Search

Collaboration and self-organization are hallmarks of many biological systems. We present the design for an intelligent decision support system that employs these characteristics: it works through a collaborative, self-organizing network of intelligent agents. Developed for the realm of Marine Safety and Security, the goal of the system is to assist in the management of a complex array of resources in both a routine and emergency role. Notably, this system must be able to handle a dynamic environment and the existence of uncertainty. The decentralized control structure of a collaborative self-organizing system reinforces its adaptiveness, robustness and scalability in critical situations. [ABSTRACT FROM AUTHOR]

Automated Planning is a very active area of research within Artificial Intelligence. Broadly this discipline deals with the methods by which an agent can independently determine the action sequence required to successfully achieve a set of objectives. In this paper, we will present initial work outlining a new approach to planning based on Clustering techniques, in order to group states of the world together and use the fundamental structure of the world to lift out more abstract representations. We will show that this approach can limit the combinatorial explosion of a typical planning problem in a way that is much more intuitive and reusable than has previously been possible, and outline ways that this approach can be developed further. [ABSTRACT FROM AUTHOR]

Highly available and resilient networks play a decisive role in today΄s networked world. As network faults are inevitable and networks are becoming constantly intricate, finding effective fault recovery solutions in a timely manner is becoming a challenging task for administrators. Therefore, an automated mechanism to support fault resolution is essential towards efficient fault handling process. In this paper we propose an architecture to support automated fault recovery in terms of traffic engineering, recovery knowledge discovery and automated recovery planning. We base our discussion on an application scenario for recovery from border router failure to maintain optimized configuration of outbound inter-domain traffic. [ABSTRACT FROM AUTHOR]

The aim of the paper is to present rough set methods of constructing hierarchical classifiers for approximation of complex concepts. Classifiers are constructed on the basis of experimental data sets and domain knowledge that are mainly represented by concept ontology. Information systems, decision tables and decision rules are basic tools for modeling and constructing such classifiers. The general methodology presented here is applied to approximate spatial complex concepts and spatio-temporal complex concepts defined for (un)structured complex objects, to identify the behavioral patterns of complex objects, and to the automated behavior planning for such objects when the states of objects are represented by spatio-temporal concepts requiring approximation. We describe the results of computer experiments performed on real-life data sets from a vehicular traffic simulator and on medical data concerning the infant respiratory failure. [ABSTRACT FROM AUTHOR]

We discuss an application of rough set tools for modeling networks of classifiers induced from data and ontology of concepts delivered by experts. Such networks allow us to develop strategies for automated planning of a treatment of infants with respiratory illness. We report results of experiments with the networks of classifiers used in automated planning of the treatment of newborn infants with respiratory failure. The reported experiments were performed on medical data obtained from the Neonatal Intensive Care Unit in the Department of Pediatrics, Collegium Medicum, Jagiellonian University. [ABSTRACT FROM AUTHOR]