2 edition of Knowledge Representation (Artificial Intelligence Texts) found in the catalog.
Knowledge Representation (Artificial Intelligence Texts)
by Blackwell Science Inc
Written in English
|The Physical Object|
|Number of Pages||130|
'An excellent text for both students and experts in answer-set programming and knowledge representation.' Chitta Baral - Arizona State University 'Michael Gelfond is one of the creators of answer-set programming, a new programming methodology based on artificial intelligence that has already found several important by: Knowledge representation incorporates findings from psychology about how humans solve problems and represent knowledge in order to design formalisms that will make complex systems easier to .
Handbook of Knowledge Representation describes the essential foundations of Knowledge Representation, which lies at the core of Artificial Intelligence (AI). The book provides an up-to-date review of twenty-five key topics in knowledge representation, written by the leaders of each field. Knowledge Representation A subarea of Arti cial Intelligence concerned with understanding, designing, and implementing ways of representing information in computers so that programs (agents) can use this information to derive information that is implied by it, to converse with people in natural languages, to decide what to do nextFile Size: KB.
Book Description Taylor & Francis Inc, United States, Paperback. Condition: New. Language: English. Brand new Book. Knowledge representation is fundamental to the study of mind. All theories of psychological processing are rooted in assumptions about how information is stored/5(8). Book Description. This in-depth introduction to knowledge representation and reasoning and their use in designing agents for answering logical and probabilistic queries, planning, diagnostics, and other intelligent tasks is based on answer set programming, a powerful knowledge-representation paradigm.4/5(5).
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Knowledge Representation is well-written and interesting. The book covers a very wide range of topics in order to analyze the forms of representation they use and to identify the advantages and disadvantages of each by: Product details Series: The Morgan Kaufmann Series in Artificial Intelligence Hardcover: pages Publisher: Morgan Kaufmann; 1 edition (June 2, ) Language: English ISBN ISBN ASIN: Product Dimensions: x Cited by: Knowledge Representation: Logical, Philosophical, and Computational Foundations by John F.
Sowa. Goodreads helps you keep track of books you want to read. Start by marking “Knowledge Representation: Logical, Philosophical, and Computational Foundations” as /5. The diagram on the cover of the book is the tree of nature and logic by the thirteenth century poet, philosopher, and missionary Ramon Lull.
The main trunk supports a version of the tree of Porphyry, which illustrates Aristotle's categories. The ten leaves on the right represent ten types of questions, and the ten leaves on the left are keyed to a system of rotating disks for generating answers.
Growing interest in symbolic representation and reasoning has pushed this backstageactivity into the spotlight as a clearly identifiable and technically rich subfield in artificialintelligence. This collection of extended versions of 12 papers from the First InternationalConference on Principles of Knowledge Representation and Reasoning provides a snapshot of the bestcurrent work in AI on.
Sowa describes knowledge representation as the application of logic and ontology to the task of constructing computable models for some domain.
This book continues the tradition established in Sowa's first book, Conceptual structures , of integrating ideas from an amazing array of disciplines in a historically based, coherent, detailed, and disciplined manner.
• Introduction to techniques used to represent symbolic knowledge • Associated methods of automated reasoning • The three systems that we saw – use symbolic knowledge representation and reasoning – But, they also use non-symbolic methods • Non-symbolic methods are.
What is representation. Symbols standing for things in the world "John" "John loves Mary" first aid women John the proposition that John loves Mary Knowledge representation: symbolic encoding of propositions believed (by some agent).
20 rows The course work will consist of assignments a mideterm and a final exam. While portions of. What is a Knowledge Representation. The object of a knowledge representation is to express knowledge in a computer tractable form, so that it can be used to enable our AI agents to perform well.
A knowledge representation language is defined by two aspects: 1. Syntax The syntax of a language defines which configurations of the components. Knowledge representation is fundamental to the study of mind.
All theories of psychological processing are rooted in assumptions about how information is stored. These assumptions, in turn, influence the explanatory - Selection from Knowledge Representation [Book].
In this book, originally published inChitta Baral shows exactly how to go about doing that: how to write programs that behave intelligently by giving them the ability to express knowledge and reason about it. He presents a language, AnsProlog, for both knowledge representation and reasoning, and declarative problem solving.
Book Description 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.
Knowledge representation is fundamental to the study of mind. All theories of psychological processing are rooted in assumptions about how information is stored. These assumptions, in turn, influence the explanatory power of theories. This book fills a gap in the existing literature by providing an.
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.
Knowledge Representation Book Abstract: Growing interest in symbolic representation and reasoning has pushed this backstage activity into the spotlight as a clearly identifiable and technically rich subfield in artificial intelligence.
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Thank you for your patience. Book Annex Membership Educators Gift Cards Stores & Events Help Auto Suggestions are available once you type at least 3 letters. Knowledge Representation. 1 - 20 of results Grid View Grid. List View List. Add to Wishlist. Quickview This book looks at the complexity of knowledge.
It takes into account diverse disciplines such as economics, social sciences, international business, and organization studies. The authors focus on knowledge internationally from a macro.
Knowledge representation and reasoning are the parts of AI that are concerned with how an agent uses what it knows in deciding what to do. It is the study of thinking as a computational process. The book introduces the symbolic structures invented for representing knowledge and the computational processes devised for reasoning with those symbolic structures.
Publisher Summary This chapter discusses the third of the major knowledge representation paradigms, logic, and in particular, the first-order predicate calculus. A number of logics have been developed in philosophy and mathematics to represent arguments and to assess their soundness or unsoundness.
The technologies of knowledge representation and inference in an artificial intelligence system focused on the domain of nuclear physics and nuclear power engineering are considered.The author's approach to AI is based on the fact that intelligence involves knowing things, and therefore artificial intelligence involves representing knowledge.
The book is an attempt to cover the whole field of artificial intelligence by discussing all issues concerning knowledge representation. Michael K. Bergman announces his new book, A Knowledge Representation Practionary: Guidance from Charles Sanders Peirce.
The book applies this guidance to the question of how to best represent human knowledge to computers. The book’s practical guidelines should be of interest to any enterprise KM manager, AI researcher interested in knowledge, or Peirce scholar.