Ensemble learning is a promising direction of research in machine learning, in which an ensemble classifier gives better predictive and more robust performance for classification problems by combining other learners. Meanwhile agent-based systems provide frameworks to share knowledge from multiple agents in an open context. This thesis combines multi-agent knowledge sharing with ensemble methods to produce a new style of learning system for open environments. We now are surrounded by many smart objects such as wireless sensors, ambient communication devices, mobile medical devices and even information supplied via other humans. When we coordinate smart objects properly, we can produce a form of collective intelligence from their collaboration. Traditional ensemble methods and agent-based systems have complementary advantages and disadvantages in this context. Traditional ensemble methods show better classification performance, while agent-based systems might not guarantee their performance for classification. Traditional ensemble methods work as closed and centralised systems (so they cannot handle classifiers in an open context), while agent-based systems are natural vehicles for classifiers in an open context. We designed an open and social ensemble learning architecture, named J-model, to merge the conflicting benefits of the two research domains. The J-model architecture is based on a service choreography approach for coordinating classifiers. Coordination protocols are defined by interaction models that describe how classifiers will interact with one another in a peer-to-peer manner. The peer ranking algorithm recommends more appropriate classifiers to participate in an interaction model to boost the success rate of results of their interactions. Coordinated participant classifiers who are recommended by the peer ranking algorithm become an ensemble classifier within J-model. We evaluated J-model’s classification performance with 13 UCI machine learning benchmark data sets and a virtual screening problem as a realistic classification problem. J-model showed better performance of accuracy, for 9 benchmark sets out of 13 data sets, than 8 other representative traditional ensemble methods. J-model gave better results of specificity for 7 benchmark sets. In the virtual screening problem, J-model gave better results for 12 out of 16 bioassays than already published results. We defined different interaction models for each specific classification task and the peer ranking algorithm was used across all the interaction models. Our research contributions to knowledge are as follows. First, we showed that service choreography can be an effective ensemble coordination method for classifiers in an open context. Second, we used interaction models that implement task specific coordinations of classifiers to solve a variety of representative classification problems. Third, we designed the peer ranking algorithm which is generally and independently applicable to the task of recommending appropriate member classifiers from a classifier pool based on an open pool of interaction models and classifiers.

The world is full of signs (symbols, signifiers). They guide us in supermarkets, on highways and in airports. They even guide us to the right bathroom. Signs have a huge impact if they are used as logos or in advertisements. Signs also help us when we read manuals or when we use a remote control. Signs are used everywhere. Signs render service as they are 'applications of skills and knowledge for the benefit of another party' (Vargo and Lusch 2004, 2008). Like all offerings signs only render service if they are used in some way. This article will argue that the usage of signs depends on the practices they are embedded in. It is argued that signs do not gain their full meaning and do not serve or create any value unless they are embedded in practices. Since signs are explicit and practices mainly implicit they are both necessary to serve and more importantly to (co-)create value. Signs and practices are two sides of the same coin so both they (and their interrelation) have to be understood in order to offer a better service.

Tourism is not only one of the world's fastest growing industries but also verycompetitive. To be able to run a competitive and attractive tourist destination, itis extremely important that the cooperation between all actors works. Manyresearchers claim that a functioning destination management is the cornerstoneof a successful and sustainable tourist destination. However, this cooperationbetween different actors, whether it is the private or the official sector can bemore difficult if a destination is divided by administrative boundaries. As many tourist destinations around the world are divided by administrativeboundaries, it is important to gain a better understanding of how theseadministrative areas and borders can affect tourism planning and destinationdevelopment. Already in the 20th century, researchers recognized thatcooperation between different municipal levels is of great importance, as this isthe only way to achieve a competitive tourist destination. Now that tourism is oneof the world's fastest growing industries and often also people's only source ofincome, it is even more important that the collaboration between different actorswithin a destination works. Collaboration is the foundation of a long-termattractive and competitive tourist destination.Despite a well-functioning destination management, this does not mean thateverything works as it should. Tourism is a complex phenomenon that requiressignificantly more understanding than just a functioning destination management.Tourism is part of an entire form of society that requires a deep understanding,which many actors often fail to do.