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

Corticomuscular Coherence (CMC) reflects the functional coupling between the neural activity of the cortical areas in the brain and the associated muscles. For a Brain-Computer Interface (BCI), the neural activities could be picked up by electrophysiological recordings from the brain, such as the electroencephalogram (EEG). The electromyogram (EMG) records the electrical activity of the muscles. This paper introduces a novel approach of estimating the CMC based on Kernel Partial Least Square (KPLS), a propitious regression method for handling non-linear systems. The proposed KPLS-CMC, an alternative method to compute CMC, that distinguishes an optimal set of features to characterize the CMC with regard to KPLS based relevance scores was examined on six healthy individual to gauge its performance in the form of correlation coefficient. The KPLS-CMC coefficient could be an effective substitute to drive a BCI system, as a corticomuscular feature.

Estimating Corticomuscular Coherence using Kernel Partial Least Square

The paper describes an abductive framework for mining spatio-temporal patterns. The spatial representation language RCC-8, often referred to as Region Connection Calculus and its spatio-temporal extension, ST0, a fragment of Prepositional Spatio-temporal Logic is used as the knowledge representation formalism. Abductive reasoning may yield more than one possible answer and is accompanied by some preference criteria. Here, the abduction technique is circumscription which implements the heuristic that changes should only occur when forced to.

Pattern Mining as Abduction from Snapshots to Spatio-temporal Patterns

Object affordance, a characterization of the different functionalities of an object, refers to an object's numerous possibilities of interaction. It has a significant part to play in priming motoric actions which depends on the actor's spontaneous neurological behaviour. Action Observation (AO) and Motor Imagery (MI) also lead to the stimulation of motor system. In fact, AO and MI result in activation of overlapping brain areas as the actual motor task. AO combined with MI (referred to as AO+MI) initiates higher cortical activity in comparison with either MI or AO alone. In this paper, we investigate the influence of affordance driven motor priming during AO, MI and AO + MI. Source current density as an EEG parameter is estimated by Low Resolution Electromagnetic Tomography (LORETA). Our results demonstrate that affordance driven motor priming during AO+MI indicates stronger electrophysiological and behavioural changes. This is evident from the N2 ERP component. Further, the current source density (in brain regions associated with motor planning) during affordance driven AO+MI is found to be maximum.

Neural Correlates of Motor Imagery during Action Observation in Affordance-based Actions: Preliminary Results

This paper presents a bionic reflex control strategy for a kinematically constrained robotic finger. Here, the bionic reflex is achieved through a force tracking impedance control strategy. The dynamic model of the finger is reduced subject to kinematic constraints. Thereafter, an impedance control strategy that allows exact tracking of forces is discussed. Simulation results for a single finger holding a rectangular object against a flat surface are presented. Bionic reflex response time is of the order of milliseconds.

Bionic Reflex Control Strategy for Robotic Finger with Kinematic Constraints

The focus of this paper is towards development of a logical framework for mining spatio-temporal sequential patterns. The spatial representation language RCC-8, often referred to as region connection calculus and its spatio-temporal extension, ST0, a fragment of prepositional spatio-temporal logic is used as the knowledge representation formalism. Standard abduction is used for mining sequential patterns in spatio-temporal data. Abductive reasoning may yield more than one possible answer and is accompanied by some preference criteria expressed using heuristics. Here, the abduction technique is circumscription which implements the heuristic that changes should only occur when forced to. Various additional heuristics to drive the selection of preferred explanations are discussed.

Shyamanta M. Hazarika

Papers

Estimating Corticomuscular Coherence using Kernel Partial Least Square

Pattern Mining as Abduction from Snapshots to Spatio-temporal Patterns

Neural Correlates of Motor Imagery during Action Observation in Affordance-based Actions: Preliminary Results

Bionic Reflex Control Strategy for Robotic Finger with Kinematic Constraints

Pattern Mining as Abduction: From Snapshots to Spatio-Temporal Sequential Patterns