Catálogo de publicaciones - libros

Since its introduction by George Boole during the mid-1800s, Boolean algebra has become an important part of the of mathematics, science, engineering, and research in artificial intelligence, machine learning and data mining. The Boolean reasoning approach has manifestly become a powerful tool for designing effective and accurate solutions for many problems in decision-making and approximate reasoning optimization. In recent years, Boolean reasoning has become a recognized technique for developing many interesting concept approximation methods in rough set theory. The problem considered in this paper is the creation of a general framework for concept approximation. The need for such a general framework arises in machine learning and data mining. This paper presents a solution to this problem by introducing a general framework for concept approximation which combines rough set theory, Boolean reasoning methodology and data mining. This general framework for approximate reasoning is called (RSABR). The contribution of this paper is the presentation of the theoretical foundation of RSABR as well as its application in solving many data mining problems and knowledge discovery in databases (KDD) such as feature selection, feature extraction, data preprocessing, classification of decision rules and decision trees, association analysis.

The human leg with segments, joints and many muscles is a complicated device. Yet, in dynamic situations such as running, hopping or jumping we behave with ease and without being overwhelmed by the complicated task. We argue that this is possible due to a careful arrangement and fine tuning of all properties from which stability and robustness emerges. Robust and stable systems are easy to control.

To investigate walking we perform experimental studies on animals in parallel with software and hardware simulations of the control structures and the body to be controlled. In this paper, we will first describe the basic behavioral properties of hexapod walking, as the are known from stick insects. Then we describe a simple neural network called Walknet which exemplifies these properties and also shows some interesting emergent properties. The latter arise mainly from the use of the physical properties to simplify explicit calculations. The model is simple, too, because it uses only static neuronal units. The system is currently tested using an adapted version of the robot TARRY II.

There are many scientific and technological problems that we cannot deal with today. Our current scientific methodology cannot be applied to what is called the real world problem. Because the real world is unpredictably and dynamically changing, it is impossible to objectify it in advance and to apply the traditional methodology to it. This real world problem especially arises in information processing systems such as the recognition and the control systems coping with the real world. The current information systems request in advance the complete information to deal with. In the case of robot in the real world, to attain the purpose a robot is usually required to solve the inverse problem adjusting the changes of the real world. It is always an ill-posed problem. When the robot autonomously solves the ill-posed problem, some proper constraints should be self-organized in the robot. In addition to the self-organization of the constraints, the robot is required to satisfy the constraints in real time. Here we propose a new real-time control mechanism for the purposive movements of a robot under the unpredictably changing environment.

This paper presents a method for energy efficient walking of a biped robot with a layered controller. The lower layer controller has a state machine for each leg. The state machine consists of four states: 1) constant torque is applied to hip and knee joints of the swing leg, 2) no torque is applied so that the swing leg can move in a ballistic manner, 3) a PD controller is used so that the certain posture can be realized at the heel contact, which enables a biped robot to walk stably, and 4) as the support leg, hip and knee joints are servoed to go back and the torque to support upper leg is applied. With this lower layer controller, the upper layer controller can search parameters that enable the robot to walk as energy efficiently as human walking without paying any attention to fall down.

Bipedal (Bp) terrestrial locomotion is a routine, everyday activity for humans and advanced non-human primates. While its elaboration seems simple, it actually involves much skill and long-term locomotor learning, such that the CNS can achieve a seamless spatial and temporal integration of multiple motor segments. To advance understanding of the CNS control mechanisms that operate during Bp locomotion, it seemed necessary to make use of a non-human primate model. This strategy invites the possibility of employing state-of-the-art interventional recording techniques and cellular-to-systems level of neuroscientific analysis to the study of locomotion. We think that the study of posture and locomotion is fundamental to the understanding of basic brain-behavior relationships from the cellular to the behavioral level of analysis. To this end, we used operant conditioning to train the normally quadrupedal (Qp)-walking juvenile Japanese monkey () to stand upright and walk bipedally on the surface of a moving treadmill belt. Our studies have started to reveal brain mechanisms involved in the successful emergence and elaboration of Bp locomotion.

During half-bound gait on a treadmill pikas (: Lagomorpha) show a preference in the choice of the trailing limb (“handedness”). Duration of steps shows significantly higher variation in the trailing limb than in the leading limb. This observation motivated calculations of the position of the center of mass (CoM) in the body frame of the pika during half-bound cycles. CoM is aligned with first of the ulna of the trailing and second of the leading limb during major parts of the forelimbs’ stance phase. Referring to our large cineradiographic data base on the kinematics of the legs we could note that the horizontal motion of the CoM in the body is mainly determined by flexion and extension of the back. This observation underlines the determinant role of the trunk as the main engine for fast locomotion. Using high-speed video films we measured the angle of attack (defined as the angle between the ulna and the ground at touch down). We couldn’t observe any significant change with speed during half-bound, indicating the important role of self-stabilising mechanisms on the choice of kinematics.

This paper examines how simple control laws stabilize complex running behaviors such as bounding. First, we discuss the unexpectedly different local and global forward speed versus touchdown angle relationships in the self-stabilized Spring Loaded Inverted Pendulum. Then we show that, even for a more complex energy conserving unactuated quadrupedal model, many bounding motions exist, which can be locally open loop stable! The success of simple bounding controllers motivated the use of similar control laws for asymmetric gaits resulting in the first experimental implementations of the half-bound and the rotary gallop on Scout II.

Designing for adaptive motion is still largely considered an art. In recent years, we have been developing a set of heuristics or design principles, that on the one hand capture theoretical insights about adaptive systems, and on the other provide guidance in actually designing and building adaptive systems. In this paper we discuss, in particular, the principle of “ecological balance” which is about the relation between morphology, materials, and control. As we will argue, artificial evolution together with morphogenesis is not only “nice to have” but turns out to be a necessary design tool for adaptive motion.

This study is intended to deal with the interdependency between control and body systems, and to discuss the “relationship as it should be” between these two systems. To this end, a decentralized control of a multi-legged robot is employed as a practical example. The results derived indicate that the convergence of decentralized gait control can be significantly ameliorated by modifying its interaction between the control system and its body system to be implemented.