Abstract
One key point in the design and implementation of intelligent systems is the process of building knowledge bases. As most of the research in the AI field has moved from the construction of general purpose problem solvers to building knowledge-based systems that deal with problems restricted to a particular domain, many techniques have been proposed to acquisition and representation of knowledge bases. This paper presents an overview of these techniques and describes several aspects related to building knowledge bases and how they can affect the overall implementation of intelligent systems.
Introduction
Before the presentation of the techniques and characteristics of building knowledge bases, it is necessary to establish the groundwork for that by first defining knowledge and how it differs from data and information.
In order to clearly distinguish these concepts, they can be thought as part of a hierarchy where data is the base of the pyramid, followed by information, knowledge and wisdom the top (Tuthill, 1990). Data consists of raw facts that has no useful meaning or has little application until they are interrelated and processed to generate what we call information. For example, a file can store a sequence of names and dates (data) which has no meaning until they are related to company X and represent the employees of X (information). Furthermore, the stored data become information when they can be processed to generate a meaningful output to a community of users.
When the information is synthesized it is called knowledge and it is considered in a higher level in the hierarchy just described. In other words, knowledge is a collection of facts, relationships and behavior of objects in a model represented ...
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Abstract: In the future, intelligent machines will replace or enhance human capabilities in many areas. Artificial intelligence is the intelligence exhibited by machines or software. It is the subfield of computer science. Artificial Intelligence is becoming a popular field in computer science as it has enhanced the human life in many areas. Artificial intelligence in the last two decades has greatly improved performance of the manufacturing and service systems. Study in the area of artificial intelligence has given rise to the rapidly growing technology known as expert system. Application areas of Artificial Intelligence is having a huge impact on various fields of life as expert system is widely used these days to solve the complex problems
How we approach the question of knowledge is pivotal. If the definition of knowledge is a necessary truth, then we should aim for a real definition for theoretical and practical knowledge. Methodology examines the purpose for the definition and how we arrived to it. The reader is now aware of the various ways to dissect what knowledge is. This entails the possibility of knowledge being a set of truths; from which it follows that one cannot possibly give a single definition. The definition given must therefore satisfy certain desiderata , while being strong enough to demonstrate clarity without losing the reader. If we base our definition on every counter-example that disproves our original definition then it becomes ad hoc. This is the case for our current defini...
Crevier, D. (1999). AI: The tumultuous history of the search for Artificial Intelligence. Basic Books: New York.
CYC is a very large, multi-contextual knowledge base and inference engine. The development of CYC was started at the Microelectronics and Computer Technology Corporation(MCC) during the early 1980s and continued at Cycorp, Inc. On January 1, 1995 at Austin, Texas. Doug Lenat, the former head of the CYC Project at MCC and the president of Cycorp at present, has lead the development of the CYC project from the beginning. The goal of the Cyc project is to break the software brittleness bottleneck once and for all by constructing a foundation of basic common sense knowledge system and semantic substratum of terms, rules, and relations that will enable a variety of knowledge-intensive products and services. T...
CHAPTER TWO KNOWLEDGE ACQUISITION AND REPRESENTATION 2.1 Overview This Chapter presents a description of Knowledge Acquisition and Knowledge Representation in Expert Systems. This chapter will explain the knowledge acquisition and representation methods, production rules and frames. The section below explains the concept of production rules, their training and learning. Also, this chapter discusses methods of Knowledge Acquisition and Knowledge Representation.
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There are expert systems that can solve complex problems that humans train their whole lives for. In 1997, IBM's Deep Blue defeated the world champion in a game of chess (Karlgaard, p43). Expert systems design buildings, configure airplanes, and diagnose breathing problems. NASA's Deep Space One probe left with software that lets the probe diagnose problems and fix itself (Lyons).
The approach to artificial intelligence should be proceeded with caution. Throughout recent years and even decades before, it has been a technological dream to produce artificial intelligence. From movies, pop culture, and recent technological advancements, there is an obsession with robotics and their ability to perform actions that require human intelligence. Artificial intelligence has become a real and approachable realization today, but should be approached with care and diligence. Humans can create advanced artificial intelligence but should not because of the harm they may cause, the monumental advancement needed in the technology, and that its harm outweighs its benefits.
unified because reasoning and problem solving may involve several areas simultaneously. A robot circuitrepair syste m, for instance, needs to reason about circuits in terms of electrical connectivity and physical layout, and about time both for circuit timing analysis and estimating labor costs. The sentences describing time therefore must be capable of being combined w ith those describing spatial layout, and must work equally well for nanoseconds and minutes, and for angstroms and meters. After we present the general ontology, we will apply it to write sentences describing the domain of grocery shopping. A brief reverie on the subject of shopping brings to mind a vast array of topics in need of representation: locations, movement, physical objects, shapes, sizes, grasping, releasing, colors, categories of objects, anchovies, amounts of stuff, nutrition, cooking, nonstick frying pans, taste, time, money, direct debit cards, arithmetic, economics, and so on. The domain is more than adequate to exercise our ontology, and leaves plenty of scope for the reader to do some creative knowledge representation of his or her own. 228 Chapter 8. Building a Knowledge Base Our discussion of the
‘… To obtain something resembling a scientific handle on the concept of information we need to begin with a clear picture of what we are observing. Physics is concerned with physical bodies of all kinds, their properties and their behaviour. We do not have to define the concept of a body in so many words because we can show a person so many concrete examples that he can learn to use the word ‘body’ as competently as we do ourselves. Similarly, we can start our exploration of information by using the concept of a sign. We might tell someone that a sign is any physical object, event, or property of an object or event which can stand for something else. But we do not leave it at that. We show them hundreds of diverse examples until they know what a sign is by ostensive definition (that is, by demonstration). In this way we escape the tyranny of a verbal regression into the domain of practical, concrete action.
Artificial Intelligence is the scientific theory to advance the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines. This is going to hold the key in the future. It has always fa...
Artificial Intelligence “is the ability of a human-made machine to emulate or simulate human methods for the deductive and inductive acquisition and application of knowledge and reason” (Bock, 182). The early years of artificial intelligence were seen through robots as they exemplified the advances and potential, while today AI has been integrated society through technology. The beginning of the thought of artificial intelligence happened concurrently with the rise of computers and the dotcom boom. For many, the utilization of computers in the world was the most advanced role they could ever see machines taking. However, life has drastically changed from the 1950s. This essay will explore the history of artificial intelligence, discuss the