Gaussian Processes (GPs) are promising Bayesian methods for classification and regression problems. They have also been used for semi-supervised learning tasks. In this paper, we propose a new algorithm for solving semi-supervised binary classification problem using sparse GP regression (GPR) models. It is closely related to semi-supervised learning based on support vector regression (SVR) and maximum margin clustering. The proposed algorithm is simple and easy to implement. It gives a sparse solution directly unlike the SVR based algorithm. Also, the hyperparameters are estimated easily without resorting to expensive cross-validation technique. Use of sparse GPR model helps in making the proposed algorithm scalable. Preliminary results on synthetic and real-world data sets demonstrate the efficacy of the new algorithm.

In many real world planning domains, some observation information is optional and useless to the execution of a plan; on the other hand, information acquisition may require some kind of cost. The problem of observation reduction for strong plans has been addressed in the literature. However, observation reduction for plans with contexts (which are more general and useful than strong plans in robotics) is still a open problem. In this paper, we present an attempt to solve the problem. Our first contribution is the definition of structured plans, which can encode sequential, conditional and iterative behaviors, and is expressive enough for dealing with incomplete observation information and internal states of the agent. A second contribution is an observation reduction algorithm for plans with contexts, which can transform a plan with contexts into a structured plan that only branches on necessary observation information.

In many real world planning domains, some observation information is optional and useless to the execution of a plan; on the other hand, information acquisition may require some kind of cost. The problem of observation reduction for strong plans has been addressed in the literature. However, observation reduction for plans with contexts (which are more general and useful than strong plans in robotics) is still a open problem. In this paper, we present an attempt to solve the problem. Our first contribution is the definition of structured plans, which can encode sequential, conditional and iterative behaviors, and is expressive enough for dealing with incomplete observation information and internal states of the agent. A second contribution is an observation reduction algorithm for plans with contexts, which can transform a plan with contexts into a structured plan that only branches on necessary observation information.

Gaussian Processes (GPs) are promising Bayesian methods for classification and regression problems. They have also been used for semi-supervised learning tasks. In this paper, we propose a new algorithm for solving semi-supervised binary classification problem using sparse GP regression (GPR) models. It is closely related to semi-supervised learning based on support vector regression (SVR) and maximum margin clustering. The proposed algorithm is simple and easy to implement. It gives a sparse solution directly unlike the SVR based algorithm. Also, the hyperparameters are estimated easily without resorting to expensive cross-validation technique. Use of sparse GPR model helps in making the proposed algorithm scalable. Preliminary results on synthetic and real-world data sets demonstrate the efficacy of the new algorithm.

Most subfields of computer science have an interface layer via which applications communicate with the infrastructure, and this is key to their success (e.g., the Internet in networking, the relational model in databases, etc.). So far this interface layer has been missing in AI. This book discusses Markov logic, a powerful language that has been successfully applied as an interface layer. ISBN 9781598296921, 155 pages.

Unsupervised learning of morphology is an important task for human learners and in natural language processing systems. Previous systems focus on segmenting words into substrings (taking ⇒ tak.ing), but sometimes a segmentation-only analysis is insufficient (e.g., taking may be more appropriately analyzed as take+ing, with a spelling rule accounting for the deletion of the stem-final e). In this paper, we develop a Bayesian model for simultaneously inducing both morphology and spelling rules. We show that the addition of spelling rules improves performance over the baseline morphology-only model.

Tagging has become a primary tool for users to organize and share digital content on many social media sites. In addition, tag information has been shown to enhance capabilities of existing search engines. However, many resources on the web still lack tag information. This paper proposes a content-based approach to tag recommendation which can be applied to webpages with or without prior tag information. While social bookmarking service such as Delicious enables users to share annotated bookmarks, tag recommendation is available only for pages with tags specified by other users. Our proposed approach is motivated by the observation that similar webpages tend to have the same tags. Each webpage can therefore share the tags they own with similar webpages. The propagation of a tag depends on its weight in the originating webpage and the similarity between the sending and receiving webpages. The similarity metric between two webpages is defined as a linear combination of four cosine similarities, taking into account both tag information and page content. Experiments using data crawled from Delicious show that the proposed method is effective in populating untagged webpages with the correct tags.

Buoyed by recent successes in the area of distributed constraint optimization problems (DCOPs), this paper addresses challenges faced when applying DCOPs to real-world domains. Three fundamental challenges must be addressed for a class of real-world domains, requiring novel DCOP algorithms. First, agents may not know the payoff matrix and must explore the environment to determine rewards associated with variable settings. Second, agents may need to maximize total accumulated reward rather than instantaneous final reward. Third, limited time horizons disallow exhaustive exploration of the environment. We propose and implement a set of novel algorithms that combine decision-theoretic exploration approaches with DCOP-mandated coordination. In addition to simulation results, we implement these algorithms on robots, deploying DCOPs on a distributed mobile sensor network.

In many practical contexts where a number of agents have to find a common decision, the votes do not come all together at the same time. In such situations, we may want to preprocess the information given by the subelectorate (consisting of the voters who have expressed their votes) so as to ``compile'' the known votes for the time when the latecomers have expressed their votes. We study the amount of space necessary for such a compilation, as a function of the voting rule, the number of candidates, and the number of votes already known. We relate our results to existing work, especially on communication complexity.

Understanding the computational complexity of manipulation in elections is arguably the most central agenda in Computational Social Choice. One of the influential variations of the the problem involves a coalition of manipulators trying to make a favorite candidate win the election. Although the complexity of the problem is well-studied under the assumption that the voters are weighted, there were very few successful attempts to abandon this strong assumption. In this paper, we study the complexity of the unweighted coalitional manipulation problem (UCM) under several prominent voting rules. Our main result is that UCM is NP-complete under the maximin rule; this resolves an enigmatic open question. We then show that UCM is NP-complete under the ranked pairs rule, even with respect to a single manipulator. Furthermore, we provide an extreme hardness-of-approximation result for an optimization version of UCM under ranked pairs. Finally, we show that UCM under the Bucklin rule is in P.