Initiated by TAC 2010, aspect-guided summaries not only address specific user need, but also ameliorate content-level coherence by using aspect information. This paper presents a full-fledged system composed of three modules: finding sentence-level textual aspects, modeling aspect-based coherence with an HMM model, and selecting and ordering sentences with aspect information to generate coherent summaries. The evaluation results on the TAC 2011 datasets show the superiority of aspect-guided summaries in terms of both information coverage and textual coherence.

Knowledge compilation is a powerful technique for compactly representing and efficiently reasoning about logical knowledge bases. It has been successfully applied to numerous problems in artificial intelligence, such as probabilistic inference and conformant planning. Conditioning, which updates a knowledge base with observed truth values for some propositions, is one of the fundamental operations employed for reasoning. In the propositional setting, conditioning can be efficiently applied in all cases. Recently, people have explored compilation for first-order knowledge bases. The majority of this work has centered around using first-order d-DNNF circuits as the target compilation language. However, conditioning has not been studied in this setting. This paper explores how to condition a first-order d-DNNF circuit. We show that it is possible to efficiently condition these circuits on unary relations. However, we prove that conditioning on higher arity relations is #P-hard. We study the implications of these findings on the application of performing lifted inference for first-order probabilistic models.This leads to a better understanding of which types of queries lifted inference can address.

The Upper Confidence Bounds (UCB) algorithm is a well-known near-optimal strategy for the stochastic multi-armed bandit problem. Its extensions to trees, such as the Upper Confidence Tree (UCT) algorithm, have resulted in good solutions to the problem of Go. This paper introduces DUCT, a distributed algorithm inspired by UCT, for solving Distributed Constraint Optimization Problems (DCOP). Bounds on the solution quality are provided, and experiments show that, compared to existing DCOP approaches, DUCT is able to solve very large problems much more efficiently, or to find significantly higher quality solutions.

Deciding what mix of engine and battery power to use is critical to hybrid vehicles' fuel efficiency. Current solutions consider several factors such as the charge of the battery and how efficient the engine operates at a given speed. Previous research has shown that by taking into account the future power requirements of the vehicle, a more efficient balance of engine vs. battery power can be attained. In this paper, we utilize a probabilistic driving route prediction system, trained using Inverse Reinforcement Learning, to optimize the hybrid control policy. Our approach considers routes that the driver is likely to be taking, computing an optimal mix of engine and battery power. This approach has the potential to increase vehicle power efficiency while not requiring any hardware modification or change in driver behavior. Our method outperforms a standard hybrid control policy, yielding an average of 1.22% fuel savings.

Sound source localization and separation with permutation resolution are essential for achieving a computational auditory scene analysis system that can extract useful information from a mixture of various sounds. Because existing methods cope separately with these problems despite their mutual dependence, the overall result with these approaches can be degraded by any failure in one of these components. This paper presents a unified Bayesian framework to solve these problems simultaneously where localization and separation are regarded as a clustering problem. Experimental results confirm that our method outperforms state-of-the-art methods in terms of the separation quality with various setups including practical reverberant environments.

Much work in learning from demonstration has focused on learning simple tasks from structured demonstrations that have a well-defined beginning and end. As we attempt to scale robot learning to increasingly complex tasks, it becomes intractable to learn task policies monolithically. Furthermore, it is desirable to be able to learn from natural, unstructured demonstrations, which are unsegmented, possibly incomplete, and may come from different tasks. We propose a three-part approach to designing a natural, scalable system that allows a robot to learn tasks of increasing complexity by automatically building and refining a library of skills over time. First, we describe a Bayesian nonparametric model that can segment unstructured demonstrations into appropriate numbers of component skills and recognize repeated skills across demonstrations and tasks. These skills can then be parameterized and generalized to new situations. Second, we propose to create a system that allows the user to provide unstructured corrections and feedback to the robot, without requiring any knowledge of the robot's underlying representation of the task or its component skills. Third, we propose to infer the user's intentions for each segmented skill and autonomously improve these skills using reinforcement learning. This approach will be applied to learn and generalize complex, multi-step tasks that are beyond the reach of current LfD methods, using the PR2 mobile manipulator as a testing platform.

We address the problem of making single-point decisions in large partially observable games, where players interleave observation, deliberation, and action.  We present information set generation as a key operation needed to reason about games in this way.  We show how this operation can be used to implement an existing decision-making algorithm.  We develop a constraint satisfaction algorithm for performing information set generation and show that it scales better than the existing depth-first search approach on multiple non-trivial games.

As the semantic web expands, ontological data becomes distributed over a large network of data sources on the Web. Consequently, evaluating queries that aim to tap into this distributed semantic database necessitates the ability to consult multiple data sources efficiently. In this paper, we propose methods and heuristics to efficiently query distributed ontological data based on a series of properties of summarized data. In our approach, each source summarizes its data as another RDF graph, and relevant section of these summaries are merged and analyzed at query evaluation time. We show how the analysis of these summaries enables more efficient source selection, query pruning and transformation of expensive distributed joins into local joins.

Sequential auctions can be used to provide solutions to the multi-robot task-allocation problem. In this paper we extend previous work on sequential auctions and propose an algorithm that clusters and auctions uninitiated task clusters repeatedly upon the completion of individual tasks. We demonstrate empirically that our algorithm results in lower overall team costs than other sequential auction algorithms that only assign tasks once.

The Nonnegative Least Squares (NNLS) formulation arises in many important regression problems. We present a novel coordinate descent method which differs from previous approaches in that we do not explicitly maintain complete gradient information. Empirical evidence shows that our approach outperforms a state-of-the-art NNLS solver in computation time for calculating radiation dosage for cancer treatment problems.