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A linked data compliant framework for dynamic and web-scale consumption of web services
The While Semantic Web Services (SWS) research aims at automating Web service tasks such as discovery, orchestration and execution, its take-up is very limited so far. This is due to several reasons, such as inherent complexity of existing SWS frameworks and the considerable costs involved in creating correct SWS descriptions. In addition, while semantics are in use to enable tasks such as discovery, interaction between service consumers, providers and brokering environments is still not supported by semantic message descriptions. On the other hand, the Linked Data approach has produced a set of established principles for sharing and describing data, such as RDF as representation language and the integral use of dereferencable URIs. In this paper we propose to apply those principles to expose Web services and Web APIs and introduce a framework in which service registries as well as services contribute to the automation of service discovery, and hence, workload is distributed more efficiently. This is achieved by developing a Linked Data compliant Web services framework with that communicate with semi-centralised registries but compute their suitability for a given request themselves. All communications among different framework components are using RDF-based message protocols including service input and output. This framework aims at optimizing load balance and performance by dynamically assembling services at run time in a massively distributed Web environment
Innovative digital learning
The new programming technologies allow for the creation of components which can be automatically or manually assembled to reach a new experience in knowledge understanding and mastering or in getting skills for a specific knowledge area. A Visual C# .NET implementation under development is discussed.learning component, user control, automatic assembly, adaptor
Performative ontologies. Sociomaterial approaches to researching adult education and lifelong learning
Sociomaterial approaches to researching education, such as those generated by actornetwork theory and complexity theory, have been growing in significance in recent years, both theoretically and methodologically. Such approaches are based upon a performative ontology rather than the more characteristic representational epistemology that informs much research. In this article, we outline certain aspects of sociomaterial sensibilities in researching education, and some of the uptakes on issues related to the education of adults. We further suggest some possibilities emerging for adult education and lifelong learning researchers from taking up such theories and methodologies. (DIPF/Orig.
Automatic Mapping of NES Games with Mappy
Game maps are useful for human players, general-game-playing agents, and
data-driven procedural content generation. These maps are generally made by
hand-assembling manually-created screenshots of game levels. Besides being
tedious and error-prone, this approach requires additional effort for each new
game and level to be mapped. The results can still be hard for humans or
computational systems to make use of, privileging visual appearance over
semantic information. We describe a software system, Mappy, that produces a
good approximation of a linked map of rooms given a Nintendo Entertainment
System game program and a sequence of button inputs exploring its world. In
addition to visual maps, Mappy outputs grids of tiles (and how they change over
time), positions of non-tile objects, clusters of similar rooms that might in
fact be the same room, and a set of links between these rooms. We believe this
is a necessary step towards developing larger corpora of high-quality
semantically-annotated maps for PCG via machine learning and other
applications.Comment: 9 pages, 7 figures. Appearing at Procedural Content Generation
Workshop 201
Modelling and analyzing adaptive self-assembling strategies with Maude
Building adaptive systems with predictable emergent behavior is a challenging task and it is becoming a critical need. The research community has accepted the challenge by introducing approaches of various nature: from software architectures, to programming paradigms, to analysis techniques. We recently proposed a conceptual framework for adaptation centered around the role of control data. In this paper we show that it can be naturally realized in a reflective logical language like Maude by using the Reflective Russian Dolls model. Moreover, we exploit this model to specify, validate and analyse a prominent example of adaptive system: robot swarms equipped with self-assembly strategies. The analysis exploits the statistical model checker PVeStA
One-Shot Learning of Manipulation Skills with Online Dynamics Adaptation and Neural Network Priors
One of the key challenges in applying reinforcement learning to complex
robotic control tasks is the need to gather large amounts of experience in
order to find an effective policy for the task at hand. Model-based
reinforcement learning can achieve good sample efficiency, but requires the
ability to learn a model of the dynamics that is good enough to learn an
effective policy. In this work, we develop a model-based reinforcement learning
algorithm that combines prior knowledge from previous tasks with online
adaptation of the dynamics model. These two ingredients enable highly
sample-efficient learning even in regimes where estimating the true dynamics is
very difficult, since the online model adaptation allows the method to locally
compensate for unmodeled variation in the dynamics. We encode the prior
experience into a neural network dynamics model, adapt it online by
progressively refitting a local linear model of the dynamics, and use model
predictive control to plan under these dynamics. Our experimental results show
that this approach can be used to solve a variety of complex robotic
manipulation tasks in just a single attempt, using prior data from other
manipulation behaviors
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