The thesis proposes creating bilingual dictionaries andWordnets for languages without many lexical resourcesusing resources of resource-rich languages. Our workwill have the advantage of creating lexical resources,reducing time and cost and at the same time improvingthe quality of resources created.

This manuscript provides the research questions, proposed research plans, as well as expected contributions of my doctoral dissertation. My dissertation is primarily focused on providing computational approaches to study and analyze dialectical reasoning in large-scale online platforms. In particular, I aim to tackle the challenge of developing novel models to automatically classify explanation and argumentation as two different types of reasoning in text of discourse on the Web. The resulting models can be incorporated in the social Web environments to increase participants' awareness of others' reasoning types, which may lead to a more effective dialogue protocol and strategy.

We provide an approach to estimate trust between agents from their interactions. Our approach takes a probabilistic model of trust founded on commitments. We assume commitments to estimate trust because a commitment describes what an agent may expect of another. Therefore, the satisfaction or violation of a commitment provides a natural basis for determining how much to trust another agent. We evaluate our approach empirically. In one study, 30 subjects read emails extracted from the Enron dataset augmented with some synthetic emails to capture commitment operations missing in the Enron corpus. The subjects estimated trust between each pair of communicating participants. We trained model parameters for each subject with respect to our automated analysis of the emails, showing that our trained parameters yield a lower prediction error of a subject's trust rating given automatically inferred commitments than fixed parameters.

In the event of an earthquake or fire, search and rescue efforts may be delayed until it is safe for a human team to enter the area. A team of robots could enter in advance to provide maps, images and locations of interest to the human team, allowing them to prepare their approach when they can enter. In a disaster area, communication may also be limited. We have developed a set of distributed algorithms that make use of a small number of robots to fully explore an unknown environment even with restrictions on communication, team size, and available sensors. We show, through proofs and experiments, that the algorithm will allow the team of robots to fully explore the environment and maintain the necessary communication to return the information to the search and rescue team waiting outside.

Within the field of Artificial Intelligence (AI) research, Vote (STV) select winners from sets of ballot. For example, the subfield of Computational Social Choice considers under plurality candidate X has won this election by virtue the application of AI techniques to problems in Social of having being at the top of the largest number of ballots. Under STV Y would win instead. Starting in the early 1990's, computer scientists began to In this work, we model voters as having partial orderings take an interest in social choice. Initial work was concerned over the candidates for their preferences, instead of linear with circumventing the impossibility results implied orderings.

As potential-based reward shaping functions (heuristic signals conflicts may exist between objectives, there is in general guiding exploration) (Brys et al. 2014a). We prove that this a need to identify (a set of) tradeoff solutions. The set modification preserves the total order, and thus also optimality, of optimal, i.e. non-dominated, incomparable solutions is of policies, mainly relying on the results by Ng, Harada, called the Pareto-front. We identify multi-objective problems and Russell (1999). This insight - that any MDP can be with correlated objectives (CMOP) as a specific subclass framed as a CMOMDP - significantly increases the importance of multi-objective problems, defined to contain those of this problem class, as well as techniques developed MOPs whose Pareto-front is so limited that one can barely for it, as these could be used to solve regular single-objective speak of tradeoffs (Brys et al. 2014b). By consequence, MDPs faster and better, provided several meaningful shapings the system designer does not care about which of the very can be devised.

This thesis seeks to address word reasoning problems from a semantic standpoint, proposing a uniform approach for generating solutions while also providing human-understandable explanations. Current state of the art solvers of semantic problems rely on traditional machine learning methods. Therefore their results are not easily reusable by algorithms or interpretable by humans. We propose leveraging web-scale knowledge graphs to determine a semantic frame of interpretation. Semantic knowledge graphs are graphs in which nodes represent concepts and the edges represent the relations between them. Our approach has the following advantages: (1) it reduces the space in which the problem is to be solved; (2) sparse and noisy data can be used without relying only on the relations deducible from the data itself; (3) the output of the inference algorithm is supported by an interpretable justification. We demonstrate our approach in two domains: (1) Topic Modeling: We form topics using connectivity in semantic graphs. We use the same topic models for two very different recommendation systems, one designed for high noise interactive applications and the other for large amounts of web data. (2) Analogy Solving: For humans, analogies are a fundamental reasoning pattern, which relies on abstraction and comparative analysis. In order for an analogy to be understood, precise relations have to be identified and mapped. We introduce graph algorithms to assess the analogy strength in contexts derived from the analogy words. We demonstrate our approach by solving standardized test analogy question.

One of the defining characteristics of an adaptive automation system is the hand-off from machine to human--and vice versa. This research seeks to improve system control hand-offs, by investigating how the manner in which the automation completes its task affects the overall performance of the human-machine team. Specifically, the research will explore how the level of similarity of action choices between the automation and the human operator affects the resulting system's performance. A design process model for creating adaptive automation systems is complete, and was used to design an adaptive automation research environment. Data gathered using this system will be used to automate user task performance in the system, and allow for research into the effects of that automation.

Conditional preference networks (CP-nets) exploit the power of conditional ceteris paribus rules to enable a compact representation of human preferences. CP-nets have much appeal. However, the study of CP-nets has not advanced sufficiently for their widespread use in complex, real-world applications. Most studies limit their attention to strict, complete, consistent preferences over binary domains. In my research, I attempt to address these limitations to make CP-nets more useful. I discuss recent research in which we presented a novel algorithm for learning CP-nets from user queries, as well as work showing how to adapt existing algorithms to learn and reason with multivalued CP-nets that can model indifference as well as strict preference. I outline anticipated research to extend our elicitation algorithm to a richer class of CP-nets, develop a formal model of expected flipping sequence length, and learn CP-nets in which a subject prefers to coordinate certain features, leading to a cycle in the dependency graph.

Hashing has recently attracted considerable attention for large scale similarity search. However, learning compact codes with good performance is still a challenge. In many cases, the real-world data lies on a low-dimensional manifold embedded in high-dimensional ambient space. To capture meaningful neighbors, a compact hashing representation should be able to uncover the intrinsic geometric structure of the manifold, e.g., the neighborhood relationships between subregions. Most existing hashing methods only consider this issue during mapping data points into certain projected dimensions. When getting the binary codes, they either directly quantize the projected values with a threshold, or use an orthogonal matrix to refine the initial projection matrix, which both consider projection and quantization separately, and will not well preserve the locality structure in the whole learning process. In this paper, we propose a novel hashing algorithm called Locality Preserving Hashing to effectively solve the above problems. Specifically, we learn a set of locality preserving projections with a joint optimization framework, which minimizes the average projection distance and quantization loss simultaneously. Experimental comparisons with other state-of-the-art methods on two large scale datasets demonstrate the effectiveness and efficiency of our method.