Year: 2015
Type: article
Abstract: Computational models of context that are compatible with human cognitive representations could be used to augment human decision-making and to enable improved interactive and cooperative capabilities of non-human agents. If unconstrained by human cognitive limitations and biases, such cognitively-inspired models of context could also scale to problem scales and time scales that exceed human capabilities. Expanding on a recently developed theoretical model for context called the Narratively Integrate Multi-level (NIM) framework, this paper presents computational details that operationalize the concepts in the NIM framework and define a set of tools that can be used to design and build human-like models of context and embed them into larger computational systems.
Source: Procedia Manufacturing
Authors: Wayne Zachary, Andrew Rosoff, Travis Thomas, Vassil Iordanov, Stephen Read, Lynn Miller
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
Funder: Office of Naval Research
Award: G8876996369
OpenAlex ID: W2327012035