Rational Inference Patterns Based on Conditional Logic
Authors: Christian Eichhorn, Gabriele Kern-Isberner, Marco Ragni
AAAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper we define inference patterns as a formalization of the joint usage or avoidance of these rules. Considering patterns instead of single inferences opens the way for categorizing inference studies with regard to their qualitative results. We apply plausibility relations which provide basic formal models for many theories of conditionals, nonmonotonic reasoning, and belief revision to asses the rationality of the patterns and thus the individual inferences drawn in the study. By this replacement of classical logic with formalisms most suitable for conditionals, we shift the basis of judging rationality from compatibility with classical entailment to consistency in a logic of conditionals. Using inductive reasoning on the plausibility relations we reverse engineer conditional knowledge bases as explanatory model for and formalization of the background knowledge of the participants. |
| Researcher Affiliation | Academia | Christian Eichhorn, Gabriele Kern-Isberner Chair 1 of Computer Science TU Dortmund University, Dortmund, Germany christian.eichhorn@tu-dortmund.de gabriele.kern-isberner@cs.tu-dortmund.de Marco Ragni Cognitive Computation Lab University of Freiburg, Freiburg, Germany ragni@informatik.uni-freiburg.de |
| Pseudocode | Yes | Algorithm: Explanation Generator Input: Inference pattern ϱ R Output: Knowledge base Δ (L|L) L|L 1. Set up Δ with a conditional for each rule in pattern ϱ according to Table 5. 2. Set up the system of inequalities (4 ) for Δ and simplify: For each inequality that is implied by the other inequalities, remove the line from the system of inequalities and the respective conditional from Δ to obtain the (wrt. set inclusion) minimal explaining knowledge base Δ. 3. Return the knowledge base Δ. |
| Open Source Code | No | No, the paper does not include any explicit statements about releasing source code or provide links to a code repository for the methodology described. |
| Open Datasets | No | No, the paper references previously published psychological studies (e.g., Byrne 1989, Thompson and Byrne 2002) as examples, but it does not introduce or provide access to a new or existing publicly available dataset for its own analysis. |
| Dataset Splits | No | No, the paper analyzes existing psychological findings and does not describe experiments that would involve training, validation, or test splits of a dataset. |
| Hardware Specification | No | No, the paper describes a theoretical and formal analysis. It does not mention any specific hardware used for computational work, such as GPU or CPU models. |
| Software Dependencies | No | No, the paper does not list specific software dependencies with version numbers (e.g., programming languages or libraries like Python 3.8, PyTorch 1.9) required to replicate any computational aspects. |
| Experiment Setup | No | No, the paper focuses on theoretical formalization and analysis, not empirical experiments. Therefore, it does not provide details about an experimental setup, such as hyperparameters or training configurations. |