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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.


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Journal ArticleDOI
TL;DR: By considering abductive inference as a constraint optimization problem, the novel approach improves performance dramatically when a BN's conditional probability tables contain a significant number of zeros, and significantly outperforms the traditional approach.
Abstract: Constraints occur in many application areas of interest to evolutionary computation. The area considered here is Bayesian networks (BNs), which is a probability-based method for representing and reasoning with uncertain knowledge. This work deals with constraints in BNs and investigates how tournament selection can be adapted to better process such constraints in the context of abductive inference. Abductive inference in BNs consists of finding the most probable explanation given some evidence. Since exact abductive inference is NP-hard, several approximate approaches to this inference task have been developed. One of them applies evolutionary techniques in order to find optimal or close-to-optimal explanations. A problem with the traditional evolutionary approach is this: As the number of constraints determined by the zeros in the conditional probability tables grows, performance deteriorates because the number of explanations whose probability is greater than zero decreases. To minimize this problem, this paper presents and analyzes a new evolutionary approach to abductive inference in BNs. By considering abductive inference as a constraint optimization problem, the novel approach improves performance dramatically when a BN's conditional probability tables contain a significant number of zeros. Experimental results are presented comparing the performances of the traditional evolutionary approach and the approach introduced in this work. The results show that the new approach significantly outperforms the traditional one.

7 citations

Book ChapterDOI
28 Apr 1993
TL;DR: This system aims to be flexible and overcome the shortcomings in its knowledge base by contextual reasoning that deals with the enablements and requirements for communication in information-seeking dialogues.
Abstract: This paper discusses the planning of system responses in information-seeking dialogues. Many dialogue systems are capable of answering single questions or carrying out dialogues which have fairly fixed structures, but they show little or no capability to continue the dialogue in an intelligent way, if something unexpected takes place. Our system aims to be flexible and overcome the shortcomings in its knowledge base by contextual reasoning that deals with the enablements and requirements for communication. Dialogue is regarded as a negotiation and the most appropriate response in the context is determined by communicative principles that are considered as constraints on cooperative and coherent communication. The prototype system is based on the knowledge base update procedure developed by Guessoum and Lloyd (1990, 1991), and it is a part of the Dialogue Manager in the PLUS system.

7 citations

Proceedings ArticleDOI
22 Sep 2010
TL;DR: In this paper, the authors present an inference framework for default reasoning using subjective logic theory, which is a relatively new branch of probabilistic logic that allows explicit representation of ignorance about knowledge in a model called subjective opinion.
Abstract: In forensic analysis of visual surveillance data, ‘default reasoning’ can play an important role for deriving plausible semantic conclusions under incomplete and contradictory information about scenes. In this paper, we present an inference framework for default reasoning using Subjective Logic theory. Subjective Logic is a relatively new branch of probabilistic logic that allows explicit representation of ignorance about knowledge in a model called subjective opinion and that also comes with a rich set of operators thereby, having big potential as a tool for belief representation and reasoning. However, its application to visual surveillance is in its infancy and its use for default reasoning is not reported yet. Therefore, the aim of this paper is to bestow the ability of default reasoning on subjective logic and show the feasibility of using the introduced inference framework for visual surveillance. Among the approaches to enable default reasoning, the Bilattice framework is one that is well known and demonstrated for visual surveillance. For deriving the usage of subjective logic for default reasoning, we first discuss the similarity between the partial ignorance concept in subjective logic and the concept of degree of information in Bilattice based structure for multivalued default logic. Then we introduce the inference mechanism for default reasoning by mapping multi-logic-values into subjective opinion and combining operators in subjective logic. Finally, we present some illustrative reasoning examples in typical visual surveillance scenarios.

7 citations

Patent
15 Sep 2011
TL;DR: An intelligent plant development library environment is presented in this article, which includes an EPC knowledge system capable of incorporating know-how of one or more construction firms in the form of assembly objects.
Abstract: An intelligent plant development library environment is presented. Contemplated environments comprise an EPC knowledge system capable of incorporating know-how of one or more construction firms in the form of assembly objects. Assembly objects represent construction components (e.g., bolts, cable trays, pipes, processing units, deliverables, etc.) that can be incorporated into a plant design. Assembly objects are stored in an assembly database and include available contexts considered relevant to the assembly objects. An inference engine is utilized to derive a specified context related to a plant design from one or more design tools. The inference engine applies rule sets to infer which assembly objects to instantiate as construction objects. The inference engine can further configure the design tools to incorporate the instantiated construction objects into a plant construction project model. Example rule sets include forward chaining rules, backward chaining rules, case-based reasoning rules, inductive reasoning rules, or abductive reasoning rules.

7 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202356
2022103
202156
202059
201956
201867