Aitzol Astigarraga

On the use of Bayesian Networks to develop behaviours for mobile robots

Bayesian Networks are models which capture uncertainties in terms of probabilities that can be used to perform reasoning under uncertainty. This paper presents an attempt to use Bayesian Networks as a learning technique to manage task execution in mobile robotics. To learn the Bayesian Network structure from data, the K2 structural learning algorithm is used, combined with three different net evaluation metrics. The experiment led to a new hybrid multiclassifying system resulting from the combination of 1-NN with the Bayesian Network, that allows one to use the power of the Bayesian Network while avoiding the computational burden of the reasoning mechanism - the so-called evidence propagation process. As an application example we present an approach of the presented paradigm to implement a door-crossing behaviour in a mobile robot using only sonar readings, in an environment with smooth walls and doors. Both the performance of the learning mechanism and the experiments run in the real robot-environment system show that Bayesian Networks are valuable learning mechanisms, able to deal with the uncertainty and variability inherent to such systems.

Classifier hierarchy learning by means of genetic algorithms

Classifier combination falls in the so called data mining area. Its aim is to combine some paradigms from the supervised classification - sometimes with a previous non-supervised data division phase - in order to improve the individual accuracy of the component classifiers. Formation of classifier hierarchies is an alternative among the several methods of classifier combination. In this paper we present a novel method to find good hierarchies of classifiers for given databases. In this new proposal, a search is performed by means of genetic algorithms, returning the best individual according to the classification accuracy over the dataset, estimated through 10-fold cross-validation. Experiments have been carried out over 14 databases from the UCI repository, showing an improvement in the performance compared to the single classifiers. Moreover, similar or better results than other approaches, such as decision tree bagging and boosting, have been obtained.

Humanizing NAO robot teleoperation using ROS

The work presented here proposes two different ROS packages to enrich the teleoperation of the robot NAO: speech-based teleoperation (in Basque) and gesture-based teleoperation together with arm control. These packages have been used and evaluated in a human mimicking experiment. The tools offered can serve as a base for many applications.

ZeuScansion: A tool for scansion of English poetry

We present a finite state technology based system capable of performing metrical scansion of verse written in English. Scansion is the traditional task of analyzing the lines of a poem, marking the stressed and non-stressed elements, and dividing the line into metrical feet. The system’s workflow is composed of several subtasks designed around finite state machines that analyze verse by performing tokenization, part of speech tagging, stress placement, and unknown word stress pattern guessing. The scanner also classifies its input according to the predominant type of metrical foot found. We also present a brief evaluation of the system using a gold standard corpus of human-scanned verse, on which a per-syllable accuracy of 86.78% is reached. The program uses open-source components and is released under the GNU GPL license.

Bertsobot: The first minstrel robot

We describe a robot capable of composing and playing traditional Basque impromptu verses - bertsoak. The system, called Bertsobot, is able to construct improvised verses according to given constraints on rhyme and meter, and to perform it in public. Towards this end, several tools and applications have been developed and integrated in Bertsobot, including: speech-based communication system, text applications for verse generation, and robot behaviours to interact with the environment in a public performance. We describe the tools and processes behind our approach, present some early experimental results and illustrative verses, and finally, remark the conclusions and future steps.

Textual Coherence in a Verse-Maker Robot

The Bertsobot project aims to develop an autonomous robot capable of composing and playing traditional Basque impromptu verses –bertsoak. The system should be able to construct novel verses according to given constraints on rhyme and meter, and to perform it in public. The Bertsobot project, at the intersection of Autonomous Robotics, Natural Language Generation and Human Robot Interaction, works to model the human abilities that collaborate in the process that enables a verse-maker to produce impromptu verses. This paper provides a general overview of the system, specially focusing on the description and evaluation of different semantic similarity methods for predicting the textual coherence of the generated verses.

User Adapted Motor-Imaginary Brain-Computer Interface by means of EEG Channel Selection Based on Estimation of Distributed Algorithms

Brain-Computer Interfaces (BCIs) have become a research field with interesting applications, and it can be inferred from published papers that different persons activate different parts of the brain to perform the same action. This paper presents a personalized interface design method, for electroencephalogram- (EEG-) based BCIs, based on channel selection. We describe a novel two-step method in which firstly a computationally inexpensive greedy algorithm finds an adequate search range; and, then, an Estimation of Distribution Algorithm (EDA) is applied in the reduced range to obtain the optimal channel subset. The use of the EDA allows us to select the most interacting channels subset, removing the irrelevant and noisy ones, thus selecting the most discriminative subset of channels for each user improving accuracy. The method is tested on the IIIa dataset from the BCI competition III. Experimental results show that the resulting channel subset is consistent with motor-imaginary-related neurophysiological principles and, on the other hand, optimizes performance reducing the number of channels.

Singing minstrel robots, a means for improving social behaviors

Bertsolaritza, Basque improvised contest poetry, offers another sphere to develop robot body language and robot communication capabilities, that shares some similarities with theatrical performances. It is also a new area to work on social robotics. The work presented in this paper makes some steps forward in designing and implementing the set of behaviors the robots need to show in the stage to increase, on the one hand robot autonomy and on the other hand, credibility and sociability.

Combining bayesian networks, k nearest neighbours algorithm and attribute selection for gene expression data analysis

In the last years, there has been a large growth in gene expression profiling technologies, which are expected to provide insight into cancer related cellular processes Machine Learning algorithms, which are extensively applied in many areas of the real world, are not still popular in the Bioinformatics community We report on the successful application of the combination of two supervised Machine Learning methods, Bayesian Networks and k Nearest Neighbours algorithms, to cancer class prediction problems in three DNA microarray datasets of huge dimensionality (Colon, Leukemia and NCI-60) The essential gene selection process in microarray domains is performed by a sequential search engine and after used for the Bayesian Network model learning Once the genes are selected for the Bayesian Network paradigm, we combine this paradigm with the well known K NN algorithm in order to improve the classification accuracy.

Emotional Poetry Generation

In this article we describe a new system for the automatic creation of poetry in Basque that not only generates novel poems, but also creates them conveying a certain attitude or state of mind. A poem is a text structured according to predefined formal rules, whose parts are semantically related and with an intended message, aiming to elicit an emotional response. The proposed system receives as an input the topic of the poem and the affective state (positive, neutral or negative) and tries to give as output a novel poem that: (1) satisfies formal constraints of rhyme and metric, (2) shows coherent content related to the given topic, and (3) expresses them through the predetermined mood. Although the presented system creates poems in Basque, it is highly modular and easily extendable to new languages.