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J. Wolff
CEng MBCS MIEEE, CognitionResearch.org, 18 Penlon, Menai Bridge, Anglesey LL59 5LR, UK

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Short Biography

Dr Gerry Wolff is the Director of CognitionResearch.org. He has held academic posts at the University of Wales, Bangor, the University of Dundee, and the University Hospital of Wales, Cardiff, UK. He has also worked as a software engineer with Praxis Systems plc in Bath, UK. His first degree at Cambridge University was in Natural Sciences and his PhD at the University of Wales, Cardiff, was in the area of Cognitive Science. He is a chartered engineer and member of the British Computer Society. Dr Wolff has numerous publications in a wide range of journals, collected works and conference proceedings.

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Journal article
Published: 20 April 2021 in Sustainability
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The SP System (SPS), referring to the SP Theory of Intelligence and its realisation as the SP Computer Model, has the potential to reduce demands for energy from IT, especially in AI applications and in the processing of big data, in addition to reductions in CO2 emissions when the energy comes from the burning of fossil fuels. The biological foundations of the SPS suggest that with further development, the SPS may approach the extraordinarily low (20 W)energy demands of the human brain. Some of these savings may arise in the SPS because, like people, the SPS may learn usable knowledge from a single exposure or experience. As a comparison, deep neural networks (DNNs) need many repetitions, with much consumption of energy, for the learning of one concept. Another potential saving with the SPS is that like people, it can incorporate old learning in new. This contrasts with DNNs where new learning wipes out old learning (‘catastrophic forgetting’). Other ways in which the mature SPS is likely to prove relatively parsimonious in its demands for energy arise from the central role of information compression (IC) in the organisation and workings of the system: by making data smaller, there is less to process; because the efficiency of searching for matches between patterns can be improved by exploiting probabilities that arise from the intimate connection between IC and probabilities; and because, with SPS-derived ’Model-Based Codings’ of data, there can be substantial reductions in the demand for energy in transmitting data from one place to another.

ACS Style

J. Wolff. How the SP System May Promote Sustainability in Energy Consumption in IT Systems. Sustainability 2021, 13, 4565 .

AMA Style

J. Wolff. How the SP System May Promote Sustainability in Energy Consumption in IT Systems. Sustainability. 2021; 13 (8):4565.

Chicago/Turabian Style

J. Wolff. 2021. "How the SP System May Promote Sustainability in Energy Consumption in IT Systems." Sustainability 13, no. 8: 4565.

Chapter
Published: 27 March 2021 in Econometrics for Financial Applications
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This chapter describes how the SP System, meaning the SP Theory of Intelligence, and its realisation as the SP Computer Model, may promote transparency and granularity in AI, and some other areas of application. The chapter describes how transparency in the workings and output of the SP Computer Model may be achieved via three routes: (1) the program provides a very full audit trail for such processes as recognition, reasoning, analysis of language, and so on. There is also an explicit audit trail for the unsupervised learning of new knowledge; (2) knowledge from the system is likely to be granular and easy for people to understand; and (3) there are seven principles for the organisation of knowledge which are central in the workings of the SP System and also very familiar to people (eg chunking-with-codes, part-whole hierarchies, and class-inclusion hierarchies), and that kind of familiarity in the way knowledge is structured by the system, is likely to be important in the interpretability, explainability, and transparency of that knowledge. Examples from the SP Computer Model are shown throughout the chapter.

ACS Style

J. Gerard Wolff. Transparency and Granularity in the SP Theory of Intelligence and Its Realisation in the SP Computer Model. Econometrics for Financial Applications 2021, 187 -216.

AMA Style

J. Gerard Wolff. Transparency and Granularity in the SP Theory of Intelligence and Its Realisation in the SP Computer Model. Econometrics for Financial Applications. 2021; ():187-216.

Chicago/Turabian Style

J. Gerard Wolff. 2021. "Transparency and Granularity in the SP Theory of Intelligence and Its Realisation in the SP Computer Model." Econometrics for Financial Applications , no. : 187-216.

Journal article
Published: 23 February 2021 in Big Data and Cognitive Computing
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This paper aims to describe how pattern recognition and scene analysis may with advantage be viewed from the perspective of the SP system (meaning the SP theory of intelligence and its realisation in the SP computer model (SPCM), both described in an appendix), and the strengths and potential of the system in those areas. In keeping with evidence for the importance of information compression (IC) in human learning, perception, and cognition, IC is central in the structure and workings of the SPCM. Most of that IC is achieved via the powerful concept of SP-multiple-alignment, which is largely responsible for the AI-related versatility of the system. With examples from the SPCM, the paper describes: how syntactic parsing and pattern recognition may be achieved, with corresponding potential for visual parsing and scene analysis; how those processes are robust in the face of errors in input data; how in keeping with what people do, the SP system can “see” things in its data that are not objectively present; the system can recognise things at multiple levels of abstraction and via part-whole hierarchies, and via an integration of the two; the system also has potential for the creation of a 3D construct from pictures of a 3D object from different viewpoints, and for the recognition of 3D entities.

ACS Style

J. Wolff. The Potential of the SP System in Machine Learning and Data Analysis for Image Processing. Big Data and Cognitive Computing 2021, 5, 7 .

AMA Style

J. Wolff. The Potential of the SP System in Machine Learning and Data Analysis for Image Processing. Big Data and Cognitive Computing. 2021; 5 (1):7.

Chicago/Turabian Style

J. Wolff. 2021. "The Potential of the SP System in Machine Learning and Data Analysis for Image Processing." Big Data and Cognitive Computing 5, no. 1: 7.

Review article
Published: 04 December 2019 in Complexity
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This paper describes a novel perspective on the foundations of mathematics: how mathematics may be seen to be largely about “information compression (IC) via the matching and unification of patterns” (ICMUP). That is itself a novel approach to IC, couched in terms of nonmathematical primitives, as is necessary in any investigation of the foundations of mathematics. This new perspective on the foundations of mathematics reflects the facts that mathematics is almost exclusively the product of human brains, and has been developed, as an aid to human thinking, mathematics is likely to be consonant with much evidence for the importance of IC in human learning, perception, and cognition. This perspective on the foundations of mathematics has grown out of a long-term programme of research developing the SP Theory of Intelligence and its realization in the SP Computer Model, a system in which a generalised version of ICMUP—the powerful concept of SP-multiple-alignment—plays a central role. This paper shows with an example how mathematics, without any special provision, may achieve compression of information. Then, it describes examples showing how variants of ICMUP may be seen in widely used structures and operations in mathematics. Examples are also given to show how several aspects of the mathematics-related disciplines of logic and computing may be understood as ICMUP. Also discussed is the intimate relation between IC and concepts of probability, with arguments that there are advantages in approaching AI, cognitive science, and concepts of probability via ICMUP. Also discussed is how the close relation between IC and concepts of probability relates to the established view that some parts of mathematics are intrinsically probabilistic, and how that latter view may be reconciled with the all-or-nothing, “exact,” forms of calculation or inference that are familiar in mathematics and logic. There are many potential benefits and applications of the mathematics-as-IC perspective.

ACS Style

J. Gerard Wolff. Mathematics as Information Compression via the Matching and Unification of Patterns. Complexity 2019, 2019, 1 -25.

AMA Style

J. Gerard Wolff. Mathematics as Information Compression via the Matching and Unification of Patterns. Complexity. 2019; 2019 ():1-25.

Chicago/Turabian Style

J. Gerard Wolff. 2019. "Mathematics as Information Compression via the Matching and Unification of Patterns." Complexity 2019, no. : 1-25.

Review article
Published: 20 February 2019 in Complexity
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This paper reviews evidence for the idea that much of human learning, perception, and cognition may be understood as information compression and often more specifically as “information compression via the matching and unification of patterns” (ICMUP). Evidence includes the following: information compression can mean selective advantage for any creature; the storage and utilisation of the relatively enormous quantities of sensory information would be made easier if the redundancy of incoming information was to be reduced; content words in natural languages, with their meanings, may be seen as ICMUP; other techniques for compression of information—such as class-inclusion hierarchies, schema-plus-correction, run-length coding, and part-whole hierarchies—may be seen in psychological phenomena; ICMUP may be seen in how we merge multiple views to make one, in recognition, in binocular vision, in how we can abstract object concepts via motion, in adaptation of sensory units in the eye of Limulus, the horseshoe crab, and in other examples of adaptation; the discovery of the segmental structure of language (words and phrases), grammatical inference, and the correction of over- and undergeneralisations in learning may be understood in terms of ICMUP; information compression may be seen in the perceptual constancies; there is indirect evidence for ICMUP in human cognition via kinds of redundancy such as the decimal expansion of π which are difficult for people to detect; much of the structure and workings of mathematics—an aid to human thinking—may be understood in terms of ICMUP; and there is additional evidence via the SP Theory of Intelligence and its realisation in the SP Computer Model. Three objections to the main thesis of this paper are described, with suggested answers. These ideas may be seen to be part of a “Big Picture” with six components, outlined in the paper.

ACS Style

J. Gerard Wolff. Information Compression as a Unifying Principle in Human Learning, Perception, and Cognition. Complexity 2019, 2019, 1 -38.

AMA Style

J. Gerard Wolff. Information Compression as a Unifying Principle in Human Learning, Perception, and Cognition. Complexity. 2019; 2019 ():1-38.

Chicago/Turabian Style

J. Gerard Wolff. 2019. "Information Compression as a Unifying Principle in Human Learning, Perception, and Cognition." Complexity 2019, no. : 1-38.

Journal article
Published: 22 January 2019 in The Computer Journal
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This paper describes a roadmap for the development of the SP Machine, based on the SP Theory of Intelligence and its realization in the SP Computer Model. The SP Machine will be developed initially as a software virtual machine with high levels of parallel processing, hosted on a high-performance computer. The system should help users visualize knowledge structures and processing. Research is needed into how the system may discover low-level features in speech and in images. Strengths of the SP System in the processing of natural language may be augmented, in conjunction with the further development of the SP System’s strengths in unsupervised learning. Strengths of the SP System in pattern recognition may be developed for computer vision. Work is needed on the representation of numbers and the performance of arithmetic processes. A computer model is needed of SP-Neural, the version of the SP Theory expressed in terms of neurons and their interconnections. The SP Machine has potential in many areas of application, several of which may be realized on short-to-medium timescales.

ACS Style

Vasile Palade; J Gerard Wolff. A Roadmap for the Development of the ‘SP Machine’ for Artificial Intelligence. The Computer Journal 2019, 62, 1584 -1604.

AMA Style

Vasile Palade, J Gerard Wolff. A Roadmap for the Development of the ‘SP Machine’ for Artificial Intelligence. The Computer Journal. 2019; 62 (11):1584-1604.

Chicago/Turabian Style

Vasile Palade; J Gerard Wolff. 2019. "A Roadmap for the Development of the ‘SP Machine’ for Artificial Intelligence." The Computer Journal 62, no. 11: 1584-1604.

Preprint
Published: 17 January 2019
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Commonsense reasoning (CSR) and commonsense knowledge (CSK) (together abbreviated as CSRK) are areas of study concerned with problems which are trivially easy for adults but which are challenging for artificial systems. This paper describes how the SP System -- meaning the "SP Theory of Intelligence" and its realisation in the "SP Computer Model" -- has strengths and potential in several aspects of CSRK. Some shortcomings of the system in that area may be overcome with planned future developments. A particular strength of the SP System is that it shows promise as an overarching theory for four areas of relative success with CSRK problems -- described by other authors -- which have been developed without any integrative theory. How the SP System may help to solve four other kinds of CSRK problem is described: 1) how the strength of evidence for a murder may be influenced by the level of lighting of the murder as it was witnessed; 2) how people may arrive at the commonly-accepted interpretation of phrases like "water bird"; 3) the interpretation of the horse's head scene in "The Godfather" film; and how the SP System may help to resolve the reference of an ambiguous pronoun in sentences in the format of a 'Winograd schema'. Also described is why a fifth CSRK problem -- modelling how a chef may crack an egg into a bowl -- is beyond the capabilities of the SP System as it is now and how those deficiencies may be overcome via planned developments of the system.

ACS Style

J Gerard Wolff. Commonsense reasoning, commonsense knowledge, and the SP Theory of Intelligence. 2019, 1 .

AMA Style

J Gerard Wolff. Commonsense reasoning, commonsense knowledge, and the SP Theory of Intelligence. . 2019; ():1.

Chicago/Turabian Style

J Gerard Wolff. 2019. "Commonsense reasoning, commonsense knowledge, and the SP Theory of Intelligence." , no. : 1.

Preprint
Published: 26 October 2017
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This paper presents evidence for the idea that much of the workings of brains and nervous systems may be understood as information compression via the matching and unification of patterns (ICMUP). Information compression can mean selective advantage for any creature: in the efficient storage and transmission of information; and, owing to the close connection between information compression and concepts of prediction and probability , in the making of predictions about where food may be found, potential dangers, and so on. Several aspects of our everyday perceptions and thinking may be seen as information compression. For example, many words in natural languages may be seen as relatively short identifiers or 'codes' for relatively complex concepts. When viewing the world with two eyes, we see one view, not two. Random-dot stereograms provide confirmation that, in binocular vision, we do indeed merge information from our two eyes and thus compress it. Information compression may be seen in the workings of sensory units in the eye of Limulus, the horseshoe crab. Computer models demonstrate how information compression may be a key to the unsupervised discovery of grammars for natural language, including segmental structures (words and phrases), classes of structure, and patterns. Information compression may be seen in the perceptual constancies, including size constancy, lightness constancy, and colour constancy. Mathematics, which is a product of the human intellect, may be seen to be a set of techniques for ICMUP, and their application. The SP theory of intelligence, with its empirical support, provides evidence for the importance of ICMUP, and more * Dr Gerry Wolff BA (Cantab) PhD (Wales) CEng MIEEE MBCS; CognitionResearch.org, Menai Bridge, UK; [email protected]; +44 (0) 1248 712962; +44 (0) 7746 290775; Skype: gerry.wolff; Web: www.cognitionresearch.org. 1 specifically a concept of 'SP-multiple-alignment', in several aspects of human learning, perception, and thinking. Four objections to the main thesis of this paper are described, with answers to those objections.

ACS Style

J Gerard Wolff. Evidence for information compression via the matching and unification of patterns in the workings of brains and nervous systems. 2017, 1 .

AMA Style

J Gerard Wolff. Evidence for information compression via the matching and unification of patterns in the workings of brains and nervous systems. . 2017; ():1.

Chicago/Turabian Style

J Gerard Wolff. 2017. "Evidence for information compression via the matching and unification of patterns in the workings of brains and nervous systems." , no. : 1.

Preprint
Published: 01 June 2017
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In this paper, it is argued that, despite its many strengths and undoubted value in science, mathematics also has some weaknesses as a handmaiden for science. There appears to be potential in developing a new mathematics for science (NMFS) which is designed to overcome those weaknesses and to meet more fully the needs of science. As a background to the proposals: 1) The conclusions of a companion paper are noted - that mathematics is, fundamentally, a set of techniques for the compression of information and the application of those techniques; 2) That the effectiveness of mathematics in science is because it provides a means of achieving the compression of information which lies at the heart of science; 3) Since science and mathematics are products of the human intellect, it should not be surprising to find that the workings of the human mind has an influence on both of them; and 4) The significance of information compression in science and mathematics is in line with an abundance of other evidence for the significance of information compression in human learning, perception, and thinking. Continuing the theme of information compression as a unifying principle, the SP theory of intelligence and its realisation in the SP computer model demonstrate how diverse aspects of intelligence may be modelled via information compression within the powerful framework of multiple alignment. An NMFS is proposed, created as an amalgamation of mathematics as it is today with the SP system as it is today, including developments in both areas that are anticipated now. It is envisaged that, for reasons described in the paper, the proposed NMFS will overcome several of the apparent weaknesses in mathematics. In several sections, there is discussion of some possible implications for science of the proposed NMFS, including: potential for the long-sought-after unification of quantum mechanics and general relativity; expansion of the concept of "object" in physics; ambiguity in human perception as an analogy for the concept of superposition in quantum mechanics; the phenomenon of discontinuous dependencies in natural languages as an analogy for nonlocality and entanglement in quantum mechanics; a "waterspout" interpretation for some of the two-slits experiments; potential advantages of the proposed NMFS in the realm of statistics; and its potential to be a vehicle for the representation of all kinds of scientific knowledge, with consequent benefits in the processing of that knowledge.

ACS Style

J Gerard Wolff. Towards a new mathematics for science. 2017, 1 .

AMA Style

J Gerard Wolff. Towards a new mathematics for science. . 2017; ():1.

Chicago/Turabian Style

J Gerard Wolff. 2017. "Towards a new mathematics for science." , no. : 1.

Hypothesis and theory article
Published: 03 November 2016 in Frontiers in Psychology
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The “SP theory of intelligence”, with its realisation in the “SP computer model”, aims to simplify and integrate observations and concepts across artificial intelligence, mainstream computing, mathematics, and human perception and cognition, with information compression as a unifying theme. This paper describes how abstract structures and processes in the theory may be realised in terms of neurons, their interconnections, and the transmission of signals between neurons. This part of the SP theory -- “SP-neural” -- is a tentative and partial model for the representation and processing of knowledge in the brain. Empirical support for the SP theory -- outlined in the paper -- provides indirect support for SP-neural. In the abstract part of the SP theory (SP-abstract), all kinds of knowledge are represented with “patterns”, where a pattern is an array of atomic symbols in one or two dimensions. In SP-neural, the concept of a ‘pattern’ is realised as an array of neurons called a “pattern assembly”, similar to Hebb's concept of a ‘cell assembly’ but with important differences. Central to the processing of information in SP-abstract is information compression via the matching and unification of patterns (ICMUP) and, more specifically, information compression via the powerful concept of “multiple alignment”, borrowed and adapted from bioinformatics. Processes such as pattern recognition, reasoning and problem solving are achieved via the building of multiple alignments, while unsupervised learning is achieved by creating patterns from sensory information and also by creating patterns from multiple alignments in which there is a partial match between one pattern and another. It is envisaged that, in SP-neural, short-lived neural structures equivalent to multiple alignments will be created via an inter-play of excitatory and inhibitory neural signals. It is also envisaged that unsupervised learning will be achieved by the creation of pattern assemblies from sensory information and from the neural equivalents of multiple alignments, much as in the non-neural SP theory -- and significantly different from the `Hebbian' kinds of learning which are widely used in the kinds of artificial neural network that are popular in computer science. The paper discusses several associated issues, with relevant empirical evidence.

ACS Style

J. Gerard Wolff. Information Compression, Multiple Alignment, and the Representation and Processing of Knowledge in the Brain. Frontiers in Psychology 2016, 7, 1584 .

AMA Style

J. Gerard Wolff. Information Compression, Multiple Alignment, and the Representation and Processing of Knowledge in the Brain. Frontiers in Psychology. 2016; 7 ():1584.

Chicago/Turabian Style

J. Gerard Wolff. 2016. "Information Compression, Multiple Alignment, and the Representation and Processing of Knowledge in the Brain." Frontiers in Psychology 7, no. : 1584.

Journal article
Published: 21 October 2014 in SpringerPlus
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The SP theory of intelligence aims to simplify and integrate concepts in computing and cognition, with information compression as a unifying theme. This article is about how the SP theory may, with advantage, be applied to the understanding of natural vision and the development of computer vision. Potential benefits include an overall simplification of concepts in a universal framework for knowledge and seamless integration of vision with other sensory modalities and other aspects of intelligence. Low level perceptual features such as edges or corners may be identified by the extraction of redundancy in uniform areas in the manner of the run-length encoding technique for information compression. The concept of multiple alignment in the SP theory may be applied to the recognition of objects, and to scene analysis, with a hierarchy of parts and sub-parts, at multiple levels of abstraction, and with family-resemblance or polythetic categories. The theory has potential for the unsupervised learning of visual objects and classes of objects, and suggests how coherent concepts may be derived from fragments. As in natural vision, both recognition and learning in the SP system are robust in the face of errors of omission, commission and substitution. The theory suggests how, via vision, we may piece together a knowledge of the three-dimensional structure of objects and of our environment, it provides an account of how we may see things that are not objectively present in an image, how we may recognise something despite variations in the size of its retinal image, and how raster graphics and vector graphics may be unified. And it has things to say about the phenomena of lightness constancy and colour constancy, the role of context in recognition, ambiguities in visual perception, and the integration of vision with other senses and other aspects of intelligence.

ACS Style

J. Gerard Wolff. Application of the SP theory of intelligence to the understanding of natural vision and the development of computer vision. SpringerPlus 2014, 3, 552 .

AMA Style

J. Gerard Wolff. Application of the SP theory of intelligence to the understanding of natural vision and the development of computer vision. SpringerPlus. 2014; 3 (1):552.

Chicago/Turabian Style

J. Gerard Wolff. 2014. "Application of the SP theory of intelligence to the understanding of natural vision and the development of computer vision." SpringerPlus 3, no. 1: 552.

Journal article
Published: 24 December 2013 in Information
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This article describes existing and expected benefits of the SP theory ofintelligence, and some potential applications. The theory aims to simplify and integrate ideasacross artificial intelligence, mainstream computing, and human perception and cognition,with information compression as a unifying theme. It combines conceptual simplicitywith descriptive and explanatory power across several areas of computing and cognition.In the SP machine—an expression of the SP theory which is currently realized in theform of a computer model—there is potential for an overall simplification of computingsystems, including software. The SP theory promises deeper insights and better solutions inseveral areas of application including, most notably, unsupervised learning, natural languageprocessing, autonomous robots, computer vision, intelligent databases, software engineering,information compression, medical diagnosis and big data. There is also potential inareas such as the semantic web, bioinformatics, structuring of documents, the detection ofcomputer viruses, data fusion, new kinds of computer, and the development of scientifictheories. The theory promises seamless integration of structures and functions within andbetween different areas of application. The potential value, worldwide, of these benefits andapplications is at least $190 billion each year. Further development would be facilitatedby the creation of a high-parallel, open-source version of the SP machine, available toresearchers everywhere.

ACS Style

J. Gerard Wolff. The SP Theory of Intelligence: Benefits and Applications. Information 2013, 5, 1 -27.

AMA Style

J. Gerard Wolff. The SP Theory of Intelligence: Benefits and Applications. Information. 2013; 5 (1):1-27.

Chicago/Turabian Style

J. Gerard Wolff. 2013. "The SP Theory of Intelligence: Benefits and Applications." Information 5, no. 1: 1-27.

Review
Published: 06 August 2013 in Information
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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.

ACS Style

J Gerard Wolff. The SP Theory of Intelligence: An Overview. Information 2013, 4, 283 -341.

AMA Style

J Gerard Wolff. The SP Theory of Intelligence: An Overview. Information. 2013; 4 (3):283-341.

Chicago/Turabian Style

J Gerard Wolff. 2013. "The SP Theory of Intelligence: An Overview." Information 4, no. 3: 283-341.