In order for agents to be able to act intelligently in an environment, a first necessary step is to become aware of the current situation in the environment. Forming such awareness is not a trivial matter. Appropriate observations should be selected by the agent, and the observation results should be interpreted and combined into one coherent picture. Humans use dedicated mental models which represent the relationships between various observations and the formation of beliefs about the environment, which then again direct the further observations to be performed. In this paper, a generic agent model for situation awareness is proposed that is able to take a mental model as input, and utilize this model to create a picture of the current situation. In order to show the suitability of the approach, it has been applied within the domain of F-16 fighter pilot training for which a dedicated mental model has been specified, and simulations experiments have been conducted.

We describe an innovative application of a novel game-theoretic approach for a \textit{national scale} security deployment. Working with the United States Transportation Security Administration (TSA), we have developed a new application called GUARDS to allocate the TSA's limited resources across hundreds of security activities to provide protection at over 400 United States airports. Similar security applications (e.g., ARMOR and IRIS) have focused on one-off tailored applications and one security activity (e.g. checkpoints) per application, GUARDS on the other hand faces three new key issues: (i) reasoning about hundreds of heterogeneous security activities; (ii) reasoning over diverse potential threats; (iii) developing a system designed for hundreds of end-users. Since a national deployment precludes tailoring to specific airports, our key ideas are: (i) creating a new game-theoretic framework that allows for heterogeneous defender activities and compact modeling of a large number of threats; (ii) developing an efficient solution technique based on general purpose Stackelberg game solvers; (iii) taking a partially centralized approach for knowledge acquisition. The scheduling assistant has been delivered to the TSA and is currently undergoing evaluation for scheduling practices at an undisclosed airport. If successful, the TSA intends to incorporate the system into their unpredictable scheduling practices nationwide.

Combining graph regularization with nonnegative matrix (tri-)factorization (NMF) has shown great performance improvement compared with traditional nonnegative matrix (tri-)factorization models due to its ability to utilize the geometric structure of the documents and words. In this paper, we show that these models are not well-defined and suffering from trivial solution and scale transfer problems. In order to solve these common problems, we propose two models for graph regularized nonnegative matrix (tri-)factorization, which can be applied for document clustering and co-clustering respectively. In the proposed models, a Normalized Cut-like constraint is imposed on the cluster assignment matrix to make the optimization problem well-defined. We derive a multiplicative updating algorithm for the proposed models, and prove its convergence. Experiments of clustering and co-clustering on benchmark text data sets demonstratethat the proposed models outperform the originalmodels as well as many other state-of-the-art clustering methods.

The process of extracting useful knowledge from large datasets has become one of the most pressing problems in today’s society. The problem spans entire sectors, from scientists to intelligence analysts and web users, all of whom are constantly struggling to keep up with the larger and larger amounts of content published every day. With this much data, it is often easy to miss the big picture. In this paper, we investigate methods for automatically connecting the dots – providing a structured, easy way to navigate within a new topic and discover hidden connections. We focus on the news domain: given two news articles, our system automatically finds a coherent chain linking them together. For example, it can recover the chain of events leading from the decline of home prices (2007) to the health-care debate (2009). We formalize the characteristics of a good chain and provide efficient algorithms to connect two fixed endpoints. We incorporate user feedback into our framework, allowing the stories to be refined and personalized. Finally, we evaluate our algorithm over real news data. Our user studies demonstrate the algorithm's effectiveness in helping users understanding the news.

Entity linking maps name mentions in the documents to entries in a knowledge base through resolving the name variations and ambiguities. In this paper, we propose three advancements for entity linking. Firstly, expanding acronyms can effectively reduce the ambiguity of the acronym mentions. However, only rule-based approaches relying heavily on the presence of text markers have been used for entity linking. In this paper, we propose a supervised learning algorithm to expand more complicated acronyms encountered, which leads to 15.1% accuracy improvement over state-of-the-art acronym expansion methods. Secondly, as entity linking annotation is expensive and labor intensive, to automate the annotation process without compromise of accuracy, we propose an instance selection strategy to effectively utilize the automatically generated annotation. In our selection strategy, an informative and diverse set of instances are selected for effective disambiguation. Lastly, topic modeling is used to model the semantic topics of the articles. These advancements give statistical significant improvement to entity linking individually. Collectively they lead the highest performance on KBP-2010 task.

Understanding extreme events, such as hurricanes or forest fires, is of paramount importance because of their adverse impacts on human beings. Such events often propagate in space and time. Predicting-even a few days in advance-what locations will get affected by the event tracks could benefit our society in many ways. Arguably, simulations from “first principles,” where underlying physics-based models are described by a system of equations, provide least reliable predictions for variables characterizing the dynamics of these extreme events. Data-driven model building has been recently emerging as a complementary approach that could learn the relationships between historically observed or simulated multiple, spatio-temporal ancillary variables and the dynamic behavior of extreme events of interest. While promising, the methodology for predictive learning from such complex data is still in its infancy. In this paper, we propose a dynamic networks-based methodology for in-advance prediction of the dynamic tracks of emerging extreme events. By associating a network model of the system with the known tracks, our method is capable of learning the recurrent network motifs that could be used as discriminatory signatures for the event's behavioral class. When applied to classifying the behavior of the hurricane tracks at their early formation stages in Western Africa region, our method is able to predict whether hurricane tracks will hit the land of the North Atlantic region at least 10-15 days lead lag time in advance with more than 90% accuracy using 10-fold cross-validation. To the best of our knowledge, no comparable methodology exists for solving this problem using data-driven models.

An important problem in voting is that agents may misrepresent their preferences in order to obtain a more preferred outcome. Unfortunately, this phenomenon has been shown to be inevitable in the case of resolute, i.e., single-valued, social choice functions. In this paper, we introduce a variant of Maskin-monotonicity that completely characterizes the class of pairwise irresolute social choice functions that are group-strategyproof according to Kelly's preference extension.The class is narrow but contains a number of appealing Condorcet extensions such as the minimal covering set and the bipartisan set, thereby answering a question raised independently by Barbera (1977) and Kelly (1977). These functions furthermore encourage participation and thus do not suffer from the no-show paradox (under Kelly's extension).

In this paper we describe a software assistant agent that can proactively assist human users situated in a time-constrained environment to perform normative reasoning--reasoning about prohibitions and obligations--so that the user can focus on her planning objectives. In order to provide proactive assistance, the agent must be able to 1) recognize the user's planned activities, 2) reason about potential needs of assistance associated with those predicted activities, and 3) plan to provide appropriate assistance suitable for newly identified user needs. To address these specific requirements, we develop an agent architecture that integrates user intention recognition, normative reasoning over a user's intention, and planning, execution and replanning for assistive actions. This paper presents the agent architecture and discusses practical applications of this approach.

In this paper we introduce and analyze social distance games, a family of non-transferable utility coalitional games where an agent's utility is a measure of closeness to the other members of the coalition. We study both social welfare maximisation and stability in these games using a graph theoretic perspective. We use the stability gap to investigate the welfare of stable coalition structures, and propose two new solution concepts with improved welfare guarantees. We argue that social distance games are both interesting in themselves, as well as in the context of social networks.

Transfer learning is the process of using knowledge gained while solving one problem to solve a new, previously unencountered problem. Current research has concentrated on analogical transfer - a mechanic is able to fix a type of car he has never seen before by comparing it to cars he has fixed before. This approach is typical of case-based reasoning systems and has been successful on a wide variety of problems [Watson, 1997]. When a new problem is encountered, a database of previously solved problems is searched for a problem with similar features. The solution to the most similar problem is selected, adapted and then applied to the new problem. Similar methods exist for adapting reinforcement learning policies [Taylor and Stone, 2009]. We refer to the above approaches as solution adaptation algorithms - a pair of problems are matched on similarity and the solution to the first problem, after some adaptation, is applied to the second problem. The solution adaptation approach requires three things.