Currently, biological databases (DBs) are a common tool to complement the research of a wide range of biomedical disciplines, but there are only a few specialized medical DBs for human brain tumour magnetic resonance spectroscopy (MRS) data; they typically store a limited range of biological data (i.e. clinical information, magnetic resonance imaging and MRS data) and are not offered as open-source Structured Query Language relational DB schemas. We present a novel approach to biological DBs: a distributed Web-accessible DB for storing and managing clinical and biomedical data related to brain tumours from different clinical centres. This tool is designed for multi-platform systems with dissimilar DB management systems. Being the main data repository of the HealthAgents (HA) project, it uses multi-agent technology and allows the centres to share data and obtain diagnosis classifications from other centres distributed around the world in a reliable way.

The HA project aims to create an agent-based distributed decision support system (DSS) to assist doctors to provide a brain tumour diagnosis and prognosis. The HA DB enables the DSS to totally integrate with its Graphical User Interface to perform classifications with the stored data and visualize the results using the HA distributed agents framework. This new feature converts the system presented in the first application in the world to combine a storage and management tool for brain tumour data and a complete Web-based DSS to obtain automatic diagnosis.

This paper advocates a new science of intelligence, one that is holistic, multi-disciplinary, oriented to crucial values as health and well-being and able to contribute to the solution of real-world problems. As a starting point we study the interplay between two research disciplines that until now have been hardly related to each other: Ayurveda and multi-agent systems. We consider some possible results of the cross fertilisation like for instance the application of ayurvedic knowledge to improve the skills of practical reasoning agents.

Magnetic resonance spectroscopy (MRS) is a non-invasive method, which can provide diagnostic information on children with brain tumours. The technique has not been widely used in clinical practice, partly because of the difficulty of developing robust classifiers from small patient numbers and the challenge of providing decision support systems (DSSs) acceptable to clinicians. This paper describes a participatory design approach in the development of an interactive clinical user interface, as part of a distributed DSS for the diagnosis and prognosis of brain tumours. In particular, we consider the clinical need and context of developing interactive elements for an interface that facilitates the classification of childhood brain tumours, for diagnostic purposes, as part of the HealthAgents European Union project. Previous MRS-based DSS tools have required little input from the clinician user and a raw spectrum is essentially processed to provide a diagnosis sometimes with an estimate of error. In childhood brain tumour diagnosis where there are small numbers of cases and a large number of potential diagnoses, this approach becomes intractable. The involvement of clinicians directly in the designing of the DSS for brain tumour diagnosis from MRS led to an alternative approach with the creation of a flexible DSS that, allows the clinician to input prior information to create the most relevant differential diagnosis for the DSS. This approach mirrors that which is currently taken by clinicians and removes many sources of potential error. The validity of this strategy was confirmed for a small cohort of children with cerebellar tumours by combining two diagnostic types, pilocytic astrocytomas (11 cases) and ependymomas (four cases) into a class of glial tumours which then had similar numbers to the other diagnostic type, medulloblastomas (18 cases). Principal component analysis followed by linear discriminant analysis on magnetic resonance spectral data gave a classification accuracy of 91% for a three-class classifier and 94% for a two-class classifier using a leave-one-out analysis. This DSS provides a flexible method for the clinician to use MRS for brain tumour diagnosis in children.

Research on the relation between Belief Revision and Argumentation has always been fruitful in both directions: some argumentation formalisms can be used to define belief change operators, and belief change techniques have also been used for modeling the dynamics of beliefs in argumentation formalisms. In this paper, we give a historical perspective on how belief revision has evolved in the last three decades, and how it has been combined with argumentation. First, we will recall the foundational works concerning the links between both areas. On the basis of such insights, we will present a conceptual view on this topic and some further developments. We offer a glimpse into the future of research in this area based on the understanding of argumentation and belief revision as complementary, mutually useful disciplines.

The Semantic Web has radically changed the landscape of knowledge acquisition research. It used to be the case that a single user would edit a local knowledge base, that the user would have domain expertise to add to the system, and that the system would have a centralized knowledge base and reasoner. The world surrounding knowledge-rich systems changed drastically with the advent of the Web, and many of the original assumptions were no longer a given. Those assumptions had to be revisited and addressed in combination with new challenges that were put forward. Knowledge-rich systems today are distributed, have many users with different degrees of expertise, and integrate many shared knowledge sources of varying quality. Recent work in interactive knowledge capture includes new and exciting research on collaborative knowledge sharing, collecting knowledge from Web volunteers, and capturing knowledge provenance.

Starting from the pioneering work on Linda and Gamma, coordination models and languages have gone through an amazing evolution process over the years. From closed to open systems, from parallel computing to multi-agent systems and from database integration to knowledge-intensive environments, coordination abstractions and technologies have gained in relevance and power in those scenarios where complexity has become a key factor. In this paper, we outline and motivate 25 years of evolution of coordination models and languages, and discuss their potential perspectives in the future of artificial systems.

Game theory is becoming central to the design and analysis of computational mechanisms in which multiple entities interact strategically. The tools of mechanism design are used extensively to engineer incentives for truth revelation into resource allocation (e.g. combinatorial auctions) and preference aggregation protocols (e.g. voting). We argue that mechanism design can also be useful in the design of logical inference procedures. In particular, it can help us understand and engineer inference procedures when knowledge is distributed among self-interested agents. We set a research agenda for this emerging area, and point to some early research efforts.