Computing and Information Systems - Theses
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ItemInterest-based negotiation in multi-agent systems( 2004)Software systems involving autonomous interacting software entities (or agents) present new challenges in computer science and software engineering. A particularly challenging problem is the engineering of various forms of interaction among agents. Interaction may be aimed at enabling agents to coordinate their activities, cooperate to reach common objectives, or exchange resources to better achieve their individual objectives. This thesis is concerned with negotiation: a process through which multiple self-interested agents can reach agreement over the exchange of scarce resources. In particular, I focus on settings where agents have limited or uncertain information, precluding them from making optimal individual decisions. I demonstrate that this form of bounded-rationality may lead agents to sub-optimal negotiation agreements. I argue that rational dialogue based on the exchange of arguments can enable agents to overcome this problem. Since agents make decisions based on particular underlying reasons, namely their interests, beliefs and planning knowledge, then rational dialogue over these reasons can enable agents to refine their individual decisions and consequently reach better agreements. I refer to this form of interaction as “interested-based negotiation.” (For complete abstract open document)
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ItemAn investigation of interactivity and flow: student behaviour during online instruction( 2004-12)This thesis combines ideas from human-computer interaction, education and psychology to explore the interactions of students in an online learning environment. The motivation for the work was to understand better how to engage students in a highly enjoyable experience of online learning. The thesis describes three experiments. The first experiment was an exploratory study investigating the influence of learner interactions in an online physics learning task. Students worked through an online learning experience that offered high and low levels of interactivity. The aim was to explore their interactions and choices in an environment in which they could elect to move from the highly interactive mode to the less interactive mode at any time. Web logs were used to track their interactions and question probes gathered data on their emotions, learning goals and strategies. The analysis revealed a number of different patterns of interaction. Statistical analysis showed that most, but not all, preferred to follow an interactive path through the material. Students who used the interactive materials showed improved learning gains in transfer-style questions compared to those in the less interactive mode. Several issues were identified as important to consider in a follow-up study: emotions, affect, challenge, and the degree of control that the learner perceives.
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ItemEfficient mining of interesting emerging patterns and their effective use in classification( 2004-07)Knowledge Discovery in Databases (KDD), or Data Mining is used to discover interesting or useful patterns and relationships in data, with an emphasis on large volume of observational databases. Among many other types of information (knowledge) that can be discovered in data, patterns that are expressed in terms of features are popular because they can be understood and used directly by people. The recently proposed Emerging Pattern (EP) is one type of such knowledge patterns. Emerging Patterns are sets of items (conjunctions of attribute values) whose frequency change significantly from one dataset to another. They are useful as a means of discovering distinctions inherently present amongst a collection of datasets and have been shown to be a powerful method for constructing accurate classifiers. (For complete abstract open document)
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ItemNatural language as an agent communication language( 2004)Intelligent agents should be able to communicate with each other using an extensible, expressive language. Agents should have the ability work together in a heterogeneous environment to solve complex goals, while, acting on their own initiative and maintaining autonomy. Current agent communication languages are not expressive enough to facilitate coordination of agents in a heterogeneous system. Natural languages, such as English have evolved to become expressive enough to advance the human race to be the dominant species. It has been refined over millenia and is proven extensible. This research demonstrates the feasibility of using natural language as an agent communication language for intelligent agents solving a collaborative task
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ItemCombining part of speech induction and morphological induction( 2004-11)Linguistic information is useful in natural language processing, information retrieval and a multitude of sub-tasks involving language analysis. Two types of linguistic information in all languages are part of speech and morphology. Part of speech information reflects syntactic structure and can assist in tasks such as speech recognition, machine translation and word sense disambiguation. Morphological information describes the structure of words and has application in automated spelling correction, natural language generation and information retrieval for morphologically complex languages. Machine learning methods in natural language processing acquire linguistic information from corpora of natural language text. While supervised learning algorithms are trained on texts that have been annotated with linguistic features, induction algorithms learn linguistic information from unannotated corpora. Such algorithms avoid any requirement for linguistically annotated training data - a resource that is highly time-intensive to produce. However, in learning from unannotated corpora, only limited sources of information are available. In practice, part of speech induction methods usually learn from distributional evidence about the contexts in which words occur. In contrast, morphological induction methods tend to be based on the orthographic structure of the words in the corpus. However, a word’s morphological form and syntactic function often correlate: a word’s morphology may indicate its syntactic function and vice versa. Thus, both distributional and orthographic evidence may be useful for both tasks. This thesis investigates the extent to which the information induced by one learner can be used to bootstrap the other: specifically, whether the incorporation of explicit annotations from one learner can improve the performance of the other.
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ItemAccounting conservatism: evidence from the oil and gas industry( 2004)Prior evidence in the oil and gas industry suggests that investors, when assessing firm value, seem to distinguish between different degrees of accounting conservatism that result from the application of the successful efforts (SE) method versus the full cost (FC) method. However, research addressing the valuation implications of accounting choice in the oil and gas industry primarily investigated periods prior to the issuance of SFAS 121. The effect of SFAS 121 on accounting conservatism in the oil and gas industry and hence on the usefulness of the SE method, relative to the FC method, remains untested. This study extends the existing literature by re-examining the effect of accounting conservatism on the usefulness of accounting numbers produced by SE and FC methods during the period 1995-2001, a period in which both SFAS 121 and the ceiling test rules were applied. Empirical results show that in an environment of both SFAS 121 and the ceiling test, there is no difference between SE and FC firms with respect to conservatism associated with the application of accounting rules. Moreover, the results show that the usefulness of accounting numbers to investors does not differ across SE and FC methods. That is, investors attach no valuation premium of one method over the other in the oil and gas industry. This examination provides updated evidence that should be of interest to regulators and standards setters.