Contextual Conditional Reasoning
Authors: Giovanni Casini, Thomas Meyer, Ivan Varzinczak6254-6261
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We extend the expressivity of classical conditional reasoning by introducing context as a new parameter. The enriched conditional logic generalises the defeasible conditional setting in the style of Kraus, Lehmann, and Magidor, and allows for a refined semantics that is able to distinguish, for example, between expectations and counterfactuals. In this paper we introduce the language for the enriched logic and define an appropriate semantic framework for it. We analyse which properties generally associated with conditional reasoning are still satisfied by the new semantic framework, provide a suitable representation result, and define an entailment relation based on Lehmann and Magidor s generally-accepted notion of Rational Closure. and Space considerations prevent us from presenting an algorithm for deciding entailment within our framework. |
| Researcher Affiliation | Academia | Giovanni Casini,1,4 Thomas Meyer, 2,4 Ivan Varzinczak 3,4 1 ISTI CNR, Italy 2 University of Cape Town 3 CRIL, Univ. Artois & CNRS, France 4 CAIR, South Africa giovanni.casini@isti.cnr.it, tmeyer@cs.uct.ac.za, varzinczak@cril.fr |
| Pseudocode | No | The paper describes theoretical concepts, logical definitions, and theorems but does not include any structured pseudocode or algorithm blocks. The authors explicitly state, 'Space considerations prevent us from presenting an algorithm for deciding entailment within our framework.' |
| Open Source Code | No | The paper does not contain any statements regarding the release of open-source code or links to a code repository for the methodology described. |
| Open Datasets | No | This paper is theoretical and does not involve empirical training on a dataset. No dataset information is provided. |
| Dataset Splits | No | This paper is theoretical and does not perform experiments requiring dataset splits. No information about validation splits is provided. |
| Hardware Specification | No | This paper is theoretical and does not describe any experiments requiring hardware. No hardware specifications are provided. |
| Software Dependencies | No | This paper is theoretical and does not describe software implementations or experiments with specific software dependencies and version numbers. |
| Experiment Setup | No | This paper is theoretical and does not present an experimental setup, hyperparameters, or training configurations. |