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.

Inhalt

Contributing Authors. Preface. Part I: Introduction to Logic-Based Artificial Intelligence. 1. Introduction to Logic-Based Artificial Intelligence; J. Minker. Part II: Commonsense Reasoning. 2. Concepts of Logical AI; J. McCarthy. Part III: Knowledge Representation. 3. Two Approaches to Efficient Open-World Reasoning; G. De Giacomo, H. Levesque. 4. Declarative Problem-Solving in DLV; T. Eder, et al. Part IV: Nonmonotonic Reasoning. 5. The Role of Default Logic in Knowledge Representation; J.P. Delgrande, Torsten Schaub. 6. Approximations, stable operators, well-founded fixpoints, applications in nonmonotonic reasoning; M. Denecker, et al. Part V: Logic for Causation, Actions. 7. Getting to the Airport: The Oldest Planning Problem in AI; V. Lifschitz, et al. Part VI: Planning, Problem Solving. 8. Encoding Domain Knowledge for Propositional Planning; H. Kautz, B. Selman. 9. Functional Strips; H. Geffner. Part VII: Logic, Planning, High Level Robotics. 10. Planning with Natural Actions in the Situation Calculus; F. Pirri, R. Reiter. 11. Reinventing Shakey; M. Shanahan. Part VIII: Logic for Agents, Actions. 12. Reasoning Agents in Dynamic Domains; C. Baral, M. Gelfond. 13. Dynamic Logic for Reasoning about Actions, Agents; J.-J.Ch. Meyer. Part IX: Inductive Reasoning. 14. Logic-Based Machine Learning; S. Muggleton, F. Marginean. Part X: Possibilistic Logic. 15. Decision, Nonmonotonic Reasoning, Possibilistic Logic; S. Benferhat, etal. Part Xl: Logic, Beliefs. 16. The Role(s) of Belief in AI; D. Perlis. 17. Modeling the Beliefs of Other Agents; R.H. Thomason. Part XII: Logic, Language. 18. The Situations We Talk about; L.K. Schuberi. Part XIII: Computational Logic. 19. Linear Time Datalog, Branching.Time Logic; G. Goitlob, et al. 20. On the Expressive Power of Planning Formalisms; B. Nebel. Part XIV: Knowledge Base System Implementations. 21. Extending the Smodels System with Cardinality, Weight Constraints; I. Niemelä, P. Simons. 22. Nonmonotonic Reasoning in LDL++; H. Wang, C. Zaniolo. Part XV: Applications of Theorem Proving, Logic Programming. 23. Towards a Mechanically Checked Theory of Computation; J. Strother Moore. 24. Logic-Based Techniques in Data Integration; A.Y. Levy. Index.