Data Mining Practical Machine Learning Tools and Techniques

An Introduction to MultiAgent Systems.

In the Hamadryas baboon, males are substantially larger than females. A troop of baboons is subdivided into a number of ‘one-male groups’, consisting of one adult male and one or more females with their young. The male prevents any of ‘his’ females from moving too far from him. Kummer (1971) performed the following experiment. Two males, A and B, previously unknown to each other, were placed in a large enclosure. Male A was free to move about the enclosure, but male B was shut in a small cage, from which he could observe A but not interfere. A female, unknown to both males, was then placed in the enclosure. Within 20 minutes male A had persuaded the female to accept his ownership. Male B was then released into the open enclosure. Instead of challenging male A , B avoided any contact, accepting A’s ownership.

Evolution and the Theory of Games

Cancer has been characterized as a heterogeneous disease consisting of many different subtypes. The early diagnosis and prognosis of a cancer type have become a necessity in cancer research, as it can facilitate the subsequent clinical management of patients. The importance of classifying cancer patients into high or low risk groups has led many research teams, from the biomedical and the bioinformatics field, to study the application of machine learning (ML) methods. Therefore, these techniques have been utilized as an aim to model the progression and treatment of cancerous conditions. In addition, the ability of ML tools to detect key features from complex datasets reveals their importance. A variety of these techniques, including Artificial Neural Networks (ANNs), Bayesian Networks (BNs), Support Vector Machines (SVMs) and Decision Trees (DTs) have been widely applied in cancer research for the development of predictive models, resulting in effective and accurate decision making. Even though it is evident that the use of ML methods can improve our understanding of cancer progression, an appropriate level of validation is needed in order for these methods to be considered in the everyday clinical practice. In this work, we present a review of recent ML approaches employed in the modeling of cancer progression. The predictive models discussed here are based on various supervised ML techniques as well as on different input features and data samples. Given the growing trend on the application of ML methods in cancer research, we present here the most recent publications that employ these techniques as an aim to model cancer risk or patient outcomes.

Machine learning applications in cancer prognosis and prediction.

There has been increased research interest in systems composed of multiple autonomous mobile robots exhibiting cooperative behavior. Groups of mobile robots are constructed, with an aim to studying such issues as group architecture, resource conflict, origin of cooperation, learning, and geometric problems. As yet, few applications of cooperative robotics have been reported, and supporting theory is still in its formative stages. In this paper, we give a critical survey of existing works and discuss open problems in this field, emphasizing the various theoretical issues that arise in the study of cooperative robotics. We describe the intellectual heritages that have guided early research, as well as possible additions to the set of existing motivations.

Cooperative mobile robotics: antecedents and directions

This paper presents a genetic-algorithm driven, cognitive radio decision engine that determines the optimal radio transmission parameters for single and multicarrier systems Determining the appropriate radio parameters, given a dynamic wireless channel environment is the primary feature of cognitive radios for wireless communication systems Genetic algorithms (GAs) are designed to select the optimal transmission parameters by scoring a subset of parameters and evolving them until the optimal value is reached for a given goal Although there have been implementations of GA-based single carrier cognitive radio engines, the performance of these algorithms has not been thoroughly analyzed nor have the fitness functions employed by the algorithms been explored in detail Multicarrier systems are common in today's communication environment, thus cognitive techniques that account for only single-carrier systems neglect the practical issues of multiple carriers A set of accurate single carrier and multicarrier fitness functions for our GA implementation that completely control the evolution of the algorithm have been derived The performance analysis results illustrate the trade-offs between the convergence time of the GA and the size of the GA search space Copyright © 2007 John Wiley & Sons, Ltd

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Cognitive engine implementation for wireless multicarrier transceivers

It is envisioned that in the near future personal mobile robots will be assisting people in their daily lives. An essential characteristic shaping the design of personal robots is the fact that they must be accepted by human users. This paper explores the interactions between humans and mobile personal robots, by focusing on the psychological effects of robot behavior patterns during task performance. These behaviors include the personal robot approaching a person, avoiding a person while passing, and performing non-interactive tasks in an environment populated with humans. The level of comfort the robot causes human subjects is analyzed according to the effects of robot speed, robot distance, and robot body design, as these parameters are varied in order to present a variety of behaviors to human subjects. The information gained from surveys taken by 40 human subjects can be used to obtain a better understanding of what characteristics make up personal robot behaviors that are most acceptable to the human users.

Psychological Effects of Behavior Patterns of a Mobile Personal Robot

In this paper, we present the details of a portable, powerful, and flexible software-defined radio development platform called the Kansas University Agile Radio (KUAR). The primary purpose of the KUAR is to enable advanced research in the areas of wireless radio networks, dynamic spectrum access, and cognitive radios. The KUAR hardware implementation and software architecture are discussed in detail. Radio configurations and applications are presented. Future research made possible by this flexible platform is also discussed.

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KUAR: A Flexible Software-Defined Radio Development Platform

Inverse kinematics computation using an artificial neural network that learns the inverse kinematics of a robot arm has been employed by many researchers. However, the inverse kinematics system of typical robot arms with joint limits is a multivalued and discontinuous function. Since it is difficult for a well-known multilayer neural network to approximate such a function, a correct inverse kinematics model cannot be obtained by using a single neural network. In order to overcome the discontinuity of the inverse kinematics function, we proposed a novel modular neural network system that consists of a number of expert neural networks. Each expert approximates the continuous part of the inverse kinematics function. The proposed system uses the forward kinematics model for selection of experts. When the number of the experts increases, the computation time for calculating the inverse kinematics solution also increases without using the parallel computing system. In order to reduce the computation time, we propose a novel expert selection by using the performance prediction networks which directly calculate the performances of the experts.

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Inverse kinematics learning by modular architecture neural networks with performance prediction networks

We focus on the task of large area surveillance. Given an area to be surveilled and a team of micro air vehicles (MAVs) with appropriate sensors, the task is to dynamically distribute the MAVs appropriately in the surveillance area for maximum coverage based on features present on the ground, and to adjust this distribution over time as changes in the team or on the ground occur. We have developed a system that learn rule sets for controlling the individual MAVs in a distributed surveillance team. Since each rule set governs an individual MAV, control of the overall behavior of the entire team is distributed; there is no single entity controlling the actions of the entire team. Currently, all members of the MAV team utilize the same rule set; specialization of individual MAVs through the evolution of unique rule sets is a logical extension to this work. A genetic algorithm is used to learn the MAV rule sets.

Arvin Agah

Papers

Cognitive engine implementation for wireless multicarrier transceivers

Psychological Effects of Behavior Patterns of a Mobile Personal Robot

KUAR: A Flexible Software-Defined Radio Development Platform

Inverse kinematics learning by modular architecture neural networks with performance prediction networks

Evolving control for distributed micro air vehicles