SOAR: an architecture for general intelligence
Artificial Intelligence
An introduction to Kolmogorov complexity and its applications
An introduction to Kolmogorov complexity and its applications
Unified theories of cognition
A scalable technique for best-match retrieval of sequential information using metrics-guided search
Journal of Information Science
Emerging trends in database and knowledge-base machines: the application of parallel architectures to smart information systems
The Unified Modeling Language user guide
The Unified Modeling Language user guide
Associative random access machines and data-parallel multiway binary-search join
Future Generation Computer Systems
The entity-relationship model—toward a unified view of data
ACM Transactions on Database Systems (TODS) - Special issue: papers from the international conference on very large data bases: September 22–24, 1975, Framingham, MA
Oracle parallel processing
Parallel Database Techniques
Intelligent Database Systems
Soar Papers: Research on Integrated Intelligence
Soar Papers: Research on Integrated Intelligence
Artificial Intelligence Review
A Study of a Parallel Database Machine and its Performance the NCR/Teradata DBC/1012
BNCOD 10 Proceedings of the 10th British National Conference on Databases: Advanced Database Systems
Simula Begin
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The SP theory of computing and cognition, described in previous publications, is an attractive model for intelligent databases because it provides a simple but versatile format for different kinds of knowledge, it has capabilities in artificial intelligence, it can function effectively in the face of errors in its input data, and it can function like established database models when that is required. This paper first describes the SP theory in outline and the computer models in which it is expressed. The main sections of the paper describe, with examples from the SP62 computer model, how the SP framework can emulate other abstract models used in database applications: the relational model (including retrieval of information in the manner of query-by-example, creating a join between two or more tables, and aggregation), object-oriented models (including class-inclusion hierarchies, part-whole hierarchies and their integration, inheritance of attributes, cross-classification and multiple inheritance), and hierarchical and network models (including discrimination networks). Comparisons are made between the SP model and those other models. The artificial intelligence capabilities of the SP model are briefly reviewed: representation and integration of diverse kinds of knowledge in one versatile format; fuzzy pattern recognition and recognition at multiple levels of abstraction; best-match and semantic forms of information retrieval; various kinds of exact reasoning and probabilistic reasoning; analysis and production of natural language; planning; problem solving; and unsupervised learning. Also considered are ways in which current prototypes may be translated into an 'industrial strength' working system.