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Product Description
The idea of knowledge bases lies at the heart of symbolic, or "traditional," artificial intelligence. A knowledge-based system decides how to act by running formal reasoning procedures over a body of explicitly represented knowledge--a knowledge base. The system is not programmed for specific tasks; rather, it is told what it needs to know and expected to infer the rest.
This book is about the logic of such knowledge bases. It describes in detail the relationship between symbolic representations of knowledge and abstract states of knowledge, exploring along the way the foundations of knowledge, knowledge bases, knowledge-based systems, and knowledge representation and reasoning. Assuming some familiarity with first-order predicate logic, the book offers a new mathematical model of knowledge that is general and expressive yet more workable in practice than previous models. The book presents a style of semantic argument and formal analysis that would be cumbersome or completely impractical with other approaches. It also shows how to treat a knowledge base as an abstract data type, completely specified in an abstract way by the knowledge-level operations defined over it.
Book Info
A text describing the relationship between symbolic representations of knowledge and abstract states of knowledge, exploring foundations of knowledge, knowledge bases, and other aspects of the logic of knowledge bases. Presents a style of semantic argument and formal analysis incompatible in any other context. DLC: Knowledge representation (Information Technology).
The idea of knowledge bases lies at the heart of symbolic, or "traditional," artificial intelligence. A knowledge-based system decides how to act by running formal reasoning procedures over a body of explicitly represented knowledge--a knowledge base. The system is not programmed for specific tasks; rather, it is told what it needs to know and expected to infer the rest.
This book is about the logic of such knowledge bases. It describes in detail the relationship between symbolic representations of knowledge and abstract states of knowledge, exploring along the way the foundations of knowledge, knowledge bases, knowledge-based systems, and knowledge representation and reasoning. Assuming some familiarity with first-order predicate logic, the book offers a new mathematical model of knowledge that is general and expressive yet more workable in practice than previous models. The book presents a style of semantic argument and formal analysis that would be cumbersome or completely impractical with other approaches. It also shows how to treat a knowledge base as an abstract data type, completely specified in an abstract way by the knowledge-level operations defined over it.
Book Info
A text describing the relationship between symbolic representations of knowledge and abstract states of knowledge, exploring foundations of knowledge, knowledge bases, and other aspects of the logic of knowledge bases. Presents a style of semantic argument and formal analysis incompatible in any other context. DLC: Knowledge representation (Information Technology).
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Inside This Book Citations: This book cites 35 books | 7 books that cite this book Explore: Citations | Books on Related Topics | Concordance | Text Stats Key Phrases - SIPs: distinct standard names, representable states, basic belief set, initial epistemic state, propositional subset (more) Browse Sample Pages: Front Cover | Table of Contents | Excerpt | Index | Back Cover | Surprise Me! |
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Inside This Book
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First Sentence:
In a book about the logical foundations of knowledge bases, it is probably a good idea to review if only briefly how concepts like knowledge, representation, reasoning, knowledge bases, and so on are understood informally within Artificial Intelligence (AI), and why so many researchers feel that these notions are important to the AI enterprise. Read the first page Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
distinct standard names, representable states, basic belief set, initial epistemic state, propositional subset, objective sentences, tautological entailment, subjective sentences, current epistemic state, finite consistent set, autoepistemic reasoning, prenex conjunctive normal form, stable expansions, autoepistemic logic, maximally consistent set, epistemic states, nested beliefs, prime implicates, admissible terms, objective formula, admissible substitution, ask operations, propositional case, belief implication, valid sentences New!
Books on Related Topics | Concordance | Text Stats Browse Sample Pages:
Front Cover | Table of Contents | Excerpt | Index | Back Cover | Surprise Me!
In a book about the logical foundations of knowledge bases, it is probably a good idea to review if only briefly how concepts like knowledge, representation, reasoning, knowledge bases, and so on are understood informally within Artificial Intelligence (AI), and why so many researchers feel that these notions are important to the AI enterprise. Read the first page Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
distinct standard names, representable states, basic belief set, initial epistemic state, propositional subset, objective sentences, tautological entailment, subjective sentences, current epistemic state, finite consistent set, autoepistemic reasoning, prenex conjunctive normal form, stable expansions, autoepistemic logic, maximally consistent set, epistemic states, nested beliefs, prime implicates, admissible terms, objective formula, admissible substitution, ask operations, propositional case, belief implication, valid sentences New!
Books on Related Topics | Concordance | Text Stats Browse Sample Pages:
Front Cover | Table of Contents | Excerpt | Index | Back Cover | Surprise Me!