One common use for swarming agents is in social simulation. This paper reports on such a model developed to track protest activities at the May 2012 NATO summit in Chicago. The use of apoptotic stigmergic agents allows the model to run on-line, consuming two kinds of external data and reporting its results in real time.

Traditional network diagnosis methods of Client-Terminal Device (CTD) problems tend to be laborintensive, time consuming, and contribute to increased customer dissatisfaction. In this paper, we propose an automated solution for rapidly diagnose the root causes of network performance issues in CTD. Based on a new intelligent inference technique, we create the Intelligent Automated Client Diagnostic (IACD) system, which only relies on collection of Transmission Control Protocol (TCP) packet traces. Using soft-margin Support Vector Machine (SVM) classifiers, the system (i) distinguishes link problems from client problems and (ii) identifies characteristics unique to the specific fault to report the root cause. The modular design of the system enables support for new access link and fault types. Experimental evaluation demonstrated the capability of the IACD system to distinguish between faulty and healthy links and to diagnose the client faults with 98% accuracy. The system can perform fault diagnosis independent of the user's specific TCP implementation, enabling diagnosis of diverse range of client devices

As a result, it is good to read these articles from a practical perspective. Papers that document deployed systems clarify the motivating application constraints, the match (and mismatch) between problems and technology, the innovations required to surmount barriers to deployment, and the impact of technology on application through practical measures of cost and benefit. Other articles describe applications that are almost feasible, drawn from papers in the IAAI emergent applications track. These papers provide a window into the search for viable applications at an earlier stage in the process of mating task with technology. All of the articles supply insight into the core question of what is feasible and why, which is a useful lens for us, as readers, to employ in viewing our own work. This special issue of AI Magazine contains expanded versions of five papers that describe deployed applications and two papers that discuss emergent applications from IAAI-11 (the article by Warrick and colleagues is from IAAI-10).

We then describe the process of getting to deployment, covering Task learning provides tremendous value for technical challenges encountered, unit CPOF by enabling individual users and collective engagement activities, and an Army-led assessment command staffs to create customized, automated of the technology. Next, we discuss the fielding information-management schemes tailored to of the technology, including tradeoffs made to individual preferences and the staff's standard ensure deployability, the impact of the deployed operating procedures, without needing software technology, and lessons learned. We close with a engineers for extensive recoding. Task learning can summary of ongoing work to deploy additional reduce work load and stress, can enable managing functionality and to broaden the user base for task more tasks with better effectiveness, and can facilitate learning in CPOF.

One of the original applications of crowdsourcing the construction of knowledge is Wikipedia, which relies entirely on people to contribute, extend, and modify the representation of knowledge. This paper presents a case for combining AI and wisdom of the crowds for the social construction of knowledge. Our social-computational approach to collective intelligence combines the strengths of human cognitive diversity in producing content and the capabilities of an AI, through methods such as topic modeling, to link and synthesize across these human contributions. In addition to drawing from established domains such as Wikipedia for inspiration and guidance, we present the design of a system that incorporates AI into wisdom of the crowds to develop a knowledge base on sustainability. In this setting the AI plays the role of scholar, as might many of the other participants, drawing connections and synthesizing across contributions. We close with a general discussion, speculating on educational implications and other roles that an AI can play within an otherwise collective human intelligence.

Existing personalisation approaches, such as collaborative filtering or content based recommendations, are highly dependent on the domain and/or the source of the data. Therefore, there is a need for more accurate means to capture and model the interests of the user across domains, and to interlink them in a semantically-enhanced interest graph. We propose a new approach for multi-source, cross-genre recommendations that can exploit the heterogeneous nature of user profile data, which has been aggregated from multiple personalised web services, such as blogs, wikis and microblogs. Our approach is based on the Spreading Activation model that exploits intrinsic links between entities across a number of data sources. The proposed method is highly customizable and applicable both to generic and specific recommendation scenarios and use cases. With the growing number of Social Web applications in the enterprise (blogs, wikis, micro blogging, etc.), it becomes difficult for knowledge workers to avoid content overload and to quickly identify relevant people, communities and information. We demonstrate the application of our approach in an industrial use case that involves recommendation of social semantic data across multiple services in a distributed collaborative environment.

The Web is a giant repository of resources (Service and content), where Discovery and Recommendation systems are used to deliver the best ranked list of relevant web resources that meet user requirements. Nowadays, these systems are based on the simulation and automation of the user search criteria, considering the relation between consumption trends and the different kinds of users’ relationships with their virtual and physical environment, based on the information from the Social Web and mobile device sensors among others. These systems are executed once an explicit query of the user has been received; however, there are resources that are useful in specific situations, where these resources have high probability to be consumed, but, due to absence of a query they are not recommended to the users. In this regard, the question is: how to make a successful Web Resource Recommendation without the user query? In order to answer the question, this research proposal presents a novel approach to Recommend Web Resources based on Dynamic Prediction of User Consumption on the Social Web, which emulates the user behavior, the resource dynamism and the context opportunities, in real time, catching the best situations to make an asynchronous (unexpected by the user) recommendation of a useful Resources; and boost Web Resources consumption.

This paper reports ongoing work in the “Networks of Texts and People” project, which is developing methods to visualize the social and epistemological contexts of information contained in blogs. Here, we propose an approach to visualize information diffusion and polarization in the blogosphere, with two novel characteristics. Firstly, we demonstrate how text content can be analyzed and visualized as key statements, rather than as keywords. Secondly, we sketch and discuss ideas for a visual analytic tool that integrates data about blog networks with data about the occurrence of related key statements in blog posts.

Our aim in this book is to present a survey of work on the computational aspects of cooperative game theory. We begin by formally defining transferable utility games in characteristic function form, and introducing key solution concepts such as the core and the Shapley value. We then discuss two major issues: identifying compact representations for games, and efficiently computing solution concepts for games. ISBN 9781608456529, 168 pages.

Crowdsourcing markets such as Amazon’s Mechanical Turk provide an enormous potential for accomplishing work by combining human and machine computation. Today crowdsourcing is mostly used for massive parallel information processing for a variety of tasks such as image labeling. However, as we move to more sophisticated problem-solving there is little knowledge about managing dependencies between steps and a lack of tools for doing so. As the contribution of this paper, we present a concept of an executable, model-based programming language and a general purpose framework for accomplishing more sophisticated problems. Our approach is inspired by coordination theory and an analysis of emergent collective intelligence. We illustrate the applicability of our proposed language by combining machine and human computation based on existing interaction patterns for several general computation problems.