This book constitutes the refereed proceedings of the 5th International Conference on Logic Programming and Nonmonotonic Reasoning, LPNMR '99, held in El Paso, Texas, USA, in December 1999. The volume presents 26 contributed papers and four invited talks, three appearing as extended abstracts and one as a full paper. Topics covered include logic programming, non-monotonic reasoning, knowledge representation, semantics, complexity, expressive power, and implementation and applicatons.

This book suggests that classification is a key to human commonsense reasoning and transforms traditional considerations of data and knowledge communications, presenting an effective classification of logical rules used in the modeling of commonsense reasoning.

This book constitutes the refereed proceedings of the 1999 European Conference on Symbolic and Quantitative Approaches to Reasoning under Uncertainty, ECSQARU'99, held in London, UK, in July 1999. The 35 revised full papers presented were carefully reviewed and selected for inclusion in the book by the program committee. The volume covers theoretical as well as application-oriented aspects of various formalisms for reasoning under uncertainty. Among the issues addressed are default reasoning, nonmonotonic reasoning, fuzzy logic, Bayesian theory, probabilistic reasoning, inductive learning, rough knowledge discovery, Dempster-Shafer theory, qualitative decision making, belief functions, and evidence theory.

Shared-workspace systems with structured graphical representations allow for the free user interaction and the joint construction of problem solutions for potentially open-ended tasks. However, group modelling in shared workspaces has to take on a process-orientated perspective due to the reduced system control in shared workspaces. This text is defined as the monitoring of user actions and the abstraction and interpretation of the raw data in the context of the group interaction and the problem representation. Formally based on plan recognition and the situation calculus, an approach has been developed that incorporates an operational hierarchy for generally modelling activities. The system performs an automatic inline analysis of group interactions and the results are visualized in different forms to give feedback and stimulating self-reflection.

This thesis presents an approach to learning good search guiding heuristics for the supposition-based theorom prover E in equational deductions. Search decisions from successful proof searches are represented as sets annotated clause patterns. Term Space Mapping, an alternative learning method for recursive structures is used to learn heuristic evaluation functions for the evaluation of potential new consequences. Experimental results with extended system E/TSM show the success of the approach. Additional contributions of the thesis are an extended superposition calculus and a description of both the proof procedure and the implementation of a state-of-the-art equational theorem prover.

Reasoning with incomplete information constitutes a major challenge for any intelligent system. In fact, we expect such systems not to become paralyzed by missing information but rather to arrive at plausible results by bridging the gaps in the information available. A versatile way of reasoning in the absence of information is to reason by default. This book aims at providing formal and practical means for automating reasoning with incomplete information by starting from the approach taken by the framework of default logic. For this endeavor, a bridge is spanned between formal semantics, over systems for default reasoning, to efficient implementation.