[서평]「Component Software」 - Beyond Object-Oriented Programming -

Knowledge representation is at the very core of a radical idea for understanding intelligence. Instead of trying to understand or build brains from the bottom up, its goal is to understand and build intelligent behavior from the top down, putting the focus on what an agent needs to know in order to behave intelligently, how this knowledge can be represented symbolically, and how automated reasoning procedures can make this knowledge available as needed. 

This landmark text takes the central concepts of knowledge representation developed over the last 50 years and illustrates them in a lucid and compelling way. Each of the various styles of representation is presented in a simple and intuitive form, and the basics of reasoning with that representation are explained in detail. This approach gives readers a solid foundation for understanding the more advanced work found in the research literature. The presentation is clear enough to be accessible to a broad audience, including researchers and practitioners in database management, information retrieval, and object-oriented systems as well as artificial intelligence. This book provides the foundation in knowledge representation and reasoning that every AI practitioner needs.

*Authors are well-recognized experts in the field who have applied the techniques to real-world problems 
* Presents the core ideas of KR&R in a simple straight forward approach, independent of the quirks of research systems 
*Offers the first true synthesis of the field in over a decade

Table of Contents

1 Introduction * 2 The Language of First-Order Logic *3 Expressing Knowledge * 4 Resolution * 5 Horn Logic * 6 Procedural Control of Reasoning * 7 Rules in Production Systems * 8 Object-Oriented Representation * 9 Structured Descriptions * 10 Inheritance * 11 Numerical Uncertainty *12 Defaults *13 Abductive Reasoning *14 Actions * 15 Planning *16 A Knowledge Representation Tradeoff * Bibliography * Index

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Knowledge Representation and Reasoning

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

Multiagent Systems is the title of a collection of papers dedicated to surveying specific themes of Multiagent Systems (MAS) and Distributed Artificial Intelligence (DAI). All of them authored by leading researchers of this dynamic multidisciplinary field.

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Multiagent systems: a modern approach to distributed artificial intelligence

Systems, apparatuses, and methods estimate the distance between a player and a ball by transmitting a chirp (sweep signal) to a radio tag located on the ball. During the chirp, the frequency of the transmitted signal is changed in a predetermined fashion. The radio tag doubles the transmitted frequency and returns the processed signal to a transceiver typically located on the player. The currently transmitted frequency is then compared with the received frequency to obtain a difference frequency from which an apparatus may estimate the distance. The apparatus may simultaneously receive the processed signal from the radio tag while transmitting the sweep signal.

Athletic Performance Monitoring Systems and Methods in a Team Sports Environment

Logic-based robot control in highly dynamic domains

The idea of component-based software engineering was proposed more that 40 years ago, yet only few robotics software frameworks follow these ideas. The main problem with robotics software usually is that it runs on a particular platform and transferring source code to another platform is crucial. In this paper, we present our software framework Fawkes which follows the component-based software design paradigm by featuring a clear component concept with well-defined communication interfaces. We deployed Fawkes on several different robot platforms ranging from service robots to biped soccer robots. Following the component concept with clearly defined communication interfaces shows great benefit when porting robot software from one robot to the other. Fawkes comes with a number of useful plugins for tasks like timing, logging, data visualization, software configuration, and even high-level decision making. These make it particularly easy to create and to debug productive code, shortening the typical development cycle for robot software.

Design principles of the component-based robot software framework Fawkes

RGB-D sensors such as the Microsoft Kinect or the Asus Xtion are inexpensive 3D sensors. A depth image is computed by calculating the distortion of a known infrared light (IR) pattern which is projected into the scene. While these sensors are great devices they have some limitations. The distance they can measure is limited and they suffer from reflection problems on transparent, shiny, or very matte and absorbing objects. If more than one RGB-D camera is used the IR patterns interfere with each other. This results in a massive loss of depth information. In this paper, we present a simple and powerful method to overcome these problems. We propose a stereo RGB-D camera system which uses the pros of RGB-D cameras and combine them with the pros of stereo camera systems. The idea is to utilize the IR images of each two sensors as a stereo pair to generate a depth map. The IR patterns emitted by IR projectors are exploited here to enhance the dense stereo matching even if the observed objects or surfaces are texture-less or transparent. The resulting disparity map is then fused with the depth map offered by the RGB-D sensor to fill the regions and the holes that appear because of interference, or due to transparent or reflective objects. Our results show that the density of depth information is increased especially for transparent, shiny or matte objects.

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IR Stereo Kinect: Improving Depth Images by Combining Structured Light with IR Stereo

Robotic soccer provides an interesting and nontrivial testbed for many aspects of mobile robotics. From a high-level decision making point of view, central issues are how to get the robots to choose intelligently among various possible courses of actions and how to get them to coordinate their actions with other members of the team. In this paper we report on our efforts to address these issues within the framework of the Golog action language.

Using Golog for Deliberation and Team Coordination in Robotic Soccer.

DTGolog was proposed by Boutilier et al. as an integration of decision-theoretic (DT) planning and the programming language Golog. Advantages include the ability to handle large state spaces and to limit the search space during planning with explicit programming. Soutchanski developed a version of DTGolog, where a program is executed on-line and DT planning can be applied to parts of a program only. One of the limitations is that DT planning generally cannot be applied to programs containing sensing actions. In order to deal with robotic scenarios in unpredictable domains, where certain kinds of sensing like measuring one’s own position are ubiquitous, we propose a strategy where sensing during deliberation is replaced by suitable models like computed trajectories so that DT planning remains applicable. In the paper we discuss the necessary changes to DTGolog entailed by this strategy and an application of our approach in the RoboCup domain.

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Alexander Ferrein

Papers

Logic-based robot control in highly dynamic domains

Design principles of the component-based robot software framework Fawkes

IR Stereo Kinect: Improving Depth Images by Combining Structured Light with IR Stereo

Using Golog for Deliberation and Team Coordination in Robotic Soccer.

On-Line Decision-Theoretic Golog for Unpredictable Domains