ALBERT

All Library Books, journals and Electronic Records Telegrafenberg

X
feed icon rss

Your email was sent successfully. Check your inbox.

An error occurred while sending the email. Please try again.

Proceed reservation?

Export
Filter
Collection
Publisher
Years
  • 1
    facet.materialart.
    Unknown
    Software Frameworks for Model Composition (2014)
    Hindawi
    Details
    Publication Date: 2014-01-01
    Description: A software framework is an architecture or infrastructure intended to enable the integration and interoperation of software components. Specialized types of software frameworks are those specifically intended to support the composition of models or other components within a simulation system. Such frameworks are intended to simplify the process of assembling a complex model or simulation system from simpler component models as well as to promote the reuse of the component models. Several different types of software frameworks for model composition have been designed and implemented; those types include common library, product line architecture, interoperability protocol, object model, formal, and integrative environment. The various framework types have different components, processes for composing models, and intended applications. In this survey the fundamental terms and concepts of software frameworks for model composition are presented, the different types of such frameworks are explained and compared, and important examples of each type are described.
    Print ISSN: 1687-5591
    Electronic ISSN: 1687-5605
    Topics: Computer Science , Technology
    Published by Hindawi
    Location Call Number Expected Availability
    BibTip Others were also interested in ...
    PAPER CURRENT
  • 2
    facet.materialart.
    Unknown
    Reinforcement Learning in an Environment Synthetically Augmented with Digital Pheromones (2014)
    Hindawi
    Details
    Publication Date: 2014-01-01
    Description: Reinforcement learning requires information about states, actions, and outcomes as the basis for learning. For many applications, it can be difficult to construct a representative model of the environment, either due to lack of required information or because of that the model's state space may become too large to allow a solution in a reasonable amount of time, using the experience of prior actions. An environment consisting solely of the occurrence or nonoccurrence of specific events attributable to a human actor may appear to lack the necessary structure for the positioning of responding agents in time and space using reinforcement learning. Digital pheromones can be used to synthetically augment such an environment with event sequence information to create a more persistent and measurable imprint on the environment that supports reinforcement learning. We implemented this method and combined it with the ability of agents to learn from actions not taken, a concept known as fictive learning. This approach was tested against the historical sequence of Somali maritime pirate attacks from 2005 to mid-2012, enabling a set of autonomous agents representing naval vessels to successfully respond to an average of 333 of the 899 pirate attacks, outperforming the historical record of 139 successes.
    Print ISSN: 1687-7470
    Electronic ISSN: 1687-7489
    Topics: Computer Science
    Published by Hindawi
    Location Call Number Expected Availability
    BibTip Others were also interested in ...
    PAPER CURRENT
Close ⊗
This website uses cookies and the analysis tool Matomo. More information can be found here...