database queries and the amount of data sent by database server, because the server only answers those parts of queries that are not available in the cooperative cache.

This work examines correlations between functional morphology and behaviour in the instance of the burrowing locomotion of bivalves. A comparatively simple and assessable behaviour and a rich fossil record documenting the evolutionary adaptations in morphology make these animals adequate for investigation. In this paper a robotic setup to simulate the burrowing behaviour of bivalves is presented. Models of both natural bivalve shell shapes and artificially designed shapes are pulled into sediment in the rocking modality these animals typically use. Different shapes, motion patterns and a water expulsion mechanism are evaluated and compared in terms of burrowing performance. The results presented here and further experiments using the (improved) platform may shed light on how bivalves burrow, how features of functional morphology evolved and how efficient automatic burrowing devices may be constructed. Keywords: biorobotics, biomimetics, underwater robots, functional morphology, burrowing locomotion, shell morphology, bivalves, artificial evolution.

We present a bio-inspired control method for moving-target seeking with a mobile robot, which resembles a predator-prey scenario. The motor repertoire of a simulated Khepera robot was restricted to a discrete number of "gaits". After an exploration phase, the robot automatically synthesizes a model of its motor repertoire, acquiring a forward model. Two additional components were introduced for the task of catching a prey robot. First, an inverse model to the forward model, which is used to determine the action (gait) needed to reach a desired location. Second, while hunting the prey, a model of the prey's behavior is learned online by the hunter robot. All the models are learned ab initio, without assumptions, work in egocentric coordinates, and are probabilistic in nature. Our architecture can be applied to robots with any physical constraints (or embodiment), such as legged robots.

We consider choice over a set of monetary acts and study a general class of preferences. These preferences favor diversification, except perhaps on a subset of sufficiently disliked acts, over which concentration is instead preferred. This structure encompasses a number of known models, such as expected utility theory, maxmin utility theory, and convex risk measures. We show that such preferences share a dual representation in terms of a family of measures of risk and a target function. Specifically, the choice function is equivalent to selection of a maximum index level such that the risk of beating the target function at that level is acceptable. This dual representation may help to uncover new models of choice. One that we explore in detail is the special case of a bounded target function. This case corresponds to a type of satisficing and has descriptive relevance. Moreover, the model results in optimization problems that may be efficiently solved in large-scale.

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