Logic-Based Artificial Intelligence

The use of mathematical logic as a formalism for artificial intelligence was recognized by John McCarthy in 1959 in his paper on Programs with Common Sense. In a series of papers in the 1960's he expanded upon these ideas and continues to do so to this date. It is now 41 years since the idea of using a formal mechanism for AI arose. It is therefore appropriate to consider some of the research, applications and implementations that have resulted from this idea. In early 1995 John McCarthy suggested to me that we have a workshop on Logic-Based Artificial Intelligence (LBAI). In June 1999, the Workshop on Logic-Based Artificial Intelligence was held as a consequence of McCarthy's suggestion. The workshop came about with the support of Ephraim Glinert of the National Science Foundation (IIS-9S2013S), the American Association for Artificial Intelligence who provided support for graduate students to attend, and Joseph JaJa, Director of the University of Maryland Institute for Advanced Computer Studies who provided both manpower and financial support, and the Department of Computer Science. We are grateful for their support. This book consists of refereed papers based on presentations made at the Workshop. Not all of the Workshop participants were able to contribute papers for the book. The common theme of papers at the workshop and in this book is the use of logic as a formalism to solve problems in AI.

Klappentext

This landmark volume represents the culmination of over 40 years of research in the use of logic as a basis for representing and manipulating problems in the field of artificial intelligence. The use of logic as a basis for commonsense reasoning was started by John McCarthy in 1959. The collection consists of both original research and surveys of almost every subject that uses logic in AI, contributed by leading scientists, and grew out of preliminary work presented at the Workshop on Logic-Based Artificial Intelligence held in Washington, DC, June 1999. All papers have been extensively refereed and revised. The introductory article presents background on research that has transpired since 1959 and discusses the significance of each chapter in this context. The topics covered in the book are commonsense reasoning, knowledge representation, nonmonotonic reasoning, logic for causation and actions, planning and problem solving, cognitive robotics, logic for agents and actions, inductive reasoning, possibilistic logic, logic and beliefs, logic and language, computational logic, knowledge base system implementations, and applications of theorem proving and logic programming. Logic-Based Artificial Intelligence is invaluable to graduate students and researchers in artificial intelligence, and advanced methods for database and knowledge base systems. Logic-Based Artificial Intelligence will also be of interest to those applying theorem proving methods to problems in program and hardware verification, to those who deal with large knowledge base systems, those developing cognitive robotics, and for those interested in the solution of McCarthy's 1959 "oldest planning problem in AI: getting from home to the airport".

Inhalt
I Introduction to Logic-Based Artificial Intelligence.- 1 Introduction to Logic-Based Artificial Intelligence.- II Commonsense Reasoning.- 2 Concepts of Logical AI.- III Knowledge Representation.- 3 Two Approaches to Efficient Open-World Reasoning.- 4 Declarative Problem-Solving in DLV.- IV Nonmonotonic Reasoning.- 5 The Role of Default Logic in Knowledge Representation.- 6 Approximations, stable operators, well-founded fixpoints and applications in nonmonotonic reasoning.- V Logic for Causation and Actions.- 7 Getting to the Airport: The Oldest Planning Problem in AI.- VI Planning and Problem Solving.- 8 Encoding Domain Knowledge for Propositional Planning.- 9 Functional Strips.- VII Logic, Planning and High Level Robotics.- 10 Planning with Natural Actions in the Situation Calculus.- 11 Reinventing Shakey.- VIII Logic for Agents and Actions.- 12 Reasoning Agents in Dynamic Domains.- 13 Dynamic Logic for Reasoning about Actions and Agents.- IX Inductive Reasoning.- 14 Logic-Based Machine Learning.- X Possibilistic Logic.- 15 Decision, Nonmonotonic Reasoning, Possibilistic Logic.- XI Logic and Beliefs.- 16 The Role(s) of Belief in AI.- 17 Modeling the Beliefs of Other Agents.- XII Logic and Language.- 18 The Situations We Talk about.- XIII Computational Logic.- 19 Linear Time Datalog and Branching Time Logic.- 20 On the Expressive Power of Planning Formalisms.- XIV Knowledge Base System Implementations.- 21 Extending the Smodels System with Cardinality and Weight Constraints.- 22 Nonmonotonic Reasoning in???.- XV Applications of Theorem Proving and Logic Programming.- 23 Towards a Mechanically Checked Theory of Computation.- 24 Logic-Based Techniques in Data Integration.