Five Dimensions of Reasoning in the Wild
Authors: Don Perlis
AAAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Reasoning is one of the oldest topics in artificial intelligence (AI). And it has made lots of progress, in the form of commonsense reasoning (CSR), planning, automated theorem-proving, and more. But I suspect it has hit a barrier that must be surmounted if we are to approach anything like human-level inference. Here I give evidence for such a barrier, and ideas about dealing with it, loosely based on evidence from human behavior. In rough synopsis, reasoning does not work well when done in isolation from its broader significance, both for the needs and interests of an agent and for the wider world. |
| Researcher Affiliation | Academia | University of Maryland, College Park MD perlis@cs.umd.edu |
| Pseudocode | No | The paper does not contain any 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 | This is a theoretical paper and does not involve the use of datasets for training or evaluation. Therefore, there is no information about public dataset availability. |
| Dataset Splits | No | This is a theoretical paper and does not involve experiments with dataset splits. Therefore, there is no information about validation splits. |
| Hardware Specification | No | This is a theoretical paper and does not describe any experiments that would require hardware specifications. |
| Software Dependencies | No | This is a theoretical paper and does not describe any software implementation or dependencies. |
| Experiment Setup | No | This is a theoretical paper and does not describe any experimental setup or hyperparameters. |