C4.5: Programs for Machine Learning (書評)

When I started out as a newly hatched PhD student, back in the day, one of the first articles I read and understood (or at least thought that I understood) was Ray Reiter’s classic article on default logic (Reiter, 1980).This was some years after the famous ‘non-monotonic logic’ issue of Artificial Intelligence in which that article appeared, but default logic was still one of the leading approaches, a tribute to the simplicity and power of the theory. As a result of reading the article, I became fascinated by both default logic and, more generally, non-monotonic logics. However, despite my fascination, these approaches never seemed terribly useful for the kinds of problem that I was supposed to be studying—problems like those in medical decision making—and so I eventually lost interest. In fact non-monotonic logics seemed to me, and to many people at the time I think, not to be terribly useful for anything. They were interesting, and clearly relevant to the long-term goals of Artificial Intelligence as a discipline, but not of any immediate practical importance. This verdict, delivered at the end of the 1980s, continued, I think, to be true for the next few years while researchers working in non-monotonic logics studied problems that to outsiders seemed to be ever more obscure. However, by the end of the 1990s, it was becoming clear, even to folk as short-sighted as I, that non-monotonic logics were getting to the point at which they could be used to solve practical problems. Knowledge in action shows quite how far these techniques have come. The reason that non-monotonic logics were invented was, of course, in order to use logic to reason about the world. Our knowledge of the world is typically incomplete, and so, in order to reason about it, one has to make assumptions about things one does not know. This, in turn, requires mechanisms for both making assumptions and then retracting them if and when they turn out not to be true. Non-monotonic logics are intended to handle this kind of assumption making and retracting, providing a mechanism that has the clean semantics of logic, but which has a non-monotonic set of conclusions. Much of the early work on non-monotonic logics was concerned with theoretical reasoning, that is reasoning about the beliefs of an agent—what the agent believes to be true. Theoretical reasoning is the domain of all those famous examples like ‘Typically birds fly. Tweety is a bird, so does Tweety fly?’, and the fact that so much of non-monotonic reasoning seemed to focus on theoretical reasoning was why I lost interest in it. I became much more concerned with practical reasoning—that is reasoning about what an agent should do—and non-monotonic reasoning seemed to me to have nothing interesting to say about practical reasoning. Of course I was wrong. When one tries to formulate any kind of description of the world as the basis for planning, one immediately runs into applications of non-monotonic logics, for example in keeping track of the state of a changing world. It is this use of non-monotonic logic that is at the heart of Knowledge in action. Building on the McCarthy’s situation calculus, Knowledge in action constructs a theory of action that encompasses a very large part of what an agent requires to reason about the world. As Reiter says in the final chapter,

Knowledge in Action: Logical Foundations for Specifying and Implementing Dynamical Systems

The paper is a overview of the major qualitative spatial representation and reasoning techniques. We survey the main aspects of the representation of qualitative knowledge including ontological aspects, topology, distance, orientation and shape. We also consider qualitative spatial reasoning including reasoning about spatial change. Finally there is a discussion of theoretical results and a glimpse of future work. The paper is a revised and condensed version of [33,34].

Qualitative Spatial Representation and Reasoning: An Overview

Scheduling, vehicle routing and timetabling are all examples of constraint problems, and methods to solve them rely on the idea of constraint propagation and search. This book meets the need for a modern, multidisciplinary introduction to the field that covers foundations and applications. Written by Krzysztof Apt, an authority on the subject, it will be welcomed by graduate students and professionals. With the insertion of constraint techniques into programming environments, new developments have accelerated the solution process. Constraint programming combines ideas from artificial intelligence, programming languages, databases, and operational research.

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Principles of constraint programming

Emotions have an important role in daily life, not only in human interaction, but also in decision-making processes, and in the perception of the world around us. Due to the recent interest shown by the research community in establishing emotional interactions between humans and computers, the identification of the emotional state of the former became a need. This can be achieved through multiple measures, such as subjective self-reports, autonomic and neurophysiological measurements. In the last years, Electroencephalography (EEG) received considerable attention from researchers, since it can provide a simple, cheap, portable, and ease-to-use solution for identifying emotions. In this paper, we present a survey of the neurophysiological research performed from 2009 to 2016, providing a comprehensive overview of the existing works in emotion recognition using EEG signals. We focus our analysis in the main aspects involved in the recognition process (e.g., subjects, features extracted, classifiers), and compare the works per them. From this analysis, we propose a set of good practice recommendations that researchers must follow to achieve reproducible, replicable, well-validated and high-quality results. We intend this survey to be useful for the research community working on emotion recognition through EEG signals, and in particular for those entering this field of research, since it offers a structured starting point.

Emotions Recognition Using EEG Signals: A Survey

Electroencephalograph (EEG) signals associated with motor imagery (MI) are highly non-Gaussian, non-stationary and have non- linear characteristics. Bispectral analysis is an advanced signal processing technique that quantifies quadratic non-linearities (phase-coupling) among the components of a signal and holds promise for characterizing MI-related EEG. Studies have been reported on the applicability of bispectrum for MI classification; often with different choice of high order spectra features. Question remains as to which of the different features of non-linear interactions over frequency components are best suited for MI classification. In this paper, an analysis based on bispectrum is reported to extract multiple high order spectra features of EEG for MI classification. MI signals from C3 and C4 channels for two tasks are used in the analysis. Based on bispectrum analysis, four high order spectra features are extracted. The classification results indicate that the extracted features could differentiate the two MI tasks with an accuracy of 90±4.71%.

Multiple Feature Extraction of Electroencephalograph Signal for Motor Imagery Classification through Bispectral Analysis

Object affordances provide useful information related to understanding of human activities. The aim of this paper is to create an ontology for object affordance reasoning that can be shared across different assistive robots operating within the household domain. A novel ontology called O-PrO (Object Property Ontology) consisting of 61 household objects is presented. The ontology can be used for computing cognitive and semantic object affordances.

O - PrO: An Ontology for Object Affordance Reasoning

In this paper we present a collaborative control architecture for a robotic wheelchair with the aim of providing “assistance as required”. The architecture is based on cBDI - an extension to the Belief-Desire-Intention model to support human-machine collaboration. We present results of an evaluation of the architecture in a simulated environment and conclude that collaborative control could ensure “feeling in control” even under assistance.

cBDI-based Collaborative Control for a Robotic Wheelchair

In this paper, we present a methodology to classify grasp types based on two channel forearm electromyogram signals. Six grasp types are identified. Classification is through support vector machine using radial basis function kernel based on sum of wavelet decomposition coefficients of the electromyogram signals. In a study involving six subjects, we achieved an average recognition rate of 86%; better than that reported in the liteature.

Classification of Grasp Types through Wavelet Decomposition of EMG Signals

This paper explores the use of bispectrum analysis of electroencephalogram (EEG) signals in estimation of imagination and observation of hand movements. Five different tasks were observed and subsequently performed in separate runs of 10 trials. Subjects were asked to observe and subsequently perform task on the presentation of an audio cue (both at the start and end of session). The acquired EEG signals after averaging in time domain were analyzed using bispectrum. The result shows bispectrum analysis of EEG signals provide a way to discriminate mental representation during observation and imagination of hand movement. Our preliminary investigation, have shown the ability of bispectrum in detecting non-linear phase coupling between alpha and beta rhythms during observation and imagination of hand movement. Further, different bispectral peaks (for tasks with and without prior visual representation) have reinforced our belief that visual representation of motor acts make difference during motor imagination.

Shyamanta M. Hazarika

Papers

Multiple Feature Extraction of Electroencephalograph Signal for Motor Imagery Classification through Bispectral Analysis

O - PrO: An Ontology for Object Affordance Reasoning

cBDI-based Collaborative Control for a Robotic Wheelchair

Classification of Grasp Types through Wavelet Decomposition of EMG Signals

Bispectrum analysis of EEG during observation and imagination of hand movement