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Explicit Rule Learning : = A Cognitive Tutorial Method to Train Users of Artificial Intelligence/Machine Learning Systems.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Explicit Rule Learning :/
其他題名:	A Cognitive Tutorial Method to Train Users of Artificial Intelligence/Machine Learning Systems.
作者:	Linja, Anne.
面頁冊數:	1 online resource (203 pages)
附註:	Source: Dissertations Abstracts International, Volume: 85-03, Section: A.
Contained By:	Dissertations Abstracts International85-03A.
標題:	Cognitive psychology. -
電子資源:	click for full text (PQDT)
ISBN:	9798380372947

Explicit Rule Learning : = A Cognitive Tutorial Method to Train Users of Artificial Intelligence/Machine Learning Systems.
Linja, Anne.

Explicit Rule Learning :A Cognitive Tutorial Method to Train Users of Artificial Intelligence/Machine Learning Systems. - 1 online resource (203 pages)

Source: Dissertations Abstracts International, Volume: 85-03, Section: A.

Thesis (Ph.D.)--Michigan Technological University, 2023.

Includes bibliographical references

Today's intelligent software systems, such as Artificial Intelligence/Machine Learning systems, are sophisticated, complicated, sometimes complex systems. In order to effectively interact with these systems, novice users need to have a certain level of understanding. An awareness of a system's underlying principles, rationale, logic, and goals can enhance the synergistic human-machine interaction. It also benefits the user to know when they can trust the systems' output, and to discern boundary conditions that might change the output. The purpose of this research is to empirically test the viability of a Cognitive Tutorial approach, called Explicit Rule Learning. Several approaches have been used to train humans in intelligent software systems; one of them is exemplar-based training. Although there has been some success, depending on the structure of the system, there are limitations to exemplars, which oftentimes are post hoc and case-based. Explicit Rule Learning is a global and rule-based training method that incorporates exemplars, but goes beyond specific cases. It provides learners with rich, robust mental models and the ability to transfer the learned skills to novel, previously unencountered situations. Learners are given verbalizable, probabilistic if...then statements, supplemented with exemplars. This is followed up with a series of practice problems, to which learners respond and receive immediate feedback on their correctness. The expectation is that this method will result in a refined representation of the system's underlying principles, and a richer and more robust mental model that will enable the learner to simulate future states. Preliminary research helped to evaluate and refine Explicit Rule Learning. The final study in this research applied Explicit Rule Learning to a more real-world system, autonomous driving. The mixed-method within-subject study used a more naturalistic environment. Participants were given training material using the Explicit Rule Learning method and were subsequently tested on their ability to predict the autonomous vehicle's actions. The results indicate that the participants trained with the Explicit Rule Learning method were more proficient at predicting the autonomous vehicle's actions. These results, together with the results of preceding studies indicate that Explicit Rule Learning is an effective method to accelerate the proficiency of learners of intelligent software systems. Explicit Rule Learning is a low-cost training intervention that can be adapted to many intelligent software systems, including the many types of AI/ML systems in today's world.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798380372947Subjects--Topical Terms:

556029
Cognitive psychology.
Subjects--Index Terms:

AI/ML systemsIndex Terms--Genre/Form:

554714
Electronic books.

Explicit Rule Learning : = A Cognitive Tutorial Method to Train Users of Artificial Intelligence/Machine Learning Systems.
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