A selectionist mechanism for autonomous behavior acquisition

The paper explores a selectionist approach for the autonomous acquisition of new behavior by a robot as it is attempting to stay viable in an ecosystem in which there are increasing pressures. The approach is based on a random exploration and subsequent selection of possible couplings between sensory quantities, actuator quantities, and internal motivational states. The paper gives some examples of behavioral acquisition from simulation experiments.     

Magnetic-interference-free dual-electric compass

The purpose of this research is to improve the reliability of automobile navigation system that utilizes the electric compass for localization. On account of its sensitivity to external interferences of the magnetic field, the electric compass itself is not accurate enough to be used for localization compared with the rate gyroscope. To overcome this shortcoming, in this research, a robust electric compass was designed by using two electric compasses to efficiently cancel out the low-frequency interferences. That is, in this paper, a dual-compass predictive calibration algorithm, which corrects irregular and long-lasting magnetic-field interferences, is newly proposed and implemented. When the external interferences are eliminated from the dual-electric compass, it becomes much more accurate than the gyroscope-based system that suffers from accumulative drift error. The reliability and performance of the designed system were verified through real navigation experiments.     

A study on the optimal path of a mobile robot based on mobility and recurrency

In this article, we define mobility and recurrency, and acquire the desired mobility and recurrency which result from the dynamic space change between two wheels of a mobile robot, assuming that the robot has a fixed wheel radius. When a robot has a fixed interval between its two wheels, it cannot avoid a sudden, obstacle because of the constraints of mobility and recurrency. The focus of this article is on instant recurrency, but high, stable mobility. That is, by changing the space between the two wheels in the simulation, the mobile robot could gain high recurrency instantly, and also high mobility with stability. We supposed that mobility and recurrency, as defined in this article, are applied to the design of a mobile robot. We suggest a theoretical change based on the optimal design of a mobile robot. The experimental data support the validity of this definition of mobility and recurrency. This work was presented, in part, at the Sixth International Symposiump onArtificial Life and Robotics, Tokyo, Japan, January 15–17, 2001.     

Towards new forms of knowledge communication: the adaptive dimension of a web-based learning environment

Adaptive Educational Hypermedia Systems aim to increase the functionality of hypermedia by making it personalised to individual learners. The adaptive dimension of these systems mainly supports knowledge communication between the system and the learner by adapting the content or the appearance of hypermedia to the knowledge level, goals and other characteristics of each learner. The main objectives are to protect learners from cognitive overload and disorientation by supporting them to find the most relevant content and path in the hyperspace. In the approach presented in this paper, learners' knowledge level and individual traits are used as valuable information to represent learners' current state and personalise the educational system accordingly, in order to facilitate learners to achieve their personal learning goals and objectives. Learners' knowledge level is approached through a qualitative model of the level of performance that learners exhibit with respect to the concepts they study and is used to adapt the lesson contents and the navigation support. Learners' individual traits and especially their learning style represent the way learners perceive and process information, and are exploited to adapt the presentation of the educational material of a lesson. The proposed approach has been implemented through various adaptation technologies and incorporated into a prototype hypermedia system. Finally, a pilot study has been conducted to investigate system's educational effectiveness.     

An Agenda- and Justification-Based Framework for Discovery Systems

We propose and evaluate an agenda- and justification-based architecture for discovery systems that selects the next tasks to perform, as well as heuristics for use in discovery systems. This framework has many desirable properties: (1) it selects its own tasks to perform based upon how plausible they are judged to be; (2) it facilitates the encoding of general discovery strategies using a variety of background knowledge; and (3) it tailors its behavior toward a users interests. Many experiments with a prototype discovery program called HAMB demonstrate that both reasons and estimates of interestingness contribute to performance in the domains of protein crystallization and patient rehabilitation data. The programs heuristics provide good initial solutions to problems encountered when implementing fully autonomous discovery systems.