Ontology-driven dynamic discovery and distributed coordination of a robot swarm

Swarm robotic systems rely heavily on dynamic interactions to provide interoperability between the different autonomous robots. In current systems, interactions between robots are programmed into the applications controlling them. Incorporating service discovery into these applications allows the robots to dynamically discover other devices. However, since most of these mechanisms use syntax-based matching, the robots cannot reason about the offered functionality. Moreover, as contextual information is often not included in the matching process, it is impossible for robots to select the most suitable device under the current context. This paper aims to tackle these issues by proposing a framework for semantic service discovery in a dynamically changing environment. A semantic layer was added to an existing discovery protocol, offering a semantic interface. Using this framework, services can be searched based on what they offer, with services best suiting the current context yielding the highest matching scores

Efficient Retrieval of Web Services Using Prioritization and Clustering

WEB services are software entities that have a well defined interface and perform a specific task. Typical examples include services returning information to the user, such as news or weather forecast services. A web service is formally described in a standardized language (WSDL). The service description may include the parameters associated with web services like input , output and quality of service. As web services and service providers proliferate, there will be a large number of candidate, and likely competing, services for fulfilling a desired task. Hence, effective service discovery mechanisms are required for identifying and retrieving the most appropriate services. The main contributions of our paper are summarized as follows; we propose and implement a method for determining dominance relationships among service advertisements that simultaneously takes into consideration multiple PDM criteria. We introduce a method for prioritization and clustering web services based on similarity measures using efficient algorithms Keywords : Web Service , PDM , dominance score ,TKDD, clustering

Ontology based Comprehensive Architecture for Service Discovery in Emergency Cloud

Abstract — Disasters such as Tsunami, Landslides, and cyclones occur frequently and a strong emergency management system is required to manage such situations. These kinds of crisis circumstances are expected to increase in future. The role of Information and communication technology can largely aid in handling calamities and provide first aid support. The characteristics of Cloud computing such as sharing on demand, connecting communities and offering everything as a service clearly indicate that it can contribute to crisis without affecting business continuity. Hence efforts have taken to articulate web services and the cloud infrastructure as ontology, in the perspective of emergency management which can improve the understanding of this proposed agent based comprehensive architecture

Melding the Data-Decisions Pipeline: Decision-Focused Learning for Combinatorial Optimization

Creating impact in real-world settings requires artificial intelligence techniques to span the full pipeline from data, to predictive models, to decisions. These components are typically approached separately: a machine learning model is first trained via a measure of predictive accuracy, and then its predictions are used as input into an optimization algorithm which produces a decision. However, the loss function used to train the model may easily be misaligned with the end goal, which is to make the best decisions possible. Hand-tuning the loss function to align with optimization is a difficult and error-prone process (which is often skipped entirely). We focus on combinatorial optimization problems and introduce a general framework for decision-focused learning, where the machine learning model is directly trained in conjunction with the optimization algorithm to produce high-quality decisions. Technically, our contribution is a means of integrating common classes of discrete optimization problems into deep learning or other predictive models, which are typically trained via gradient descent. The main idea is to use a continuous relaxation of the discrete problem to propagate gradients through the optimization procedure. We instantiate this framework for two broad classes of combinatorial problems: linear programs and submodular maximization. Experimental results across a variety of domains show that decision-focused learning often leads to improved optimization performance compared to traditional methods. We find that standard measures of accuracy are not a reliable proxy for a predictive model's utility in optimization, and our method's ability to specify the true goal as the model's training objective yields substantial dividends across a range of decision problems.Comment: Full version of paper accepted at AAAI 201

Enhanced matching engine for improving the performance of semantic web service discovery

Web services are the means to realize the Service Oriented Architecture (SOA) paradigm. One of the key tasks of the Web services is discovery also known as matchmaking. This is the act of locating suitable Web services to fulfill a specific goal and adding semantic descriptions to the Web services is the key to enabling an automated, intelligent discovery process. Current Semantic Web service discovery approaches are primarily classified into logic-based, non-logic-based and hybrid categories. An important challenge yet to be addressed by the current approaches is the use of the available constructs in Web service descriptions to achieve a better performance in matchmaking. Performance is defined in terms of precision and recall as well-known metrics in the information retrieval field. Moreover, when matchmaking a large number of Web services, maintaining a reasonable execution time becomes a crucial challenge. In this research, to address these challenges, a matching engine is proposed. The engine comprises a new logic-based and nonlogic- based matchmaker to improve the performance of Semantic Web service discovery. The proposed logic-based and non-logic-based matchmakers are also combined as a hybrid matchmaker for further improvement of performance. In addition, a pre-matching filter is used in the matching engine to enhance the execution time of matchmaking. The components of the matching engine were developed as prototypes and evaluated by benchmarking the results against data from the standard repository of Web services. The comparative evaluations in terms of performance and execution time highlighted the superiority of the proposed matching engine over the existing and prominent matchmakers. The proposed matching engine has been proven to enhance both the performance and execution time of the Semantic Web service discovery