Construction of Intelligent Virtual Worlds Using a Grammatical Framework

The potential of integrating multiagent systems and virtual environments has not been exploited to its whole extent. This paper proposes a model based on grammars, called Minerva, to construct complex virtual environments that integrate the features of agents. A virtual world is described as a set of dynamic and static elements. The static part is represented by a sequence of primitives and transformations and the dynamic elements by a series of agents. Agent activation and communication is achieved using events, created by the so-called event generators. The grammar defines a descriptive language with a simple syntax and a semantics, defined by functions. The semantics functions allow the scene to be displayed in a graphics device, and the description of the activities of the agents, including artificial intelligence algorithms and reactions to physical phenomena. To illustrate the use of Minerva, a practical example is presented: a simple robot simulator that considers the basic features of a typical robot. The result is a functional simple simulator. Minerva is a reusable, integral, and generic system, which can be easily scaled, adapted, and improved. The description of the virtual scene is independent from its representation and the elements that it interacts with.

Aid decision algorithms to estimate the risk in congenital heart surgery

Background and objective: In this paper, we have tested the suitability of using different artificial intelligence-based algorithms for decision support when classifying the risk of congenital heart surgery. In this sense, classification of those surgical risks provides enormous benefits as the a priori estimation of surgical outcomes depending on either the type of disease or the type of repair, and other elements that influence the final result. This preventive estimation may help to avoid future complications, or even death. Methods: We have evaluated four machine learning algorithms to achieve our objective: multilayer perceptron, self-organizing map, radial basis function networks and decision trees. The architectures implemented have the aim of classifying among three types of surgical risk: low complexity, medium complexity and high complexity. Results: Accuracy outcomes achieved range between 80% and 99%, being the multilayer perceptron method the one that offered a higher hit ratio. Conclusions: According to the results, it is feasible to develop a clinical decision support system using the evaluated algorithms. Such system would help cardiology specialists, paediatricians and surgeons to forecast the level of risk related to a congenital heart disease surgery.