It is well known that argumentation can usefully be analysed as a distinct, if complex, type of speech act. Speech acts that form a part of argumentative discourse, and in particular, of argumentative dialogue, can be seen as anchors for the establishment of inferences between propositions in the domain of discourse. Most often, the speech acts that directly give rise to inference are implicit, but can be drawn out in analysis by consideration of the type of dialogue game being played. AI approaches to argumentation often focus solely on such inferences as the means by which persuasion can be effected – but this is in contrast with psychological and rhetorical models which have long recognised the role played by extra-logical features of the dialogical context. These ‘peripheral’ cues can not only affect persuasive effect of the logical, ‘central’ argumentation, but can override and dominate it. This paper presents a theory which allows both central and peripheral aspects of argumentation to be represented in a coherent analytical account based on the sequences of speech acts which constitute dialogues.

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.

Rather, the goal is to Microtext media (Ellen, 2011), such as SMS, IM, Twitter, gather relevant messages, organize them, and extract some and text chat, have in common that they use short strings other kind of useful information from them, such as how for immediate communication or broadcast. Microtext can well a team is performing or what people are talking about be construed as one form of micro-messaging (e.g., and when. However, micro-messages do not exist in a Milstein, et al., 2008) which we extend here to include any vacuum; they are contextually oriented and may be part of of a number of other modalities (e.g., telephone calls, a larger network of communications which includes email, face-to-face interaction) used for short, immediate and telephone and other media, including "macro-text." Given (potentially) persistent message passing among this, we have found that natural language processing of the coordinating agents. In this paper, we describe several microtext must be paired with temporal or network recent attempts to study micro-messaging military and analysis of the context. To demonstrate this process, we related organizational contexts.

How widespread is the phenomenon of commenting or editing a tweet in the practice of retweeting by members of political communities in Twitter? What is the nature of comments(agree/disagree), or of edits (change audience, change meaning, curate content). Being able to answer these questions will provide knowledge that will help answering other questions such as: what are the topics, events, people that attract more discussion (in forms of commenting) or controversy (agree/disagree)? Who are the users who engage in the processing of curating content by inserting hashtags or adding links? Which political community shows more enthusiasm for an issue and how broad is the base of engaged users? How can detection of agreement/disagreement in conversations inform sentiment analysis - the technique used to make predictions (who will win an election) or support insightful analytics (which policy issue resonates more with constituents). We argue that is necessary to go beyond the much-adopted aggregate text analysis of the volume of tweets, in order to discover and understand phenomena at the level of single tweets. This becomes important in the light of the increase in the number of human-mimicking bots in Twitter. Genuine interaction and engagement can be better measured by analyzing tweets that display signs of human intervention. Editing the text of an original tweet before it is retweeted, could reveal mindful user engagement with the content, and therefore, would allow us to perform sampling among real human users. This paper presents work in progress that deals with the challenges of discovering retweets that contain comments or edits, and outlines a machine-learning based strategy for classifying the nature of such comments.

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.

Traditionally, security decisions have been made without explicitly accounting for adaptive, intelligent attackers. Recent game theoretic security models have explicitly included attacker response in computing randomized security policies. Techniques to date, however, generally fail to explicitly account for interdependence between the targets to be secured, which is of vital importance in a variety of domains, including cyber, supply chain, and critical infrastructure security. We introduce a novel framework for computing optimal randomized security policies in networked domains which extends previous approaches in two ways. First, we extend previous linear programming techniques for Stackelberg security games to incorporate benefits and costs of arbitrary security configurations on individual assets. Second, we offer a principled model of failure cascades that allows us to capture both the direct and indirect value of assets. Finally, we use our framework to analyze four models, two based on random graph generation models, a simple model of interdependence between critical infrastructure and key resource sectors, and a model of the Fedwire interbank payment network.

We build a mechanism to form an ontology of entities which improves a relevance of matching and searching microtext. Ontology construction starts from the seed entities and mines the web for new entities associated with them. To form these new entities, machine learning of syntactic parse trees (syntactic generalization) is applied to form commonalities between various search results for existing entities on the web. Ontology and syntactic generalization are applied to relevance improvement in search and text similarity assessment in commercial setting; evaluation results show substantial contribution of both sources to microtext processing.

End-user programming raises the possibility that the people who know best what a software system should do will be able to customize, remedy original programming defects and adapt systems as requirements change. As computing increasingly enters the home and workplace, the need for such tools is high, but state of practice approaches offer very limited capability. We describe the Interactive Bootstrapped Learning (iBL) system which allows users to modify code by interactive teaching similar to human instruction. It builds on an earlier system focused on exploring how machine learning can be used to compensate for limited instructional content. iBL provides an end-to-end solution in which user-iBL dialog gradually refines a hypothesis about what transformation to a target code base will best achieve user intent. The approach integrates elements of many AI technologies including machine learning, dialog management, AI planning and automated model construction.

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.

Nonnegative Matrix Factorization (NMF) techniques has aroused considerable interests from the field of artificial intelligence in recent years because of its good interpretability and computational efficiency. However, in many real world applications, the data features usually evolve over time smoothly. In this case, it would be very expensive in both computation and storage to rerun the whole NMF procedure after each time when the data feature changing. In this paper, we propose Evolutionary Nonnegative Matrix Factorization (eNMF), which aims to incrementally update the factorized matrices in a computation and space efficient manner with the variation of the data matrix. We devise such evolutionary procedure for both asymmetric and symmetric NMF. Finally we conduct experiments on several real world data sets to demonstrate the efficacy and efficiency of eNMF.