OVERVIEW OF THE SP CONCEPTS
The SP theory of intelligence: an overview (PDF)
In Information, 4 (3), 283-341, 2013.
This article is an overview of the SP theory of intelligence, which aims to
simplify and integrate concepts across artificial intelligence, mainstream computing and
human perception and cognition, with information compression as a unifying theme. It
is conceived of as a brain-like system that receives “New” information and stores some
or all of it in compressed form as “Old” information; and it is realised in the form of a
computer model, a first version of the SP machine. The matching and unification of patterns
and the concept of multiple alignment are central ideas. Using heuristic techniques, the
system builds multiple alignments that are “good” in terms of information compression.
For each multiple alignment, probabilities may be calculated for associated inferences.
Unsupervised learning is done by deriving new structures from partial matches between
patterns and via heuristic search for sets of structures that are “good” in terms of information
compression. These are normally ones that people judge to be “natural”, in accordance with
the “DONSVIC” principle—the discovery of natural structures via information compression.
The SP theory provides an interpretation for concepts and phenomena in several other areas,
including “computing”, aspects of mathematics and logic, the representation of knowledge,
natural language processing, pattern recognition, several kinds of reasoning, information
storage and retrieval, planning and problem solving, information compression, neuroscience
and human perception and cognition. Examples include the parsing and production of
language with discontinuous dependencies in syntax, pattern recognition at multiple levels
of abstraction and its integration with part-whole relations, nonmonotonic reasoning and
reasoning with default values, reasoning in Bayesian networks, including “explaining away”,
causal diagnosis, and the solving of a geometric analogy problem.
The simplicity and power model for inductive inference
In Artificial Intelligence Review 26, 211–225, 2006. PDF.
- Describes the simplicity and power model and its application to cognitive modelling
Information compression and multiple alignment as
unifying concepts in AI and computing
In Expert Update 4 (3), 22-36, 2001.
- This is a short, informal overview of the SP concepts, without
compression by multiple alignment, unification and search as a unifying
principle in computing and cognition
In Artificial Intelligence Review 19(3), 193-230, 2003.
- This describes the SP concepts at more length and provides