Abductive reasoning

About: Abductive reasoning is a research topic. Over the lifetime, 1917 publications have been published within this topic receiving 44645 citations. The topic is also known as: abduction & abductive inference.

The SP Theory of Intelligence as a Foundation for the Development of a General, Human-Level Thinking Machine.

Abstract: This paper summarises how the "SP theory of intelligence" and its realisation in the "SP computer model" simplifies and integrates concepts across artificial intelligence and related areas, and thus provides a promising foundation for the development of a general, human-level thinking machine, in accordance with the main goal of research in artificial general intelligence. The key to this simplification and integration is the powerful concept of "multiple alignment", borrowed and adapted from bioinformatics. This concept has the potential to be the "double helix" of intelligence, with as much significance for human-level intelligence as has DNA for biological sciences. Strengths of the SP system include: versatility in the representation of diverse kinds of knowledge; versatility in aspects of intelligence (including: strengths in unsupervised learning; the processing of natural language; pattern recognition at multiple levels of abstraction that is robust in the face of errors in data; several kinds of reasoning (including: one-step `deductive' reasoning; chains of reasoning; abductive reasoning; reasoning with probabilistic networks and trees; reasoning with 'rules'; nonmonotonic reasoning and reasoning with default values; Bayesian reasoning with 'explaining away'; and more); planning; problem solving; and more); seamless integration of diverse kinds of knowledge and diverse aspects of intelligence in any combination; and potential for application in several areas (including: helping to solve nine problems with big data; helping to develop human-level intelligence in autonomous robots; serving as a database with intelligence and with versatility in the representation and integration of several forms of knowledge; serving as a vehicle for medical knowledge and as an aid to medical diagnosis; and several more).

Hypothesis generation for management intelligence

Abstract: Investigation of the role of hypothesis formation in complex (business) problem solving has resulted in a new approach to hypothesis generation A prototypical hypothesis generation paradigm for management intelligence has been developed, reflecting a widespread need to support management in such areas as fraud detection and intelligent decision making This thesis presents this new paradigm and its application to goal directed problem solving methodologies, including case based reasoning The hypothesis generation model, which is supported by a dynamic hypothesis space, consists of three components -anomaly detection, abductive reasoning, and conflict resolution modelling

Research on the Case-based Reasoning

Abstract: The case-based reasoning system plays a very important role in the research on the AI(artificial intelligence).This paper researches on the structure of the case-based reasoning,expounds the corresponding functions of the structure,and applies some data mining algorithms in the case-based reasoning,which makes the case-based reasoning trend to intellectualization.

A robust logic for rule-based reasoning under uncertainty

Abstract: A symbolically quantified logic is presented for reasoning under uncertainty that is based upon the concept of rough sets. This mathematical model provides a simple yet sound basis for a robust reasoning system. A rule of inference analogous to modus ponens is described, and it is shown how it might be used by a reasoning system to determine the most likely outcome under conditions of uncertain knowledge. An analysis of the robustness of the logic in rule-based reasoning is also presented. >

Artificial Creativity and Self-Evolution: Abductive Reasoning in Artificial Life Forms

Abstract: What contributions can Cognitive Science offer to the understanding of nature of providing creativity to artificial life forms? For this discussion, it is necessary to investigate creative processes from a mechanistic perspective as well as involve subjective elements which cannot, in principle, be described from this perspective. These two basic approaches will be investigated here, focusing the artificial creative process on the nature of artificial abductive reasoning. As an initial hypothesis we will characterize creativity as a self-organizing process in which abductive reasoning occurs using self-organizing, semantic topical maps in conjunction with an abductive neural network, allowing the creation and expansion of a well-structured set of beliefs within the artificial system. This process is considered here as part of the establishment of order parameters in the flow of information available to allow artificial life forms to self-organize and infer on sensory information. In this sense, we will argue that a deeper understanding of how self-organizing processes involving abductive reasoning may take place in artificial dynamic systems, and how this can assist in the creation of an artificial creative process within an artificially intelligent artificial life form we refer to as a Synthetic, Evolving Life Form (SELF).