Parallel Processing for Artificial Intelligence 3, Volume 20

Knowledge Representation Massively parallel knowledge representation and reasoning: taking a cue from the brain (L. Shastri, D.R. Mani). Massively parallel support for nonmonotonic reasoning (B. Boutsinas et al.). Parallel operations on class hierarchies with double strand representation (E.Y. Lee, J. Geller). PARKA on MIMD-Supercomputers (K. Stoffel, et al.). Search and Partitioning A hybrid approach to improving the performance of parallel search (D.J. Cooke). Static partitioning with slackness. (C.B. Suttner). Problem partition and solvers coordination in distributed constraint satisfaction (P. Berlandier, B. Neveu). Theorem Proving Parallel propagation in the description-logic system FLEX (F.W. Bergmann, J.J. Quantz). An alternative approach to concurrent theorem-proving (M. Fisher). SiCoTHEO - simple competitive parallel theorem provers based on SETHEO (J. Schumann). Miscellaneous Low-Level computer vision algorithms: performance evaluation on parallel and distributed architectures (G. Destri, P. Marenzoni). Decision trees on parallel processors (R. Kufrin). Application development under ParCeL-1 (Y. Lallement et al.). AI applications of massive parallelism: an experience report (D.L. Waltz). Appendix

The third in an informal series of books about parallel processing for Artificial Intelligence, this volume is based on the assumption that the computational demands of many AI tasks can be better served by parallel architectures than by the currently popular workstations. However, no assumption is made about the kind of parallelism to be used. Transputers, Connection Machines, farms of workstations, Cellular Neural Networks, Crays, and other hardware paradigms of parallelism are used by the authors of this collection.

The papers arise from the areas of parallel knowledge representation, neural modeling, parallel non-monotonic reasoning, search and partitioning, constraint satisfaction, theorem proving, parallel decision trees, parallel programming languages and low-level computer vision. The final paper is an experience report about applications of massive parallelism which can be said to capture the spirit of a whole period of computing history.

This volume provides the reader with a snapshot of the state of the art in Parallel Processing for Artificial Intelligence.