Stanimir Stoyanov

An ontology based CBR architecture for knowledge management in BULCHINO catalogue

In this paper we present otology-based architecture for knowledge management in BULCHINO web-based catalogue for Bulgarian cultural heritage. Our approach for realizing content-based search and retrieval in the catalogue implies the application of the CBR technology for the representation of metadata characterizations of the cultural objects and the integration of ontology for semantic formalization of these characterizations. We provide an insight of the technical aspects of the architecture by enumerating the technological requirements, where we pay specific attention to the CBR framework jCOLIBRI and its features for implementing concept-based similarity assessment.

Semantic Multi-Agent mLearning System

Within this chapter, an agent-oriented middleware created to support the delivery of context-aware mLearning services provision is presented. This middleware architecture, based on the concept of InfoStations and developed within a University campus domain, is described in detail. Concepts for the control and management of service sessions and communications scenarios are also presented. The multi-agent approach adopted for the implementation of this system and indeed the system entity interactions involved in service delivery are discussed. The harvesting and utilisation of semantic information in order to facilitate the contextualisation and personalisation of mLearning services is also detailed.

Multi-agent architecture for context-aware mLearning provision via InfoStations

A software architecture for context-aware mLearning provision via InfoStations within a University Campus is presented. The multi-agent nature of the proposed architecture receives particular attention.

A Multi-Step Procedure for Asset Allocation in Case of Limited Resources

Abstract Portfolio management is a process involving decision making in dynamic and unpredictable environment. Asset allocation plays a key role in this process, since the optimal use of the capital is a complex and resource-consuming problem. During our research in this field we have detected some problems that lead to biased results and one of them occurs in case of limited financial resources. In this paper a mathematical assessment of the dependence of the capital, used on asset prices is derived, and a multi-step procedure for asset allocation, aiming at optimization of the investor’s utility in case of limited resources is described. The procedure is implemented as a module in a decision support system based on fuzzy logic. The paper contains comparison of the obtained test results with results from the classical Markowitz portfolio model. The conducted tests are on real data from the Bulgarian stock exchange.

Agent-Oriented Middleware for Mobile eLearning Services

An agent-oriented middleware supporting context-aware mobile eLearning services provision is presented. The middleware architecture developed for a distributed InfoStation-based network established within a University Campus is described in detail. A concept for the control and management of service sessions and communications scenarios is also presented.

An Approach for the Development of a Context-Aware and Adaptive eLearning Middleware

This chapter describes a generic, service-oriented and agent-based approach for the development of eLearning intelligent system architectures providing wireless access to electronic services (eServices) and electronic content (eContent) for users equipped with mobile devices, via a set of InfoStations deployed in key points around a University Campus. The approach adopts the ideas suggested by the Model Driven Architecture (MDA) specification of the Object Management Group (OMG). The architectural levels and iterations of the approach are discussed in detail and the resultant context-aware, adaptive middleware architecture is presented. The classification and models of the supporting agents are presented as well.

Context-Aware mLearning Service Execution in an InfoStations Environment

A software system architecture for context-aware mLearning service execution in an InfoStations environment established within a University Campus is presented. The main user scenarios affecting the context-aware service execution are described. The multi-agent nature of the InfoStation’s architecture receives particular attention.

Service-oriented and agent-based approach for the development of InfoStation eLearning intelligent system architectures

The paper describes a generic service-oriented and agent-based approach for the development of eLearning intelligent system architectures providing mobile access to electronic services (eServices) and electronic content (eContent) for users equipped with wireless devices, via a set of infoStations deployed in key points around a University Campus. The approach adopts the ideas suggested by the MDA specification of OMG. The agent level and service level of the resultant system architecture are discussed in detail. A classification and models of supporting agents are presented as well.

Agent-Based Support of a Virtual eLearning Space

This paper provides an overview and presents the architecture of a virtual space supporting eLearning. Various types of assistants are examined as well which are implemented as rational BDI agents supporting the operation of the space. Furthermore, development of the space as an IoT ecosystem is considered.

Automatic generation of test questions by software agents using ontologies

This paper presents test environment that is intended to support e-learning in software engineering education. The environment is part of the Virtual Education Space and consists of two operatives named Questioner Operatives. They support automatic generation and assessment of the test questions by using ontologies. These operatives are implemented as intelligent agents that use structured educational content (an ontology in the Unified Modeling Language domain) to generate and assess different types of test questions, according to the QTI (Questions and Test Interoperability specification.