<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Colin Kupitz</style></author><author><style face="normal" font="default" size="100%">Aaron Eberhart</style></author><author><style face="normal" font="default" size="100%">Daniel Schmidt</style></author><author><style face="normal" font="default" size="100%">Christopher Stevens</style></author><author><style face="normal" font="default" size="100%">Cogan Shimizu</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author><author><style face="normal" font="default" size="100%">Dario Salvucci</style></author><author><style face="normal" font="default" size="100%">Benji Maruyama</style></author><author><style face="normal" font="default" size="100%">Chris Myers</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Toward Undifferentiated Cognitive Models</style></title><secondary-title><style face="normal" font="default" size="100%">International Conference on Cognitive Modeling</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2021</style></year></dates><edition><style face="normal" font="default" size="100%">19</style></edition><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Autonomous systems are a new frontier for pushing sociotechnical advancement. Such systems will eventually become pervasive, involved in everything from manufacturing, healthcare, defense, and even research itself. However, proliferation is stifled by the high development costs and the resulting inflexibility of the produced systems. The current time needed to create and integrate state of the art autonomous systems that operate as team members in complex situations is a 3-15 year development period, often requiring humans to adapt to limitations in the resulting systems. A new research thrust in interactive task learning (ITL) has begun, calling for natural human-autonomy interaction to facilitate system flexibility and minimize users’ complexity in providing autonomous systems with new tasks. We discuss the development of an undifferentiated agent with a modular framework as a method of approaching that goal.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Aaron Eberhart</style></author><author><style face="normal" font="default" size="100%">Cogan Shimizu</style></author><author><style face="normal" font="default" size="100%">Christopher Stevens</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author><author><style face="normal" font="default" size="100%">Christopher W. Myers</style></author><author><style face="normal" font="default" size="100%">Benji Maruyam</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A Domain Ontology for Task Instructions</style></title><secondary-title><style face="normal" font="default" size="100%">KGSWC</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2020</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%"> Knowledge graphs and ontologies represent information in a variety of different applications. One use case, the Intelligence, Surveillance, &amp; Reconnaissance: Mutli-Attribute Task Battery (ISR-MATB), comes from Cognitive Science, where researchers use interdisciplinary methods to understand the mind and cognition. The ISR-MATB is a set of tasks that a cognitive or human agent perform which test visual, 
 auditory, and memory capabilities. An ontology can represent a cognitive agent’s background knowledge of the task it was instructed to perform and act as an interchange format between different Cognitive Agent tasks similar to ISR-MATB. We present several modular patterns for representing ISR-MATB task instructions, as well as a unified diagram that links them together.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>27</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Cogan Shimizu</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author><author><style face="normal" font="default" size="100%">Aaron Eberhart</style></author><author><style face="normal" font="default" size="100%">Quinn Hirt</style></author><author><style face="normal" font="default" size="100%">Christopher Stevens</style></author><author><style face="normal" font="default" size="100%">Christopher W. Myers</style></author><author><style face="normal" font="default" size="100%">Benji Maruyama</style></author><author><style face="normal" font="default" size="100%">Colin Kupitz</style></author><author><style face="normal" font="default" size="100%">Dario Salvucci</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">An Ontology of Instruction 1.0</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2020</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record></records></xml>