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.

/pdf/principles-of-constraint-programming-46i5k65pcd.pdf

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

Firewalls are the frontier defense in network security. Firewalls provide a set of rules that identify how to handle individual data packets arriving at the network. Firewall configuration is increasingly becoming difficult. Filter properties called anomalies hint at possible conflicts between rules. An argumentation framework could provide ways of handling such conflicts. Verification of a firewall involve finding out whether anomalies exist or not. Reconfiguration involves removing critical anomalies discovered in the verification phase. In this paper, we show how a Defeasible Logic Programming approach with an underlying argumentation based semantics could be applied for verification and reconfiguration of a firewall.

/pdf/an-application-of-defeasible-logic-programming-for-firewall-1gm3kalm95.pdf

An Application of Defeasible Logic Programming for Firewall Verification and Reconfiguration

The work described in this paper stems from the Cognitive Wheelchair Project - an effort to build a cognitively enhanced collaborative control architecture for an intelligent wheelchair. A number of challenges arises when developing such a system including ensuring indiscernibility of assistance provided by the system i.e., user unable to realize so easily that he is getting help. In this paper, our focus is primarily on design of such a reactive navigator for collaborative control of an intelligent wheelchair. Under conditions attuned to replicate the scenarios available to the wheelchair, we conducted a series of maze solving experiments. A set of design elements were extracted from the wayfinding experiment leading to the finite state machine (FSM) characterizing the reactive navigator. The FSM arrived at through such an exercise is expected to emulate the cognitive processes of human wayfinding under environment conditions as perceivable to an intelligent wheelchair and ensure indiscernibility of assistance.

Solving a Maze: Experimental Exploration on Wayfinding Behavior for Cognitively Enhanced Collaborative Control

Bispectrum analysis is presented to analyze electroencephalogram( EEG) signals recorded during two states of motor acts i.e. during imagination and observation of hand movements. EEG signals are recorded from primary hand areas i.e. from electrode position C3 and C4. This paper emphasizes the nonlinear behavior of EEG signal and we figure out, by bispectrum analysis it is possible to estimate spontaneous rhythm in the EEG during imagination and observation of hand movements. The results show that the location of bispectral peaks in bifrequency are quite different depending on the EEG signals in different motor acts.

Bispectrum Analysis of EEG in Estimation of Hand Movement

The focus of this paper is on the development of a prosthetic finger prototype following a biomimetic approach. Electromyogram signals are used to actuate the prototype performing flexion-extension and abduction-adduction. Finger joints' torque are transmitted through an antagonastic tendon system. The prototype replicates the dimensions and joint range motion of the human finger.

Development of A Biomimetic Prosthetic Finger

This paper presents a framework for automatically learning rules of a simple game of cards using data from a vision system observing the game being played. Incremental learning of object and protocol models from video, for use by an artificial cognitive agent, is presented. iLearn--a novel algorithm for inducing univariate decision trees for symbolic datasets is introduced. iLearn builds the decision tree in an incremental way allowing automatic learning of rules of the game.

Shyamanta M. Hazarika

Papers

An Application of Defeasible Logic Programming for Firewall Verification and Reconfiguration

Solving a Maze: Experimental Exploration on Wayfinding Behavior for Cognitively Enhanced Collaborative Control

Bispectrum Analysis of EEG in Estimation of Hand Movement

Development of A Biomimetic Prosthetic Finger

Learning rules of a card game from video