Browsing by Author "Munindar P. Singh, Committee Member"

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    A Comparison of Two Algorithms for Clearing Multi-unit Bid Double Auctions
    (2003-01-06) Bao, Shengli; Rada Y. Chirkova, Committee Member; Peter R. Wurman, Committee Chair; Munindar P. Singh, Committee Member
    Auctions have rapidly achieved a prominent position as an online revenue model. As the number and diversity of participants grow, the complexity of choosing an efficient allocation mechanism for a specific situation increases. One aspect of running online auctions is the algorithms used by the auction server to manage bids. In this paper, we present two algorithms. We discuss how these two algorithms expedite the negotiations between buyers and sellers and under which situations they can achieve an efficient allocation for goods and services.
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    An Inductive Framework for Affect Recognition and Expression in Interactive Learning Environments
    (2009-03-18) McQuiggan, Scott William; James C. Lester, Committee Chair; John L. Nietfeld, Committee Member; Munindar P. Singh, Committee Member; R. Michael Young, Committee Member
    Recent years have seen a growing recognition of the importance of affective reasoning in human-computer interaction. Because affect plays an important role in cognitive functions, such as perception and decision-making, the prospect of modeling user affect and enabling interactive systems to respond appropriately holds much appeal for a broad range of applications. Affective reasoning is particularly promising for educational applications because of the strong connections between affect and learning. If it were possible to accurately detect frustration, monitor changes in efficacy, and predict students’ affective states, interactive learning environments could more effectively tailor problem-solving episodes. However, constructing computational models of affect recognition and affect expression is challenging because of the need to devise solutions that are accurate, efficient, and capable of making early predictions. To this end we propose CARE, an inductive framework for affect recognition and expression. CARE learns models of affect from observation of human-computer and human-human interaction. First, in training sessions, users perform a series of tasks in interactive environments while CARE monitors reports of users’ affective experiences. In addition, CARE monitors user actions, world state, and physiological responses. Second, CARE induces models of affect from observed data with machine learning techniques that include decision trees, naive Bayes classifiers, support vector machines, Bayesian networks, and n-grams. Third, at runtime, CARE-induced models monitor user actions, world state, and physiological responses to predict user affective states. In a series of studies involving more than four hundred subjects, the CARE framework has successfully been used to perform a number of affect prediction tasks, including emotional state prediction, self-efficacy, and metacognitive monitoring prediction. It has also been used to induce models of empathy for virtual agents in interactive learning environments. Results suggest that CARE-induced affect models satisfy the real-time requirements of interactive systems and provide a solid foundation for empirically informed affective reasoning.
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    A System for Managing User Obligations
    (2009-12-05) Irwin, Keith; S. Purushothaman Iyer, Committee Member; Munindar P. Singh, Committee Member; Ting Yu, Committee Chair; Ping Ning, Committee Member
    As computer systems become a more pervasive part of our societies, actions within those computer systems are becoming increasingly governed by complex policies such as laws, corporate policies, and legal agreements such as data sharing agreements and privacy policies. These policies impose both requirements about what may or may not be done and about what must be done. Current security policies may be able to manage restrictions on actions, but they are not sufficient to describe actions which are required. We examine herein the idea of user obligations, which are actions which are required of the users, but which the system cannot directly cause to occur. We propose a system for the management of user obligations. This system should both ensure that obligations are assigned in a manner such that it will be possible for them to be fulfilled and allow users of a system to know what they are required to do. We present an abstract formal model of such a system. We examine a number of aspects of such a system, principally including the maintenance of an acceptable system state, the assignment of blame when users fail to fulfill their obligations, and providing adequate feedback to users when their actions are rejected. For each of these aspects, we present formal definitions to define the range of acceptable behavior. We also provide a more specific and concrete model of one possible user obligations management system and develop algorithms for that model. We do this in order to show the practicality of our formal models and properties.