This collection includes most of the ASU Theses and Dissertations from 2011 to present. ASU Theses and Dissertations are available in downloadable PDF format; however, a small percentage of items are under embargo. Information about the dissertations/theses includes degree information, committee members, an abstract, supporting data or media.
In addition to the electronic theses found in the ASU Digital Repository, ASU Theses and Dissertations can be found in the ASU Library Catalog.
Dissertations and Theses granted by Arizona State University are archived and made available through a joint effort of the ASU Graduate College and the ASU Libraries. For more information or questions about this collection contact or visit the Digital Repository ETD Library Guide or contact the ASU Graduate College at gradformat@asu.edu.
Building computational models of human problem solving has been a longstanding goal in Artificial Intelligence research. The theories of cognitive architectures addressed this issue by embedding models of problem solving within them. This thesis presents an extended account of human problem solving and describes its implementation within one such theory…
Building computational models of human problem solving has been a longstanding goal in Artificial Intelligence research. The theories of cognitive architectures addressed this issue by embedding models of problem solving within them. This thesis presents an extended account of human problem solving and describes its implementation within one such theory of cognitive architecture--ICARUS. The document begins by reviewing the standard theory of problem solving, along with how previous versions of ICARUS have incorporated and expanded on it. Next it discusses some limitations of the existing mechanism and proposes four extensions that eliminate these limitations, elaborate the framework along interesting dimensions, and bring it into closer alignment with human problem-solving abilities. After this, it presents evaluations on four domains that establish the benefits of these extensions. The results demonstrate the system's ability to solve problems in various domains and its generality. In closing, it outlines related work and notes promising directions for additional research.
The future will be replete with Artificial Intelligence (AI) based agents closely collaborating with humans. Although it is challenging to construct such systems for real-world conditions, the Intelligent Tutoring System (ITS) community has proposed several techniques to work closely with students. However, there is a need to extend these systems…
The future will be replete with Artificial Intelligence (AI) based agents closely collaborating with humans. Although it is challenging to construct such systems for real-world conditions, the Intelligent Tutoring System (ITS) community has proposed several techniques to work closely with students. However, there is a need to extend these systems outside the controlled environment of the classroom. More recently, Human-Aware Planning (HAP) community has developed generalized AI techniques for collaborating with humans and providing personalized support or guidance to the collaborators. In this thesis, the take learning from the ITS community is extend to construct such human-aware systems for real-world domains and evaluate them with real stakeholders. First, the applicability of HAP to ITS is demonstrated, by modeling the behavior in a classroom and a state-of-the-art tutoring system called Dragoon. Then these techniques are extended to provide decision support to a human teammate and evaluate the effectiveness of the framework through ablation studies to support students in constructing their plan of study (\ipos). The results show that these techniques are helpful and can support users in their tasks. In the third section of the thesis, an ITS scenario of asking questions (or problems) in active environments is modeled by constructing questions to elicit a human teammate's model of understanding. The framework is evaluated through a user study, where the results show that the queries can be used for eliciting the human teammate's mental model.
