Toponym resolution, or grounding names of places to their actual locations, is an important problem in analysis of both historical corpora and present-day news and web content. Recent approaches have shifted from rule-based spatial minimization methods to machine learned classifiers that use features of the text surrounding a toponym. Such methods have been shown to be highly effective, but they crucially rely on gazetteers and are unable to handle unknown place names or locations. We address this limitation by modeling the geographic distributions of words over the earth's surface: we calculate the geographic profile of each word based on local spatial statistics over a set of geo-referenced language models. These geo-profiles can be further refined by combining in-domain data with background statistics from Wikipedia. Our resolver computes the overlap of all geo-profiles in a given text span; without using a gazetteer, it performs on par with existing classifiers. When combined with a gazetteer, it achieves state-of-the-art performance for two standard toponym resolution corpora (TR-CoNLL and Civil War). Furthermore, it dramatically improves recall when toponyms are identified by named entity recognizers, which often (correctly) find non-standard variants of toponyms.

We consider the problem of repeatedly matching a set of alternatives to a set of agents in the absence of monetary transfer. We propose a generic framework for evaluating sequential matching mechanisms with dynamic preferences, and show that unlike single-shot settings, the random serial dictatorship mechanism is manipulable.

Many AI applications involve the interaction of multiple autonomous agents, requiring those agents to reason about their own beliefs, as well as those of other agents. However, planning involving nested beliefs is known to be computationally challenging. In this work, we address the task of synthesizing plans that necessitate reasoning about the beliefs of other agents. We plan from the perspective of a single agent with the potential for goals and actions that involve nested beliefs, non-homogeneous agents, co-present observations, and the ability for one agent to reason as if it were another. We formally characterize our notion of planning with nested belief, and subsequently demonstrate how to automatically convert such problems into problems that appeal to classical planning technology. Our approach represents an important first step towards applying the well-established field of automated planning to the challenging task of planning involving nested beliefs of multiple agents.

Advice giving has been long explored in artificial intelligence to build robust learning algorithms. We consider advice giving in relational domains where the noise is systematic. The advice is provided as logical statements that are then explicitly considered by the learning algorithm at every update. Our empirical evidence proves that human advice can effectively accelerate learning in noisy structured domains where so far humans have been merely used as labelers or as designers of initial structure of the model.

Adaptive exploration uses active learning principles to improve the efficiency of autonomous robotic surveys. This work considers an important and understudied aspect of autonomous exploration: in situ validation of remote sensing measurements. We focus on high- dimensional sensor data with a specific case study of spectroscopic mapping. A field robot refines an orbital image by measuring the surface at many wavelengths. We introduce a new objective function based on spectral unmixing that seeks pure spectral signatures to accurately model diluted remote signals. This objective reflects physical properties of the multi-wavelength data. The rover visits locations that jointly improve its model of the environment while satisfying time and energy constraints. We simulate exploration using alternative planning approaches, and show proof of concept results with the canonical spectroscopic map of a mining district in Cuprite, Nevada.

The overarching goal of music theory is to explain the inner workings of a musical composition by examining the structure of the composition. Schenkerian music theory supposes that Western tonal compositions can be viewed as hierarchies of musical objects. The process of Schenkerian analysis reveals this hierarchy by identifying connections between notes or chords of a composition that illustrate both the small- and large-scale construction of the music. We present a new probabilistic model of this variety of music analysis, details of how the parameters of the model can be learned from a corpus, an algorithm for deriving the most probable analysis for a given piece of music, and both quantitative and human-based evaluations of the algorithm's performance. This represents the first large-scale data-driven computational approach to hierarchical music analysis.

Most typical statistical and machine learning approaches to time series modeling optimize a single-step prediction error. In multiple-step simulation, the learned model is iteratively applied, feeding through the previous output as its new input. Any such predictor however, inevitably introduces errors, and these compounding errors change the input distribution for future prediction steps, breaking the train-test i.i.d assumption common in supervised learning. We present an approach that reuses training data to make a no-regret learner robust to errors made during multi-step prediction. Our insight is to formulate the problem as imitation learning; the training data serves as a "demonstrator" by providing corrections for the errors made during multi-step prediction. By this reduction of multi-step time series prediction to imitation learning, we establish theoretically a strong performance guarantee on the relation between training error and the multi-step prediction error. We present experimental results of our method, DaD, and show significant improvement over the traditional approach in two notably different domains, dynamic system modeling and video texture prediction.

This paper describes a learning-based approach for automatic derivation of word variant forms bythe suffixation process. We employ the sequence labeling technique, which entails learning when to preserve, delete, substitute, or add a letter to form a new word from a given word. The features used by the learner are based on characters, phonetics, and hyphenation positions of the given word. To ensure that our system is robust to word variants that can arise from different forms of a root word, we generate multiple variant hypothesis for each word based on the sequence labeler's prediction. We then filter out ill-formed predictions, and create clusters of word variants by merging together a word and its predicted variants with other words and their predicted variants provided the groups share a word in common. Our results show that this learning-based approach is feasible for the task and warrants further exploration.

Backdoors are a powerful tool to obtain efficient algorithms for hard problems. Recently, two new notions of backdoors to planning were introduced. However, for one of the new notions (i.e., variable-deletion) only hardness results are known so far. In this work we improve the situation by defining a new type of variable-deletion backdoors based on the extended causal graph of a planning instance. For this notion of backdoors several fixed-parameter tractable algorithms are identified. Furthermore, we explore the capabilities of polynomial time preprocessing, i.e., we check whether there exists a polynomial kernel. Our results also show the close connection between planning and verification problems such as Vector Addition System with States (VASS).

In this game, each player deal with enormous sizes such as a smart grid is rapidly increasing associated with a variable keeps providing probability distributions in AI communities. The distributed constraint optimization over its domain, and tries to minimize the regret, problem (DCOP for short) is arguably the most which is the average additional cost incurred by the probability studied problem in this setting, where the goal is to find an distributions against the strategy of outputting a best assignment that minimizes the total sum of costs incurred single value all the time. We can make the regret of each by (local) cost functions. Since it takes a prohibitively long agent arbitrarily small by utilizing the multiplicative weights time to exactly solve DCOP, we need to resort to incomplete method. Finally, we round the obtained probability distributions algorithms, and a plethora of incomplete algorithms to integer values. We prove that our method converges have been proposed in the literature, such as local search to a certain kind of equilibrium, called a coarse correlated based algorithms (Maheswaran, Pearce, and Tambe 2004; equilibrium. Zhang et al. 2005), inference based algorithms (Farinelli We empirically compare our methods with previous stateof-the-art et al. 2008), graph based algorithms (Bowring et al. 2008; methods. We demonstrate that our methods are Kiekintveld et al. 2010), divide-and-coordinate based algorithms scalable, and that DMW-Game outperforms other methods (Vinyals, Rodriguez-Aguilar, and Cerquides 2010; in terms of solution quality and efficiency. Hatano and Hirayama 2013), and sampling based algorithms (Ottens, Dimitrakakis, and Faltings 2012; Nguyen, Yeoh, and Lau 2013).