Browsing by Author "Robert St. Amant, Committee Chair"

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    Effective Tool Use in a Habile Agent
    (2005-04-28) Wood, Alexander Burchi; Robert St. Amant, Committee Chair; R. Michael Young, Committee Member; Jon Doyle, Committee Member
    Tool use is a hallmark of human intelligence, which has not fully been explored in the artificial intelligence research community. Research in cognitive neuroscience on primates suggests that not only do we maintain a mental representation of our body, but the body schema is modified to include a tool during intentional tool use (Iriki et al., 1996). We have developed a habile (tool-using) agent, based on the Sony Aibo platform, that can pick up a stick and use it as a tool to reach objects previously out of its range. The agent uses a recurrent neural network developed by Steinkühler and Cruse (1998) for maintaining an internal body schema used to find appropriate postures for reaching and grasping tools. We argue that analysis of activities of such tool using agents offers an informative way to evaluate intelligence.
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    HabilisDraw: A Tool-based Direct Manipulation Software Environment
    (2004-07-26) Horton, Thomas Eugene; R. Michael Young, Committee Member; Dennis R. Bahler, Committee Member; Robert St. Amant, Committee Chair
    Direct manipulation interfaces already employ a weak analogy to the use of physical tools in the real world. Despite certain tradeoffs, a stronger application of tool-using principles can lead to improvements in the design of software interfaces. I outline here some of the theory behind such an approach, and describe the design of systems that follow these principles, with emphasis on a tool-based drawing application called HabilisDraw.
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    Personalized Hierarchical Menu Organization for Mobile Device Users
    (2007-04-16) Dawkins, Andrea Lauren; James Lester, Committee Member; Annie I. Antón, Committee Member; Robert St. Amant, Committee Chair
    As Internet access via cell phone browsers becomes more common, new website interfaces are emerging. Hierarchical menus are effective for such interfaces due to their compact presentation. For menus with many levels and possible destinations, personalized menu organizations can potentially expedite navigation. This thesis contains an empirical and analytical evaluation of two menu personalization techniques based on user preferences, Expanded menus and Expanded⁄Reordered menus. A 30 participant experiment revealed that both types of personalized menus resulted in faster performance and higher evaluations from the users, with the Expanded⁄Reordered menu ranking the highest in both respects. One interesting finding was that individual users' evaluation was not consistent with their performance. Data collected from the performance study was used to evaluate an existing GOMS model of cell phone menu traversal, which demonstrated significant limitations in the generality of the model. However, the results also suggest that individual differences in user performance may account for much of the error in model predictions.
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    A Schematic Representation for Cognitive Tool-Using Agents
    (2009-05-13) Mu, Wei; Robert St. Amant, Committee Chair; Ronald P. Endicott, Committee Member; James C. Lester, Committee Member; R. Michael Young, Committee Member
    In artificial intelligence (AI) research, embodied systems have received increasing attention since the 1990s. How to bridge the gap between raw sensorimotor data and symbolic representation in a robotic agent is still an open question. The research described in this document is inspired by theories in cognitive science, such as concept theory and embodied realism, as well as work in robotics and AI. The general goal of this research is to build a system capable of acquiring and maintaining semantic knowledge for higher-level reasoning, in particular reasoning about the use of tools, from the embodied experience of a cognitive agent in a simulated environment or in the real world. This research addresses cognitive theories of embodiment, the design of a general computational architecture, and the design and implementation of AI techniques for solving tool-using problems. One of the major contributions of this research is to provide a computational architecture for an embodied agent that can capture semantic relations from its interactions with the world, sufficient to support effective tool use both in short-term predictions and plan generation. As a result, we have implemented an example of this architecture in an Action Schema Generator, or ASG, which can automatically generate production rules and symbolic representations from a simulated agent’s embodied experience without losing the capability of transferring the knowledge backwards to its original numerical sensorimotor format. We have developed pragmatic methods to evaluate the performance of ASG, at the component level and the system level, in simulated and real scenarios, for tasks with and without tools. We also have compared our design with other robotics and cognitive architectures, including behavior-based robotics, Neuroevolution, and psychologically inspired architectures. We believe that our work can provide a general foundation for embodied agents, and should be useful in future research.
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    Statistical Modeling of Human Response Times for Task Modeling in HCI.
    (2009-09-14) Bonto-Kane, Maria Vicente; Robert St. Amant, Committee Chair; Ana Anton, Committee Member; James Lester II, Committee Member; David Kaber, Committee Member
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    A System for Generating Device-Specific Action Sequences
    (2003-11-21) Simpson, Clarence Adriano; Robert St. Amant, Committee Chair; Christopher G. Healey, Committee Member; James Lester, Committee Member
    As computing devices become more varied and complex, it has been shown that it is useful, perhaps even essential, to begin describing elements of human-computer interaction in more abstract terms. Much of the literature has focused on specific cases of interaction involving certain input devices, tasks, and operating system environments. However, it has thus far been taken for granted that the mapping between the controls on an input device and the low-level tasks being executed with the device are an optimal mapping. We present a system for generating this optimal mapping by classifying input devices and low-level tasks based on the proposed taxonomies, then using a knowledge base to determine the efficiency of using certain classes of input device controls to perform certain classes of tasks. We also present some preliminary work in the use of user modeling to account for cognitive and psychological factors such as user control preference and the relation between action sequence complexity and human memory.
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    Toward Video Games on Video
    (2008-08-04) Narayanan Kutty, Chaya; Robert St. Amant, Committee Chair; Steffen Heber, Committee Member; R Michael Young, Committee Member