Издательство Elsevier, 2008, -1035 pp.
Knowledge Representation and Reasoning is at the heart of the great challenge of Artificial Intelligence: to understand the nature of intelligence and cognition so well that computers can be made to exhibit human-like abilities. As early as 1958, John McCarthy contemplated Artificial Intelligence systems that could exercise common sense. From this and other early work, researchers gained the conviction that (artificial) intelligence could be formalized as symbolic reasoning with explicit representations of knowledge, and that the core research challenge is to figure out how to represent knowledge in computers and to use it algorithmically to solve problems.
Fifty years later, this book surveys the substantial body of scientific and engineering insights that constitute the field of Knowledge Representation and Reasoning. Advances have been made on three fronts. First, researchers have explored general methods of knowledge representation and reasoning, addressing fundamental issues that cut across application domains. Second, researchers have developed specialized methods of knowledge representation and reasoning to handle core domains, such as time, space, causation and action. Third, researchers have tackled important applications of knowledge representation and reasoning, including query answering, planning and the Semantic Web. Accordingly, the book is divided into three sections to cover these themes.
Part I focuses on general methods for representing knowledge in Artificial Intelligence systems. It begins with background on classical logic and theorem proving, then tus to new approaches that extend classical logic—for example, to handle qualitative or uncertain information—and to improve its computational tractability.
Part II delves into the special challenges of representing and reasoning with some core domains of knowledge, including time, space, causation and action. These challenges are ubiquitous across application areas, so solutions must be general and composable.
Part III surveys important applications of knowledge representation and reasoning. The application areas span the breadth of Artificial Intelligence to include question answering, the Semantic Web, planning, robotics and multi-agent systems. Each application draws extensively on the research results described in Parts I and II.
Together, these 25 chapters, organized in the three sections General Methods, Specialized Representations and Applications, provide a unique survey of the best that Knowledge Representation has achieved, written by researchers who have helped to shape the field. We hope that students, researchers and practitioners in all areas of Artificial Intelligence and Cognitive Science will find this book to be a useful resource.
I General Methods in Knowledge Representation and Reasoning
Knowledge Representation and Classical Logic
Satisfiability Solvers
Description Logics
Constraint Programming
Conceptual Graphs
Nonmonotonic Reasoning
Answer Sets
Belief Revision
Qualitative Modeling
Model-based Problem Solving
Bayesian Networks
II Classes of Knowledge and Specialized Representations
Temporal Representation and Reasoning
Qualitative Spatial Representation and Reasoning
Physical Reasoning
Reasoning about Knowledge and Belief
Situation Calculus
Event Calculus
Temporal Action Logics
Nonmonotonic Causal Logic
III Knowledge Representation in Applications
Knowledge Representation and Question Answering
The SemanticWeb:Webizing Knowledge Representation
Automated Planning
Cognitive Robotics
Multi-Agent Systems
Knowledge Engineering
Knowledge Representation and Reasoning is at the heart of the great challenge of Artificial Intelligence: to understand the nature of intelligence and cognition so well that computers can be made to exhibit human-like abilities. As early as 1958, John McCarthy contemplated Artificial Intelligence systems that could exercise common sense. From this and other early work, researchers gained the conviction that (artificial) intelligence could be formalized as symbolic reasoning with explicit representations of knowledge, and that the core research challenge is to figure out how to represent knowledge in computers and to use it algorithmically to solve problems.
Fifty years later, this book surveys the substantial body of scientific and engineering insights that constitute the field of Knowledge Representation and Reasoning. Advances have been made on three fronts. First, researchers have explored general methods of knowledge representation and reasoning, addressing fundamental issues that cut across application domains. Second, researchers have developed specialized methods of knowledge representation and reasoning to handle core domains, such as time, space, causation and action. Third, researchers have tackled important applications of knowledge representation and reasoning, including query answering, planning and the Semantic Web. Accordingly, the book is divided into three sections to cover these themes.
Part I focuses on general methods for representing knowledge in Artificial Intelligence systems. It begins with background on classical logic and theorem proving, then tus to new approaches that extend classical logic—for example, to handle qualitative or uncertain information—and to improve its computational tractability.
Part II delves into the special challenges of representing and reasoning with some core domains of knowledge, including time, space, causation and action. These challenges are ubiquitous across application areas, so solutions must be general and composable.
Part III surveys important applications of knowledge representation and reasoning. The application areas span the breadth of Artificial Intelligence to include question answering, the Semantic Web, planning, robotics and multi-agent systems. Each application draws extensively on the research results described in Parts I and II.
Together, these 25 chapters, organized in the three sections General Methods, Specialized Representations and Applications, provide a unique survey of the best that Knowledge Representation has achieved, written by researchers who have helped to shape the field. We hope that students, researchers and practitioners in all areas of Artificial Intelligence and Cognitive Science will find this book to be a useful resource.
I General Methods in Knowledge Representation and Reasoning
Knowledge Representation and Classical Logic
Satisfiability Solvers
Description Logics
Constraint Programming
Conceptual Graphs
Nonmonotonic Reasoning
Answer Sets
Belief Revision
Qualitative Modeling
Model-based Problem Solving
Bayesian Networks
II Classes of Knowledge and Specialized Representations
Temporal Representation and Reasoning
Qualitative Spatial Representation and Reasoning
Physical Reasoning
Reasoning about Knowledge and Belief
Situation Calculus
Event Calculus
Temporal Action Logics
Nonmonotonic Causal Logic
III Knowledge Representation in Applications
Knowledge Representation and Question Answering
The SemanticWeb:Webizing Knowledge Representation
Automated Planning
Cognitive Robotics
Multi-Agent Systems
Knowledge Engineering