998 research outputs found
Modeling Web Service Selection for Composition as a Distributed Constraint Optimization Problem (DCOP)
During development of a Service-oriented Application, some software pieces could be fulfilled by the connection to Web Services. A list of candidate Web Services could be obtained by making use of any service discovery registry, which are then selected and integrated into the application. However, when it comes to a distributed system, multiple functional and non-functional constraints arise from the interaction between several service requesters and providers, particularly when composing different services. To overcome with such constraints, in this work we propose to model service selection and composition scenarios as Distributed Constraints Optimization Problems (DCOP).We propose different modeling approaches and develop representative examples to be solved through different DCOP algorithms. Also, we analyze the impact of possible extensions to the model in the computability of the problem.Sociedad Argentina de Informática e Investigación Operativa (SADIO
Decentralization of Multiagent Policies by Learning What to Communicate
Effective communication is required for teams of robots to solve
sophisticated collaborative tasks. In practice it is typical for both the
encoding and semantics of communication to be manually defined by an expert;
this is true regardless of whether the behaviors themselves are bespoke,
optimization based, or learned. We present an agent architecture and training
methodology using neural networks to learn task-oriented communication
semantics based on the example of a communication-unaware expert policy. A
perimeter defense game illustrates the system's ability to handle dynamically
changing numbers of agents and its graceful degradation in performance as
communication constraints are tightened or the expert's observability
assumptions are broken.Comment: 7 page
Efficient Digital Management in Smart Cities
The concept of smart cities puts the citizen at the center of all processes. It is the citizen who decides what kind of city they live in. Their opinions and attitudes towards technologies and the solutions they would like to see in their cities must be listened to. With Deep Intelligence, cities will be able to create more optimal citizen-centered services as, as the tool can collect data from multiple sources, such as databases and social networks, from which valuable information on citizens’ opinions and attitudes regarding technology, smart city services and urban problems, may be extracted
Proceedings of the Workshop on Models and Model-driven Methods for Enterprise Computing (3M4EC 2008)
Abmash: Mashing Up Legacy Web Applications by Automated Imitation of Human Actions
Many business web-based applications do not offer applications programming
interfaces (APIs) to enable other applications to access their data and
functions in a programmatic manner. This makes their composition difficult (for
instance to synchronize data between two applications). To address this
challenge, this paper presents Abmash, an approach to facilitate the
integration of such legacy web applications by automatically imitating human
interactions with them. By automatically interacting with the graphical user
interface (GUI) of web applications, the system supports all forms of
integrations including bi-directional interactions and is able to interact with
AJAX-based applications. Furthermore, the integration programs are easy to
write since they deal with end-user, visual user-interface elements. The
integration code is simple enough to be called a "mashup".Comment: Software: Practice and Experience (2013)
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