Forming coalitions is a generic means for cooperation: people, robots, web services, resources, firms; they can all improve their performance by joining forces. The use of coalitions has been successful in domains such as task allocations, sensor networks, and electronic marketplaces. Forming efficient coalitions requires the identification of matching synergies between different entities (finding complementary partners, or similar partners, or partners who add diversity). In addition, the different parties must negotiate a fair repartition of the worth created by the coalition. The first part of this paper is a tutorial on cooperative game theory (also called coalitional games). We then survey the different scenarios and the key issues addressed by the multiagent systems community.

This article presents the Kasimir system dedicated to decision knowledge management in oncology and which is built on top of Semantic Web technologies, taking benefit from standard knowledge representation formalisms and open reasoning tools. The representation of medical decision protocols, in particular for breast cancer treatment, is based on concepts and instances implemented within the description logic OWL DL (Web ontology language description logic). The knowledge units related to a protocol can then be applied for solving specific medical problems, using instance or concept classification. However, the straight application of a protocol is not always satisfactory, for example, because of contraindications, necessitating an adaptation of the protocol. This is why the principles and methods of case-based reasoning (CBR) in the framework of DLs have been used. In addition, the domain of oncology is complex and involves several specialties, for example, surgery and chemotherapy. This complexity can be better undertaken with a viewpoint-based representation of protocols and viewpoint-based reasoning, for either application or adaptation of the protocols. Accordingly, a distributed DL has been used for representing a viewpoint-based protocol. The application and the adaptation of the viewpoint-based protocol to medical cases is carried out using global instance classification and decentralized CBR.

The fast increase in the size and number of databases demands data mining approaches that are scalable to large amounts of data. This has led to the exploration of parallel computing technologies in order to perform data mining tasks concurrently using several processors. Parallelization seems to be a natural and cost-effective way to scale up data mining technologies. One of the most important of these data mining technologies is the classification of newly recorded data. This paper surveys advances in parallelization in the field of classification rule induction.

To work in a higher abstraction level is of critical importance for the development of multiagent systems (MAS) since it is almost impossible to observe code-level details of such systems due to their internal complexity, distributedness and openness. As one of the promising software development approaches, model-driven development (MDD) aims to change the focus of software development from code to models. This paradigm shift, introduced by the MDD, may also provide the desired abstraction level during the development of MASs. For this reason, MDD of autonomous agents and MASs has been recognized and become one of the research topics in agent-oriented software engineering (AOSE) area. Contributions are mainly based on the model-driven architecture (MDA), which is the most famous and in-use realization of MDD. Within this direction, AOSE researchers define MAS metamodels in various abstraction levels and apply model transformations between the instances of these metamodels in order to provide rapid and efficient implementation of the MASs in various platforms. Reorganization of the existing MAS development methodologies to support model-driven agent development is another emerging research track. In this paper, we give a state of the art survey on above mentioned model-driven MAS development research activities and evaluate the introduced approaches according to five quality criteria we define on model-driven MAS engineering: (1) definition of a platform independent MAS metamodel, (2) model-to-model transformability, (3) model-to-code transformability, (4) support for multiple MAS platforms and finally (5) tool support for software modeling and code generation. Our evaluation has shown that the researchers contributed to the area by providing MDD processes in which design of the MASs are realized at a very high abstraction level and the software for these MASs are developed as a result of the application of a series of model transformations. However, most of the approaches are incapable of supporting multiple MAS environments due to the restricted specifications of their metamodels and model transformations. Also efficiency and practicability of the proposed methodologies are under debate since the amount and quality of the executable MAS components, gained automatically, appear to be not sufficient.