Model-free, Model-based, and General Intelligence
Authors: Hector Geffner
IJCAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, I review developments in AI and draw on these theories to discuss the gap between model-free learners and model-based solvers, a gap that needs to be bridged in order to have intelligent systems that are robust and general. The goal of this paper is to place these developments in perspective, in particular by comparing model-free learners with model-based solvers. |
| Researcher Affiliation | Academia | Hector Geffner1,2 1 Universitat Pompeu Fabra, Roc Boronat 138, 08032 Barcelona, Spain 2 ICREA, Pg. Llu ıs Companys 23, 08010 Barcelona, Spain hector.geffner@upf.edu |
| Pseudocode | No | The paper does not contain any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement or link indicating that open-source code for the described methodology is available. |
| Open Datasets | No | The paper does not conduct new experiments and thus does not refer to a dataset used or made publicly available by the authors of this paper. |
| Dataset Splits | No | The paper does not perform new experiments and therefore does not specify training, validation, or test dataset splits. |
| Hardware Specification | No | The paper does not explicitly describe the hardware used to run its experiments, as it is a conceptual review. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers, as it is a conceptual review. |
| Experiment Setup | No | The paper does not provide details about an experimental setup, such as hyperparameters or system-level training settings, as it is a conceptual review. |