Abductive Reasoning and Second Language Learning

A Hybrid Learning Model of Abductive Reasoning

Multicausal abductive tasks appear to have deliberate and implicit components: people generate and modify explanations using a series of recognizable steps, but these steps appear to be guided by an implicit hypothesis evaluation process. This paper proposes a hybrid symbolic-connectionist learning architecture for multicausal abduction. The architecture tightly integrates a symbolic Soar model...

Learning Abductive Reasoning Using Random Examples

We consider a new formulation of abduction. Our formulation differs from the existing approaches in that it does not cast the “plausibility” of explanations in terms of either syntactic minimality or an explicitly given prior distribution. Instead, “plausibility,” along with the rules of the domain, is learned from concrete examples (settings of attributes). Our version of abduction thus falls ...

Robust Textual Inference Via Learning and Abductive Reasoning

We present a system for textual inference (the task of inferring whether a sentence follows from another text) that uses learning and a logical-formula semantic representation of the text. More precisely, our system begins by parsing and then transforming sentences into a logical formula-like representation similar to the one used by (Harabagiu et al., 2000). An abductive theorem prover then tr...

Combining abductive reasoning and inductive learning to evolve requirements specifications

Tunneling Neural Perception and Logic Reasoning through Abductive Learning

Perception and reasoning are basic human abilities that are seamlessly connected as part of human intelligence. However, in current machine learning systems, the perception and reasoning modules are incompatible. Tasks requiring joint perception and reasoning ability are difficult to accomplish autonomously and still demand human intervention. Inspired by the way language experts decoded Mayan ...