PARKA Publications

Research Issues in PARKA:

James Hendler, Kilian Stoffel, Merwyn Taylor. Advances in High Performance Knowledge Representation. University of Maryland Institute for Advanced Computer Studies~Dept. of Computer Science, Univ. of Maryland, July 1996.

CS-TR-3672 (Also cross-referenced as UMIACS-TR-96-56)

This report contains two papers describing important new results in the Parka High Performance Knowledge Representation language.

We have ported the SIMD Parka knowledge representation system to generic MIMD machines. The system has been recoded in C and supported using runtime optimization packages developed in the High Performance Systems Software Laboratory at the University of Maryland. New ``scanning'' algorithms have been developed for inheritance and recognition inferences. These algorithms have been tested with both random networks and on a recoding of the ontology of the CYC knowledge base as well as on large planning case-bases. Tests show that the new version is significantly faster than the SIMD system, and that it promises to scale well to knowledge bases orders of magnitude larger than CYC.
Real world applications are demanding that KR systems provide support for knowledge bases containing millions of assertions. We present Parka-DB, a high-performance reimplementation of the Parka KR language that uses a standard relational DBMS. The integration of a DBMS and the Parka KR language allows us to efficiently support complex queries on extremely large KBs using a single processor, as opposed to our earlier massively parallel system. In addition, the system can make good use of secondary memory, with the whole system needing less than 16MB of RAM to hold a KB of over 2,000,000 assertions. We demonstrate empirically that this reduction in primary storage requires only about 10% overhead in time, and decreases the load time of very large KBs by more than two orders of magnitude.

M.P. Evett, J.A. Hendler, and L. Spector, Parallel Knowledge Representation on the Connection Machine. Journal of Parallel and Distributed Computing, 22:168-184, 1994.

Evett, M.P., PARKA: A System for Massively Parallel Knowledge Representation, Ph.D. dissertation, 1994.

W.A. Andersen, J.A. Hendler, M.P. Evett, and B.P. Kettler. Massively Parallel Matching of Knowledge Structures. In H. Kitano and J. Hendler, editors, Massively Parallel Artificial Intelligence. AAAI/The MIT Press, 1994.

Kettler, B.P., Hendler, J.A., Andersen, W.A., and Evett, M.P. Massively Parallel Support for a Case-based Planning System. IEEE Expert, Feb. 1994, pp. 8-14.

M.P. Evett, W.A. Andersen, and J.A. Hendler. Providing Computational Effective Knowledge Representation via Massive Parallelism. In L. Kanal, V. Kumar, H. Kitano, and C. Suttner, editors, Parallel Processing for Artificial Intelligence. Elsevier Science Publishers, 1994.

Evett, M.P., Hendler, J.A., Mahanti, A. and Nau, D. PRA*: Massively Parallel Heuristic Search, Journal of Parallel and Distributed Computing, (accepted March, 1993).

Evett, M.P., Andersen, W.A. and Hendler, J.A., Massively Parallel Support for Computationally Effective Recognition Queries, Proceedings of the Eleventh National Conference on Artificial Intelligence (AAAI-93), AAAI Press, Menlo Park, CA, 1993.

Evett, M.P., Andersen, W.A. and Hendler, J.A., Massively Parallel Support for Efficient Knowledge Representation, Proceedings of the Thirteenth International Joint Conference on Artificial Intelligence (IJCAI-93), Morgan Kaufmann, Denver, 1993.

PARKA User Documentation:
B. Kettler, W. Andersen, J. Hendler, and S. Luke. Using the Parka Parallel Knowledge Representation System (Version 3.2), Technical Report CS-TR-3485 (UMIACS TR-95-68), Dept. of Computer Science, University of Maryland at College Park, 1995.
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