Logical Formalizations of Commonsense Reasoning: A Survey
Authors: Ernest Davis
JAIR 2017 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | This paper surveys the use of logic-based representations of commonsense knowledge in artificial intelligence research. This paper surveys this line of research, focusing on the representations that have been developed for various commonsense domains, rather than on inference techniques. |
| Researcher Affiliation | Academia | Ernest Davis EMAIL Department of Computer Science, New York University 251 Mercer St. New York, NY 10012 USA |
| Pseudocode | No | The paper discusses various logical formalisms and representations (e.g., propositional logic, predicate calculus, modal logic, situation calculus, event calculus) and presents them using tables of axioms, predicates, or example sentences. It does not feature any section explicitly titled "Pseudocode" or "Algorithm," nor does it present structured steps of an algorithm in a code-like format. For example, tables like "Table 1: Axioms from a propositional encoding of a blocks-world planning problem" contain logical statements, not algorithmic steps. |
| Open Source Code | No | The paper is a survey and does not describe a novel methodology for which code would be released. There are no statements about releasing code or links to repositories for the work presented in this paper. |
| Open Datasets | No | The paper is a survey of logical formalizations and does not use datasets for empirical evaluation. It refers to "Sample 1: On a mundane morning in late summer in Paris, the impossible happened. The Mona Lisa vanished." and "Sample 2: In allopatric speciation... (see figure [1]). Campbell Biology (Reece et al., 2011)" as examples to illustrate commonsense reasoning problems, but these are not presented as datasets with access information. |
| Dataset Splits | No | This paper is a theoretical survey and does not involve empirical experiments with datasets, thus it does not describe any training, validation, or test splits. |
| Hardware Specification | No | This paper is a theoretical survey of logical formalizations of commonsense reasoning and does not involve experimental runs requiring specific hardware specifications. |
| Software Dependencies | No | This paper is a theoretical survey and does not detail any experimental implementations or list specific software dependencies with version numbers for its own methodology. It mentions programming languages and systems (e.g., "Prolog", "CPLEX", "Gecode") in the context of discussing other research or general concepts, but not as dependencies for the work described in this paper. |
| Experiment Setup | No | This paper is a theoretical survey and does not involve empirical experiments, therefore it does not provide details on experimental setup, hyperparameters, or training configurations. |