scispace - formally typeset
Search or ask a question
Topic

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.


Papers
More filters
Book ChapterDOI
01 Jan 2023
Book ChapterDOI
01 Jan 2020
TL;DR: In recent years, design thinking has become a buzzword for disruptive user-centered innovation and addresses diverse shortcomings of analytical strategy development methods in a dynamic and fast-paced business environment.
Abstract: In recent years, design thinking has become a buzzword for disruptive user-centered innovation. Design thinking is an abductive and iterative approach transforming observations and related insights into practical validated solutions. Abductive reasoning starts with a set of abstractions, that is, an incomplete set of observations, and seeks for the simplest and most likely solution. The initial solution is then improved upon through inference until it becomes a robust solution. Design thinking addresses diverse shortcomings of analytical strategy development methods in a dynamic and fast-paced business environment. It aims at learning from methodologies used by designers, such as architects, artists, or creative directors, to solve problems which are incomplete by nature and cannot be solved by traditional linear problem-solving approaches. Design thinking in the context of strategy development proceeds in four steps, that is, observing, learning, designing, and validating, by iterating through divergent and convergent thinking. This leads to unique value design thinking offers towards developing strategies that work by taking a customer-centric viewpoint.
Proceedings Article
22 Jul 2012
TL;DR: This research seeks to answer the question of how any agent that is tasked with making sense of its world, by finding explanations for evidence using domain-general strategies, may accurately and efficiently handle incomplete evidence, noisy evidence, and an incomplete knowledge base.
Abstract: My research seeks to answer the question of how any agent that is tasked with making sense of its world, by finding explanations for evidence (e.g., sensor reports) using domain-general strategies, may accurately and efficiently handle incomplete evidence, noisy evidence, and an incomplete knowledge base. I propose the following answer to the question. The agent should employ an optimal abductive reasoning algorithm (developed piece-wise and shown to be best in a class of similar algorithms) that allows it to reason from evidence to causes. For the sake of efficiency and operational concerns, the agent should establish beliefs periodically rather than waiting until it has obtained all evidence it will ever be able to obtain. If the agent commits to beliefs on the basis of incomplete or noisy evidence or an incomplete knowledge base, these beliefs may be incorrect. Future evidence obtained by the agent may result in failed predictions or anomalies. The agent is then tasked with determining whether it should retain its beliefs and therefore discount the newly-obtained evidence, revise its prior beliefs, or expand its knowledge base (what can be described as anomaly-driven or explanation-based learning).
Journal ArticleDOI
A. A. Müller1
TL;DR: The path from abductive inference to an information retrieval system is described and the prototype system is demonstrated by tracing an example of building and querying a semantic model derived from an SGML-structured document collection on art and artists.
Journal ArticleDOI
TL;DR: This paper describes how heuristic rules and device models can be represented and revised in a logic programming framework and demonstrates how logic programming can be extended to perform abductive reasoning in addition to deductive reasoning.
Abstract: This paper discusses learning in the context of a diagnostic expert system. The diagnostic expert system is an example of a generate-and-test problem solver. Fault diagnosis heuristics (i.e. logical implications representing association between unusual features and failed components) hypothesize potential faults. The potential faults are verified or denied by comparing the predictions of a qualitative simulation to observe data. Learning in this context consists of modifying the fault diagnosis heuristics. This paper describes how heuristic rules and device models can be represented and revised in a logic programming framework. In addition, we demonstrate how logic programming can be extended to perform abductive reasoning in addition to deductive reasoning. Finally, we compare failure-driven learning and learning from successes for acquiring fault diagnosis heuristics via explanation-based learning.

Network Information
Related Topics (5)
Natural language
31.1K papers, 806.8K citations
82% related
Ontology (information science)
57K papers, 869.1K citations
79% related
Inference
36.8K papers, 1.3M citations
76% related
Heuristics
32.1K papers, 956.5K citations
76% related
Social network
42.9K papers, 1.5M citations
75% related
Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202356
2022103
202156
202059
201956
201867