This is the third volume in an informal series of books about parallel processing for artificial intelligence. It 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 a report about applications of massive parallelism and aims to capture the spirit of a whole period of computing history.