Minker J. (ed.) Logic-Based Artificial Intelligence

Издательство Kluwer, 2000, -631 pp.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 1998 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-9820138), 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.Part I Introduction to Logic-Based Artificial Intelligence
Introduction to Logic-Based Artificial Intelligence
Part II Commonsense Reasoning
Concepts of Logical AI
Part III Knowledge Representation
Two Approaches to Efficient Open-World Reasoning
Declarative Problem-Solving in DLV
Part IV Nonmonotonic Reasoning
The Role of Default Logic in Knowledge Representation
Approximations, Stable Operators, Well-Founded Fixpoints and Applications in Nonmonotonic Reasoning
Part V Logic for Causation and Actions
Getting to the Airport: The Oldest Planning Problem in AI
Part VI Planning and Problem Solving
Encoding Domain Knowledge for Propositional Planning
Part VII Logic, Planning and High Level Robotics
Planning with Natural Actions in the Situation Calculus
Reinventing Shakey
Part VIII Logic for Agents and Actions
Reasoning Agents in Dynamic Domains
Dynamic Logic for Reasoning about Actions and Agents
Part IX Inductive Reasoning
Logic-Based Machine Learning
Part X Possibilistic Logic
Decision, Nonmonotonic Reasoning, Possibilistic Logic
Part XI Logic and Beliefs
The Role(s) of Belief in AI
Modeling the Beliefs of Other Agents
Part XII Logic and Language
The Situations We Talk about
Part XIII Computational Logic
Linear Time Datalog and Branching Time Logic
On the Expressive Power of Planning Formalisms
Part XIV Knowledge Base System Implementations
Extending the Smodels System with Cardinality and Weight Constraints
Nonmonotonic Reasoning in CVC++
Part XV Applications of Theorem Proving and Logic Programming
Towards a Mechanically Checked Theory of Computation
Logic-Based Techniques in Data Integration