Software agents can be used to automate many of the tedious, time-consuming information processing tasks that humans currently have to complete manually. However, to do so, agent plans must be capable of representing the myriad of actions and control flows required to perform those tasks. In addition, since these tasks can require integrating multiple sources of remote information ? typically, a slow, I/O-bound process ? it is desirable to make execution as efficient as possible. To address both of these needs, we present a flexible software agent plan language and a highly parallel execution system that enable the efficient execution of expressive agent plans. The plan language allows complex tasks to be more easily expressed by providing a variety of operators for flexibly processing the data as well as supporting subplans (for modularity) and recursion (for indeterminate looping). The executor is based on a streaming dataflow model of execution to maximize the amount of operator and data parallelism possible at runtime. We have implemented both the language and executor in a system called THESEUS. Our results from testing THESEUS show that streaming dataflow execution can yield significant speedups over both traditional serial (von Neumann) as well as non-streaming dataflow-style execution that existing software and robot agent execution systems currently support. In addition, we show how plans written in the language we present can represent certain types of subtasks that cannot be accomplished using the languages supported by network query engines. Finally, we demonstrate that the increased expressivity of our plan language does not hamper performance; specifically, we show how data can be integrated from multiple remote sources just as efficiently using our architecture as is possible with a state-of-the-art streaming-dataflow network query engine.

Concept Analysis provides a principled approach to effective management of wide area information systems, such as the Nebula File System and Interface. This not only offers evidence to support the assertion that a digital library is a bounded collection of incommensurate information sources in a logical space, but also sheds light on techniques for collaboration through coordinated access to the shared organization of knowledge.

Multi-step ahead forecasting is still an open challenge in time series forecasting. Several approaches that deal with this complex problem have been proposed in the literature but an extensive comparison on a large number of tasks is still missing. This paper aims to fill this gap by reviewing existing strategies for multi-step ahead forecasting and comparing them in theoretical and practical terms. To attain such an objective, we performed a large scale comparison of these different strategies using a large experimental benchmark (namely the 111 series from the NN5 forecasting competition). In addition, we considered the effects of deseasonalization, input variable selection, and forecast combination on these strategies and on multi-step ahead forecasting at large. The following three findings appear to be consistently supported by the experimental results: Multiple-Output strategies are the best performing approaches, deseasonalization leads to uniformly improved forecast accuracy, and input selection is more effective when performed in conjunction with deseasonalization.

In this paper we bring to bear some new tools from statistical learning on the analysis of roll call data. We present a new data-driven model for roll call voting that is geometric in nature. We construct the model by adapting the "Partition Decoupling Method," an unsupervised learning technique originally developed for the analysis of families of time series, to produce a multiscale geometric description of a weighted network associated to a set of roll call votes. Central to this approach is the quantitative notion of a "motivation," a cluster-based and learned basis element that serves as a building block in the representation of roll call data. Motivations enable the formulation of a quantitative description of ideology and their data-dependent nature makes possible a quantitative analysis of the evolution of ideological factors. This approach is generally applicable to roll call data and we apply it in particular to the historical roll call voting of the U.S. House and Senate. This methodology provides a mechanism for estimating the dimension of the underlying action space. We determine that the dominant factors form a low- (one- or two-) dimensional representation with secondary factors adding higher-dimensional features. In this way our work supports and extends the findings of both Poole-Rosenthal and Heckman-Snyder concerning the dimensionality of the action space. We give a detailed analysis of several individual Senates and use the AdaBoost technique from statistical learning to determine those votes with the most powerful discriminatory value. When used as a predictive model, this geometric view significantly outperforms spatial models such as the Poole-Rosenthal DW-NOMINATE model and the Heckman-Snyder 6-factor model, both in raw accuracy as well as Aggregate Proportional Reduced Error (APRE).

Discovering latent connections between seemingly unconnected documents and constructing "stories" from scattered pieces of evidence are staple tasks in intelligence analysis. We have worked with government intelligence analysts to understand the strategies they use to make connections. Beyond techniques like clustering that aim to provide an initial broad summary of large document collections, an important goal of analysts in this domain is to assimilate and synthesize fine grained information from a smaller set of foraged documents. Further, analysts' domain expertise is crucial because it provides rich contextual background for making connections and thus the goal of KDD is to augment human discovery capabilities, not supplant it. We describe a visual analytics system we have built - Analyst's Workspace (AW) - that integrates browsing tools with a storytelling algorithm in a large screen display environment. AW helps analysts systematically construct stories of desired fidelity from document collections and helps marshall evidence as longer stories are constructed.

US Federal Legislation is a common subject of discussion and advocacy on the web. The contents of bills present a significant challenge to both experts and average citizens due to their length and complex legal language. To make bills more accessible to the general public, we present Many Bills: a web-based visualization prototype that reveals the underlying semantics of a bill. We classify the sections of a bill into topics and visualize them using different colors. Further, using information retrieval techniques, we locate sections that don't seem to fit with the overall topic of the bill. To highlight outliers in our `misfit mode', we visualize them in red, which builds a contrast against the remaining gray sections. Both topic and misfit visualizations provide an overview and detail view of bills, enabling users to read individual sections of a bill and compare topic patterns across multiple bills. We obtained initial user feedback and continue collecting label corrections from users through the interface.

The goal for attackers is to move from one side of the graph to the Border security is a key element of national security policy other (represented by sets of source and target nodes); this for any sovereign nation. In the United States, the Border represents a typical scenario of crossing an open region from Patrol deploys thousands of agents integrated with technology one side of the border to destination points in the interior of (e.g., vehicles, cameras, sensors) and infrastructure the county. The paths between the source and target nodes (e.g., fences, checkpoints) to prevent illegal entry of people may represent major or minor roads, or paths suitable for and goods into the country along vast land borders with travel on foot. We use weights on the edges to represent Canada and Mexico. The problem of border security is incredibly the relative speed/cost of transit on the different paths (for complex, due to the diversity and volume of illegal example, it may be must slower and more difficult to use activity that must be controlled, the variety of resources that a foot path than a major highway). Nodes may represent can be deployed to secure the border, and the differences in intersections, checkpoints, or other important waypoints.

Automatic detection of persuasion is essential for machine interaction on the social web. To facilitate automated persuasion detection, we present a novel microtext corpus derived from hostage negotiation transcripts as well as a detailed manual (codebook) for persuasion annotation. Our corpus, called the NPS Persuasion Corpus, consists of 37 transcripts from four sets of hostage negotiation transcriptions. Each utterance in the corpus is hand annotated for one of nine categories of persuasion based on Cialdini’s model: reciprocity, commitment, consistency, liking, authority, social proof, scarcity, other, and not persuasive. Initial results using three supervised learning algorithms (Na ̈ve Bayes, Maximum Entropy, and Support Vector Machines) combined with gappy and orthogonal sparse bigram feature expansion techniques show that the annotation process did capture machine learnable features of persuasion with F-scores better than baseline.

Model-driven visualization (MDV) is a novel framework that supports more effective, intelligent user interfaces to improve decision making in complex environments by coupling cognitive and perceptual theories of information processing with advanced artificial intelligence methods. It embeds empirical and theory driven approaches for identifying and prioritizing data based on the information requirements and needs of the human decision maker within intelligent agents. The agents automatically deliver and present information based on its likely value using visualizations that best convey that information to the user(s) of the system. Agents also reason about the context and constraints of the user, environment, and display to enable a higher degree of personalization within an interactive user interface (e.g., by drawing a user’s attention to interesting aspects of the data such as trends, anomalies, and patterns). We apply cognitive systems engineering processes to help identify the information available to individuals and/or teams, where it resides, where it is needed, and ultimately how to create the mappings required in connecting critical information to those who need it with innovative visualizations that most effectively support the end user. This paper describes the application of MDV to intelligently deliver timely, mission-critical information by adapting a Common Tactical Picture (CTP) display used for maritime situation awareness, threat assessment, and decision support.

In many multiagent scenarios, groups of participants (known as coalitions) may attempt to cooperate, seeking to increase the benefits realized by the members. Depending on the scenario, such cooperation may be benign, or may be unwelcome or even forbidden (often called collusion). Coalitions can present a problem for many multiagent systems, potentially undermining the intended operation of systems. In this paper, we present a technique for detecting the presence of coalitions (malicious or otherwise), and identifying their members. Our technique employs clustering in benefit space, a high-dimensional feature space reflecting the benefit flowing between agents, in order to identify groups of agents who are similar in terms of the agents they are favoring. A statistical approach is then used to characterize candidate clusters, identifying as coalitions those groups that favor their own members to a much greater degree than the general population. We believe that our approach is applicable to a wide range of domains. Here, we demonstrate its effectiveness within a simulated marketplace making use of a trust and reputation system to cope with dishonest sellers. Many trust and reputation proposals readily acknowledge their ineffectiveness in the face of collusion, providing one example of the importance of the problem. While certain aspects of coalitions have received significant attention (e.g., formation, stability, etc.), relatively little research has focused on the problem of coalition identification. We believe our research represents an important step towards addressing the challenges posed by coalitions.