A DHT-Based Multi-Agent System for Semantic Information Sharing. In

Abstract. This paper presents AOIS, a multi-agent system that supports the sharing of information among a dynamic community of users connected through the Internet thanks to the use of a well-known DHT-based peer-to-peer platform: BitTorrent. In respect to Web search engines, this system enhances the search through domain ontologies, avoids the burden of publishing the information on the Web and guaranties a controlled and dynamic access to the information. The use of agent technologies has made the realization of three of the main features of the system straightforward: i) filtering of information coming from different users, on the basis of the previous experience of the local user, ii) pushing of some new information that can be of interest for a user, and iii) delegation of access capabilities, on the basis of a reputation network, built by the agents of the system on the community of its users. The use of BitTorrent will allow us to offer the AOIS systems to the hundreds of millions of users that already share documents though the BitTorrent platform

State of the art of a multi-agent based recommender system for active software engineering ontology

Software engineering ontology was first developed to provide efficient collaboration and coordination among distributed teams working on related software development projects across the sites. It helped to clarify the software engineering concepts and project information as well as enable knowledge sharing. However, a major challenge of the software engineering ontology users is that they need the competence to access and translate what they are looking for into the concepts and relations described in the ontology; otherwise, they may not be able to obtain required information. In this paper, we propose a conceptual framework of a multi-agent based recommender system to provide active support to access and utilize knowledge and project information in the software engineering ontology. Multi-agent system and semantic-based recommendation approach will be integrated to create collaborative working environment to access and manipulate data from the ontology and perform reasoning as well as generate expert recommendation facilities for dispersed software teams across the sites

Ontology driven multi-agent systems : an architecture for sensor web applications.

Thesis (Ph.D.)-University of KwaZulu-Natal, 2009.Advances in sensor technology and space science have resulted in the availability of vast quantities of high quality earth observation data. This data can be used for monitoring the earth and to enhance our understanding of natural processes. Sensor Web researchers are working on constructing a worldwide computing infrastructure that enables dynamic sharing and analysis of complex heterogeneous earth observation data sets. Key challenges that are currently being investigated include data integration; service discovery, reuse and composition; semantic interoperability; and system dynamism. Two emerging technologies that have shown promise in dealing with these challenges are ontologies and software agents. This research investigates how these technologies can be integrated into an Ontology Driven Multi-Agent System (ODMAS) for the Sensor Web. The research proposes an ODMAS framework and an implemented middleware platform, i.e. the Sensor Web Agent Platform (SWAP). SWAP deals with ontology construction, ontology use, and agent based design, implementation and deployment. It provides a semantic infrastructure, an abstract architecture, an internal agent architecture and a Multi-Agent System (MAS) middleware platform. Distinguishing features include: the incorporation of Bayesian Networks to represent and reason about uncertain knowledge; ontologies to describe system entities such as agent services, interaction protocols and agent workflows; and a flexible adapter based MAS platform that facilitates agent development, execution and deployment. SWAP aims to guide and ease the design, development and deployment of dynamic alerting and monitoring applications. The efficacy of SWAP is demonstrated by two satellite image processing applications, viz. wildfire detection and monitoring informal settlement. This approach can provide significant benefits to a wide range of Sensor Web users. These include: developers for deploying agents and agent based applications; end users for accessing, managing and visualising information provided by real time monitoring applications, and scientists who can use the Sensor Web as a scientific computing platform to facilitate knowledge sharing and discovery. An Ontology Driven Multi-Agent Sensor Web has the potential to forever change the way in which geospatial data and knowledge is accessed and used. This research describes this far reaching vision, identifies key challenges and provides a first step towards the vision

Towards engineering ontologies for cognitive profiling of agents on the semantic web

Research shows that most agent-based collaborations suffer from lack of flexibility. This is due to the fact that most agent-based applications assume pre-defined knowledge of agents’ capabilities and/or neglect basic cognitive and interactional requirements in multi-agent collaboration. The highlight of this paper is that it brings cognitive models (inspired from cognitive sciences and HCI) proposing architectural and knowledge-based requirements for agents to structure ontological models for cognitive profiling in order to increase cognitive awareness between themselves, which in turn promotes flexibility, reusability and predictability of agent behavior; thus contributing towards minimizing cognitive overload incurred on humans. The semantic web is used as an action mediating space, where shared knowledge base in the form of ontological models provides affordances for improving cognitive awareness

Is a Semantic Web Agent a Knowledge-Savvy Agent?

The issue of knowledge sharing has permeated the field of distributed AI and in particular, its successor, multiagent systems. Through the years, many research and engineering efforts have tackled the problem of encoding and sharing knowledge without the need for a single, centralized knowledge base. However, the emergence of modern computing paradigms such as distributed, open systems have highlighted the importance of sharing distributed and heterogeneous knowledge at a larger scale—possibly at the scale of the Internet. The very characteristics that define the Semantic Web—that is, dynamic, distributed, incomplete, and uncertain knowledge—suggest the need for autonomy in distributed software systems. Semantic Web research promises more than mere management of ontologies and data through the definition of machine-understandable languages. The openness and decentralization introduced by multiagent systems and service-oriented architectures give rise to new knowledge management models, for which we can’t make a priori assumptions about the type of interaction an agent or a service may be engaged in, and likewise about the message protocols and vocabulary used. We therefore discuss the problem of knowledge management for open multi-agent systems, and highlight a number of challenges relating to the exchange and evolution of knowledge in open environments, which pertinent to both the Semantic Web and Multi Agent System communities alike

A Semantic Collaboration Method Based on Uniform Knowledge Graph

The Semantic Internet of Things is the extension of the Internet of Things and the Semantic Web, which aims to build an interoperable collaborative system to solve the heterogeneous problems in the Internet of Things. However, the Semantic Internet of Things has the characteristics of both the Internet of Things and the Semantic Web environment, and the corresponding semantic data presents many new data features. In this study, we analyze the characteristics of semantic data and propose the concept of a uniform knowledge graph, allowing us to be applied to the environment of the Semantic Internet of Things better. Here, we design a semantic collaboration method based on a uniform knowledge graph. It can take the uniform knowledge graph as the form of knowledge organization and representation, and provide a useful data basis for semantic collaboration by constructing semantic links to complete semantic relation between different data sets, to achieve the semantic collaboration in the Semantic Internet of Things. Our experiments show that the proposed method can analyze and understand the semantics of user requirements better and provide more satisfactory outcomes

A Semantic-Based Information Management System to Support Innovative Product Design

International competition and the rapidly global economy, unified by improved communication and transportation, offer to the consumers an enormous choice of goods and services. The result is that companies now require quality, value, time to market and innovation to be successful in order to win the increasing competition. In the engineering sector this is traduced in need of optimization of the design process and in maximization of re-use of data and knowledge already existing in the company. The “SIMI-Pro” (Semantic Information Management system for Innovative Product design) system addresses specific deficiencies in the conceptual phase of product design when knowledge management, if applied, is often sectorial. Its main contribution is in allowing easy, fast and centralized collection of data from multiple sources and in supporting the retrieval and re-use of a wide range of data that will help stylists and engineers shortening the production cycle. SIMI-Pro will be one of the first prototypes to base its information management and its knowledge sharing system on process ontology and it will demonstrate how the use of centralized network systems, coupled with Semantic Web technologies, can improve inter-working activities and interdisciplinary knowledge sharing