An Abstract View on Modularity in Knowledge Representation
Authors: Yuliya Lierler, Miroslaw Truszczynski
AAAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We introduce model-based modular systems, an abstract framework for modular knowledge representation formalisms, similar in scope to multi-context systems but employing a simpler information-flow mechanism. We establish the precise relationship between the two frameworks, showing that they can simulate each other. We demonstrate that recently introduced modular knowledge representation formalisms integrating logic programming with satisfiability and, more generally, with constraint satisfaction can be cast as modular systems in our sense. These results show that our formalism offers a simple unifying framework for studies of modularity in knowledge representation. |
| Researcher Affiliation | Academia | Yuliya Lierler Department of Computer Science University of Nebraska at Omaha Omaha, NE 68182, USA ylierler@unomaha.edu Miroslaw Truszczynski Department of Computer Science University of Kentucky Lexington, KY 40506, USA mirek@cs.uky.edu |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any concrete access to source code for the methodology described. |
| Open Datasets | No | The paper is theoretical and does not use or refer to any datasets for training or evaluation. |
| Dataset Splits | No | The paper is theoretical and does not report on experiments, thus no dataset split information (train/validation/test) is provided. |
| Hardware Specification | No | The paper is theoretical and does not involve computational experiments, thus no hardware specifications are mentioned. |
| Software Dependencies | No | The paper is theoretical and does not involve computational experiments, thus no specific software dependencies with version numbers are mentioned. |
| Experiment Setup | No | The paper is theoretical and does not describe any experimental setup details such as hyperparameters or training configurations. |