The need for representing and manipulating complex knowledge arising in Arti?cial Intel-ligence and in other emerging areas, like Knowledge Management and Information Inte-gration (see also Section 8), has renewed the interest in advanced logic-based formalisms for Knowledge Representation and Reasoning (KR&R), which started in the early 1980s. knowledge in a more limited way, so that the reasoning is more amenable to pro-cedural control; among the important concepts covered there we ?nd rule-based production systems. Chapters 8 through 10 deal with a more object-oriented ap-proach to Knowledge Representation and the taxonomic reasoning that goes with it. There is a familiar pattern in knowledge representation research in which the description of a new knowledge representation technology is followed by claims that the new ideas are in fact formally equivalent to an existing technology. Historically the claim has often been phrased in terms of equivalence to logic. Don't show me this again. Welcome! This is one of over 2,200 courses on OCW. Find materials for this course in the pages linked along the left. MIT OpenCourseWare is a free & open publication of material from thousands of MIT courses, covering the entire MIT curriculum.. No enrollment or registration. Knowledge Representation and Reasoning (KRR). In writing this survey it came as a pleasant surprise to us to see how much our fellow Greeks have accomplished in the past few years. Ranging from core KRR topics like Non-Monotonic Reasoning, Epistemic Logics, Belief Revision, and Reasoning Knowledge Representation and Reasoning University - A free PowerPoint PPT presentation (displayed as a Flash slide show) on PowerShow.com - id: 3c7737-ZmUwM 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. Knowledge Representation and Reasoning (KR&R) is 'the area of Arti?cial Intelli-gence concerned with how knowledge can be represented symbolically and manipulated in an automated way by reasoning programs' [Brachman & Levesque 2004]. Knowledge is represented symbolically by means of a knowledge representation. According to [Davis, dai.fmph.uniba.sk Controlled natural languages (CNLs) are subsets of natural languages that are obtained by restricting the grammar and vocabulary in order to reduce or eliminate ambiguity and complexity. Traditionally, controlled languages fall into two major types: those that improve readability for human readers (e.g. non-native speakers), and those that enable reliable automatic semantic analysis of the Knowledge representation (see Knowledge Representation) and reasoning plays a central role in Artificial Intelligence. Research in Artificial Intelligence (henceforth AI) started off by trying to identify the general mechanisms responsible for intelligent behavior. However, it quickly became obvious that general and powerful methods are not KNOWLEDGE REPRESENTATION AND REASONING 5. circumscriptive event calculus) in which a robot constructs a model of the world through an abductive process whereby sensor data is explained by hypothesizing the existence, lo-cations, and shapes of object