| dc.contributor.author | Kettler, Brian | en_US |
| dc.contributor.author | Andersen, William | en_US |
| dc.contributor.author | Hendler, James | en_US |
| dc.contributor.author | Luke, Sean | en_US |
| dc.date.accessioned | 2004-05-31T22:33:04Z | |
| dc.date.available | 2004-05-31T22:33:04Z | |
| dc.date.created | 1995-06 | en_US |
| dc.date.issued | 1998-10-15 | en_US |
| dc.identifier.uri | http://hdl.handle.net/1903/736 | |
| dc.description.abstract | Parka is a symbolic, semantic network knowledge representation system
that takes advantage of the massive parallelism of supercomputers such
as the Connection Machine. The Parka language has many of the
features of traditional semantic net/frame-based knowledge
representation languages but also supports several kinds of rapid
parallel inference mechanisms that scale to large knowledge-bases of
hundreds of thousands of frames or more. Parka is intended for
general-purpose use and has been used thus far to support A.I. systems
for case-based reasoning and data mining.
This document is a user manual for the current version of Parka,
version 3.2. It describes the Parka language and presents some
examples of knowledge representation using Parka. Details about the
parallel algorithms, implementation, and empirical results are
presented elsewhere.
(Also cross-referenced as UMIACS-TR-95-68) | en_US |
| dc.format.extent | 1235179 bytes | |
| dc.format.mimetype | application/postscript | |
| dc.language.iso | en_US | |
| dc.relation.ispartofseries | UM Computer Science Department; CS-TR-3485 | en_US |
| dc.relation.ispartofseries | UMIACS; UMIACS-TR-95-68 | en_US |
| dc.title | Using the Parka Parallel Knowledge Representation System (Version 3.2) | en_US |
| dc.type | Technical Report | en_US |
| dc.relation.isAvailableAt | Digital Repository at the University of Maryland | en_US |
| dc.relation.isAvailableAt | University of Maryland (College Park, Md.) | en_US |
| dc.relation.isAvailableAt | Tech Reports in Computer Science and Engineering | en_US |
| dc.relation.isAvailableAt | UMIACS Technical Reports | en_US |
