The conventional model of coalition formation considers every possible subset of agents as a potential coalition. However, in many real-world applications, there are inherent constraints on feasible coalitions: for instance, certain agents may be prohibited from being in the same coalition, or the coalition structure may be required to consist of coalitions of the same size. In this paper, we present the first systematic study of constrained coalition formation (CCF). We propose a general framework for this problem, and identify an important class of CCF settings, where the constraints specify which groups of agents should/should not work together. We describe a procedure that transforms such constraints into a structured input that allows coalition formation algorithms to identify, without any redundant computations, all the feasible coalitions. We then use this procedure to develop an algorithm for generating an optimal (welfare-maximizing) constrained coalition structure, and show that it outperforms existing state-of-the-art approaches by several orders of magnitude.

In this paper, we will present a unified view for LDA. We will (1) emphasize that standard LDA solutions are not unique, (2) propose several new LDA formulations: St-orthonormal LDA, Sw-orthonormal LDA and orthogonal LDA which have unique solutions, and (3) show that with St-orthonormal LDA and Sw-orthonormal LDA formulations, solutions to all four major LDA objective functions are identical. Furthermore, we perform an indepth analysis to show that the LDA sometimes performs poorly due to over-fitting, i.e., it picks up PCA dimensions with small eigenvalues. From this analysis, we propose a stable LDA which uses PCA first to reduce to a small PCA subspace and do LDA in the subspace.

Successful approaches to the robot localization problem include Monte Carlo particle filters, which estimate non-parametric localization belief distributions. However, particle filters fare poorly at determining the robot's position without a good initial hypothesis. This problem has been addressed for robots that sense visual landmarks with sensor resetting, by performing sensor-based resampling when the robot is lost. For robots that make sparse, ambiguous and noisy observations, standard sensor resetting places new location hypotheses across a wide region, in positions that may be inconsistent with previous observations. We propose Multi-Observation Sensor Resetting, where observations from multiple frames are merged to generate new hypotheses more effectively. We demonstrate experimentally in the robot soccer domain on the NAO humanoid robots that Multi-Observation Sensor Resetting converges more efficiently to the robot's true position than standard sensor resetting, and is more robust to systematic vision errors.

We present the initial stage of our research on Learning from Demonstration algorithms. We have implemented an algorithm based on Confident Execution, one of the components of the Confidence-Based Autonomy algorithm developed by Chernova and Veloso. Our preliminary experiments were conducted first in simulation and then using a Sony AIBO ERS-7 robot. So far, our robot has been able to learn crude navigation strategies, despite limited trials. We are currently working on improving our implementation by including additional features that describe more broadly the state of the agent. Our long term goal is to incorporate Learning from Demonstration techniques in our HRTeam (human/multi-robot) framework.

An ad hoc team setting is one in which teammates must work together to obtain a common goal, but without any prior agreement regarding how to work together. In this abstract we present a role-based approach for ad hoc teamwork, in which each teammate is inferred to be following a specialized role that accomplishes a specific task or exhibits a particular behavior. In such cases, the role an ad hoc agent should select depends both on its own capabilities and on the roles currently selected by the other team members. We present methods for evaluating the influence of the ad hoc agent's role selection on the team's utility and we examine empirically how to select the best suited method for role assignment in a complex environment. Finally, we show that an appropriate assignment method can be determined from a limited amount of data and used successfully in similar new tasks that the team has not encountered before.

Epistemic extensions of description logics (DLs) have been introduced several years ago in order to enhance expressivity and querying capabilities of these logics by knowledge base introspection. We argue that unintended effects occur when imposing the traditionally employed semantics on the very expressive DLs that underly the OWL 1 and OWL 2 standards. Consequently, we suggest a revised semantics that behaves more intuitively in these cases and coincides with the traditional semantics of less expressive DLs. Moreover, we introduce a way of answering epistemic queries to OWL knowledge bases by a reduction to standard OWL reasoning. We provide an implementation of our approach and present first evaluation results.

"Who can fix this bug?" is an important question in bug triage to "accurately" assign developers to bug reports. To address this question, recent research treats it as a optimizing recommendation accuracy problem and proposes a solution that is essentially an instance of content-based recommendation (CBR). However, CBR is well-known to cause over-specialization, recommending only the types of bugs that each developer has solved before. This problem is critical in practice, as some experienced developers could be overloaded, and this would slow the bug fixing process. In this paper, we take two directions to address this problem: First,we reformulate the problem as an optimization problem of both accuracy and cost. Second, we adopt a content-boosted collaborative filtering (CBCF), combining an existing CBR with a collaborative filtering recommender (CF), which enhances the recommendationquality of either approach alone. However, unlike general recommendation scenarios, bug fix history is extremely sparse. Due to the nature of bug fixes, one bug is fixed by only one developer, which makes it challenging to pursue the above two directions. To address this challenge, we develop a topic-model to reduce the sparseness and enhance the quality of CBCF. Our experimental evaluation shows that our solution reduces the cost efficiently by 30% without seriously compromising accuracy.

This paper presents a novel symmetric graph regularization framework for pairwise constraint propagation. We first decompose the challenging problem of pairwise constraint propagation into a series of two-class label propagation subproblems and then deal with these subproblems by quadratic optimization with symmetric graph regularization. More importantly, we clearly show that pairwise constraint propagation is actually equivalent to solving a Lyapunov matrix equation, which is widely used in Control Theory as a standard continuous-time equation. Different from most previous constraint propagation methods that suffer from severe limitations, our method can directly be applied to multi-class problem and also can effectively exploit both must-link and cannot-link constraints. The propagated constraints are further used to adjust the similarity between data points so that they can be incorporated into subsequent clustering. The proposed method has been tested in clustering tasks on six real-life data sets and then shown to achieve significant improvements with respect to the state of the arts.

End-user interactive machine learning is a promising tool for enhancing human productivity and capabilities with large unstructured data sets. Recent work has shown that we can create end-user interactive machine learning systems for specific applications. However, we still lack a generalized understanding of how to design effective end-user interaction with interactive machine learning systems. This work presents three explorations in designing for effective end-user interaction with machine learning in CueFlik, a system developed to support Web image search. These explorations demonstrate that interactions designed to balance the needs of end-users and machine learning algorithms can significantly improve the effectiveness of end-user interactive machine learning.

When interacting with computer agents, people make inferences about various characteristics of these agents, such as their reliability and trustworthiness. These perceptions are significant, as they influence people's behavior towards the agents, and may foster or inhibit repeated interactions between them. In this paper we investigate whether computer agents can use the expression of emotion to influence human perceptions of trustworthiness. In particular, we study human-computer interactions within the context of a negotiation game, in which players make alternating offers to decide on how to divide a set of resources. A series of negotiation games between a human and several agents is then followed by a "trust game." In this game people have to choose one among several agents to interact with, as well as how much of their resources they will trust to it. Our results indicate that, among those agents that displayed emotion, those whose expression was in accord with their actions (strategy) during the negotiation game were generally preferred as partners in the trust game over those whose emotion expressions and actions did not mesh. Moreover, we observed that when emotion does not carry useful new information, it fails to strongly influence human decision-making behavior in a negotiation setting.