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Bridging Macrocognitive/Microcognitive Methods: ACT-R Under Review
Published in Schraagen Jan Maarten, Laura G. Militello, Tom Ormerod, Lipshitz Raanan, Naturalistic Decision Making and Macrocognition, 2017
Schraagen Jan Maarten, Laura G. Militello, Tom Ormerod, Lipshitz Raanan
Building upon Klein et al.’s (2003) earlier suggestion of theoretical connections existing across macrocognitive and microcognitive levels, we suggest that the difficulties associated with RPD validation may be addressed by utilizing microcognitive modeling architectures. We propose ACT-R (Atomic Components of Thought-Rational) to be a constructive research tool that may inform RPD validation efforts with a higher degree of precision than methods currently available. ACT-R is a computational architecture that is designed to support the modeling of human cognition and performance (Anderson and Lebière, 1998). In short, it is a production system that operates via a series of production rules (condition-action pairs) that embody procedural knowledge and declarative structures. Our choice in ACT-R as a modeling environment resides in the findings of a recent study of offshore installation emergencies, which found that decision making was characterized by serially generated condition-action pairs (Skriver, 1998).
Cognitive Architectures
Published in Ron Fulbright, Democratization of Expertise, 2020
As shown in Fig. 6-10, the Adaptive Control of Thought-Rational (ACT-R) architecture was developed by John R. Anderson and Christian Lebiere at Carnegie Mellon University (Anderson, 2013). Work leading to ACT-R began in the 1970s influenced by Allen Newell and the idea of developing unified theory of cognition by studying human cognition.
Cognitive model of phonological awareness focusing on errors and formation process through Shiritori
Published in Advanced Robotics, 2022
Jumpei Nishikawa, Junya Morita
Based on various cognitive architectures that have been developed, we have selected ACT-R [12] for this study. As ACT-R is based on psychological experiments with regard to thought process and memory, it enables us to comprehensively grasp various phenomena related to human cognition. The ACT-R structure is represented as a production system with multiple modules, where the central production module controls other modules. In addition, various parameters specify the behavior of the modules, facilitating the modeling of varieties of individuals. The current study focuses on the knowledge representation that can be expressed as discrete symbols provided by the modeler. It would be useful to consider how to map continuous sounds with phonological units (i.e. symbols) based on the knowledge representation in ACT-R to explain phonological awareness through modeling.
Scaffolding the Mastery of Healthy Behaviors with Fittle+ Systems: Evidence-Based Interventions and Theory
Published in Human–Computer Interaction, 2021
Peter Pirolli, G. Michael Youngblood, Honglu Du, Artie Konrad, Les Nelson, Aaron Springer
The ACT-R theory (Anderson, 2007) provides a deeper understanding of the dual-system framework for habit formation. We have used ACT-R to develop refined predictive computational models of our interventions for self-efficacy and implementation intentions. ACT-R (Anderson, 2007; Anderson et al., 2004) is a unified theory of how the structure and dynamics of the brain give rise to the functioning of the mind. The ACT-R simulation environment is a computational architecture that supports the development of models.
On the quantification of human-robot collaboration quality
Published in International Journal of Computer Integrated Manufacturing, 2023
George Kokotinis, George Michalos, Zoi Arkouli, Sotiris Makris
Last but not least, an equally important role in the evaluation of a collaborative cell has been identified for the efficient and harmonious Human-Robot Interaction (HRI). Under this concept, various studies have focused on the way that both resources can interact, proposing novel methods and technologies to enhance efficiency, but not aiming for a fast evaluation of the benefits and the quality that a proper human-robot interaction can offer to a collaborative system. In (Cirillo, Karlsson, and Saffiotti 2008), a framework for human-aware planning is presented in which three kinds of human-robot interaction approaches are examined, implicit cooperation, robot supportive role, and robot asking for feedback. The main purpose of the proposed approach is to generate a sequence of robot actions, that takes into account the status of the environment, the goals of the robot, and the forecasted plan of the human. Another study focused on the design and evaluation of Chaski, a task-level execution system that enables a robot to collaboratively execute a shared plan with a person, using insights from human-human teaming to make human-robot teaming more natural and fluid (Shah and Wiken 2011). Furthermore, research on robot decisional abilities has been conducted, taking into account explicit reasoning on the human environment and on the robot’s capacities to achieve its tasks, building a supervision system called SHARY (Supervision for Human-Robot (Y)Interaction) (Clodic et al. 2019). An interaction infrastructure called the ``Human-Robot Interaction Operating System’’ (HRI/OS) was developed by (Fong et al. 2006), aiming at building human-robot teams where they are able to work effectively together and converse about abilities, goals, and achievements. In (Trafton et al. 2013), ACT-R/E has been presented as a cognitive architecture that is used for human-robot interaction tasks, simulating how humans think, perceive, and act in the real world.