Karin Pröll

• University of Applied Sciences Upper Austria

In this chapter we present a novel method for scoring function specification and feature selection by combining unsupervised learning with supervised cross validation. Various clustering algorithms such as one dimensional Kohonen SOM, k-means, fuzzy c-means and hierarchical clustering procedures are used to perform a clustering of object-data for a...

The paper presents the analysis of two different approaches for a system to support cancer diagnosis. The first one uses only tumor marker data containig missing values to predict cancer occurrence and the second one also includes standard blood parameters. Both systems are based on several heterogeneous artificial neural networks for estimating mi...

The paper presents methods that allow an intelligent multiagent system to coordinate and negotiate their actions in order to achieve a common goal.

The design of intelligent and sensor-based autonomous agents learning by themselves to perform complex real-world tasks is a still-open challenge for artificial and computational intelligence. In this paper a concept of a framework for conflict resolution in an autonomous robotic agent system is presented. The structure of an intelligent robotic ag...

Background: While many authors have discussed models and tools for studying protein evolution at the sequence level, molecular function is usually mediated by complex, higher order features such as independently folding domains and linear motifs that are based on or embedded in a particular arrangment of features such as secondary structure elemen...

This paper presents an approach for a Q-learning based decision support system for therapy planning. It focuses the consideration on a multilevel approach for constructing a data driven evidence based model for classification of different drug dosages and their effectiveness for clinical trials. We consider time-ordered sequences of patient data (c...

The paper presents a method that allows an intelligent multiagent system to coordinate and negotiate their actions in order to achieve a common goal. Each individual agent consists of several autonomous components that allow the agent to perceive and react to its environment, to plan and execute an action, and to negotiate with other agents in an i...

This paper presents an approach for a multilevel knowledge base system for evidence-based medicine. A sequence of events called patient trial is extracted from computer patient records. These events describe one flow of therapy for a concrete disease. Each event is represented by state and time. We introduce a measure between states, which is used...

This paper presents a system-theoretical approach for an intelligent multiagent system which is used to control and coordinate a society of robotic agents acting independently in a partially known environment. We focus our presentation on only one aspect of the coordination of the robotic agent group, namely on the conflict management. We present s...

Intelligent agents are a new paradigm for developing complex system applications. Agent systems are based on autonomous software-hardware components (technical agents) that cooperate within an environment to perform some task. An agent can be viewed as a self-contained, concurrently executing thread of control that encapsulates some states and comm...

Within the last years the paradigm of intelligent software agents became an emerging topic in computer science research and development. Intelligent software agents are computational systems that populate complex dynamic environment, act and react there in an autonomous way in order realize a specific task based on a set of goals given to them. In...

Therapy modeling and planning are important components for optimal and cost-effective patient care. Therapeutic response of the individual patient not only relies on the selection of effective drugs but is also heavily influenced by appropriate drug dosage. A variety of intelligent techniques have been initiated to support physicians in deciding an...

A multiagent robotic system consists of a group of autono-mous agents on the first -lower -level of its hierarchy, and the contract and conflict management agents on the second -upper -level of the hierarchy. The contract man-agement agent considers task distribution when a new job enters the system. It has to direct autonomous agents by specifying...