Open AccessBook
Knowledge Representation and Reasoning
TLDR
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, and offers the first true synthesis of the field in over a decade.Abstract:
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 * Indexread more
Citations
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Journal ArticleDOI
Learning Bayesian Networks: The Combination of Knowledge and Statistical Data
TL;DR: In this article, a Bayesian approach for learning Bayesian networks from a combination of prior knowledge and statistical data is presented, which is derived from a set of assumptions made previously as well as the assumption of likelihood equivalence, which says that data should not help to discriminate network structures that represent the same assertions of conditional independence.
Proceedings ArticleDOI
Goal-oriented requirements engineering: a guided tour
TL;DR: The paper compares the main approaches to goal modeling, goal specification and goal-based reasoning in the many activities of the requirements engineering process and suggests what a goal-oriented requirements engineering method may look like.
Journal ArticleDOI
A Review of Relational Machine Learning for Knowledge Graphs
TL;DR: This paper provides a review of how statistical models can be “trained” on large knowledge graphs, and then used to predict new facts about the world (which is equivalent to predicting new edges in the graph) and how such statistical models of graphs can be combined with text-based information extraction methods for automatically constructing knowledge graphs from the Web.
Proceedings Article
The Winograd schema challenge
TL;DR: The Winograd Schema Challenge as mentioned in this paper is an alternative to the Turing Test that has some conceptual and practical advantages, such as the ability to be easily found using selectional restrictions or statistical techniques over text corpora.
Proceedings ArticleDOI
Requirements engineering in the year 00: a research perspective
TL;DR: The initial description of a complex safety-critical system is used to illustrate a number of current research trends in RE-specific areas such as goal-oriented requirements elaboration, conflict management, and the handling of abnormal agent behaviors.
References
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Book
Fuzzy sets
TL;DR: A separation theorem for convex fuzzy sets is proved without requiring that the fuzzy sets be disjoint.
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Artificial Intelligence: A Modern Approach
Stuart Russell,Peter Norvig +1 more
TL;DR: In this article, the authors present a comprehensive introduction to the theory and practice of artificial intelligence for modern applications, including game playing, planning and acting, and reinforcement learning with neural networks.
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Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference
TL;DR: Probabilistic Reasoning in Intelligent Systems as mentioned in this paper is a complete and accessible account of the theoretical foundations and computational methods that underlie plausible reasoning under uncertainty, and provides a coherent explication of probability as a language for reasoning with partial belief.
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A mathematical theory of evidence
TL;DR: This book develops an alternative to the additive set functions and the rule of conditioning of the Bayesian theory: set functions that need only be what Choquet called "monotone of order of infinity." and Dempster's rule for combining such set functions.