Knowledge Representation and Reasoning: What’s Hot
Authors: Chitta Baral, Giuseppe De Giacomo
AAAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Knowledge representation and reasoning (KR) stems from a deep tradition in logic. In particular, it aims at building systems that know about their world and are able to act in an informed way in it, as humans do. ... Research in KR is currently present in the major AI generalist conferences like the International Joint Conference on Artificial Intelligence (IJCAI), the AAAI Conference on Artificial Intelligence (AAAI), and European Conference on Artificial Intelligence (ECAI). The series of International Conferences on Principles of Knowledge Representation and Reasoning (KR) is one of the most scientifically respected conferences in AI. ... The papers presented at KR 2014 can be broadly divided into the following areas: Description Logics, Reasoning about Actions and Processes; Belief Revision and Nonmonotonicity; General Knowledge Representation and Reasoning; Planning, Strategies, and Diagnosis; Answer Set Programming and Logic Programming; Argumentation; Automated Reasoning and Computation; Causality; and Rationality and Uncertainty. |
| Researcher Affiliation | Academia | Chitta Baral Arizona State University Arizona, USA chitta@asu.edu Giuseppe De Giacomo Sapienza University of Rome Rome, Italy degiacomo@dis.uniroma1.it |
| Pseudocode | No | The paper is a survey of the field of Knowledge Representation and Reasoning and does not contain any pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not present any specific methodology with associated source code and therefore does not provide any links or statements about open-source code for the work described in the paper. The only link provided is to a workshop website (http://krnsfworkshop.cs.illinois.edu/). |
| Open Datasets | No | The paper mentions 'various available knowledge bases that can be and are being used in developing NLU systems (Ovchinnikova 2014)' but does not use or provide access information for a dataset as part of its own work. |
| Dataset Splits | No | The paper does not report on experiments using datasets and therefore provides no dataset split information. |
| Hardware Specification | No | The paper is a survey and does not describe any experiments that would require specific hardware specifications. |
| Software Dependencies | No | The paper is a survey and does not describe any experiments that would require specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is a survey and does not present an experimental setup or details like hyperparameter values. |