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.

Humans have developed jurisprudence as a mechanism to solve conflictive situations by using past experiences. Following this principle, we propose an approach to enhance a multi-agent system by adding an authority which is able to generate new regulations whenever conflicts arise. Regulations are generated by learning from previous similar situations, using a machine learning technique (based on Case-Based Reasoning) that solves new problems using previous experiences. This approach requires: to be able to gather and evaluate experiences; and to be described in such a way that similar social situations require similar regulations. As a scenario to evaluate our proposal, we use a simplified version of a traffic scenario, where agents are traveling cars. Our goals are to avoid collisions between cars and to avoid heavy traffic. These situations, when happen, lead to the synthesis of new regulations. At each simulation step, applicable regulations are evaluated in terms of their effectiveness and necessity. Overtime the system generates a set of regulations that, if followed, improve system performance (i.e. goal achievement).

We propose a logical framework to represent and reason about agent interactions in normative systems. Our starting point is a dynamic logic of propositional assignments whose satisfiability problem is PSPACE-complete. We show that it embeds Coalition Logic of Propositional Control CL-PC and that various notions of ability and capability can be captured in it. We illustrate it on a water resource management case study. Finally, we show how the logic can be easily extended in order to represent constitutive rules which are also an essential component of the modelling of social reality.

Temporal regulations are needed to express commitments The proposal of Elisa Marengo's thesis is to extend to achieve something and in a specified order. For commitment protocols in order to (i) allow for expressing instance, an insurance company commits to paying an innetwork commitments to temporal regulations, and surgeon for a procedure only after a covered patient (ii) to supply a tool for expressing laws, conventions has undergone the procedure. Patterns of interaction, instead, and the like, in order to specify legal interactions.

In this paper we propose a research agenda on the use of information markets as tools to collect, aggregate and analyze citizens’ opinions, expectations and preferences from social media in order to support public policy design and implementation. We argue that markets are institutional settings able to efficiently allocate scarce resources, aggregate and disseminate information into prices and accommodate hedging against various types of risks. We discuss various types of information markets, as well as address the participation of both human and computational agents in such markets.

In the last years, the blogosphere has become a vital part of the web, covering a variety of different points of view and opinions on political and event-related topics such as immigration, election campaigns, or economic developments. Tracking the public opinion is usually done by conducting surveys resulting in significant costs both for interviewers and persons consulted. In this paper, we propose a method for extracting political trends in the blogosphere.To this end, we apply sentiment and time series analysis techniques in combination with aggregation methods on blog data to estimate the temporal development of opinions on politicians.

We present a description of the PhD thesis which aims to propose a rule-based query answering method for relational data. In this approach we use an additional knowledge which is represented as a set of rules and describes the source data at concept (ontological) level. Queries are posed in the terms of abstract level. We present two methods. The first one uses hybrid reasoning and the second one exploits only forward chaining. These two methods are demonstrated by the prototypical implementation of the system coupled with the Jess engine. Tests are performed on the knowledge base of the selected economic crimes: fraudulent disbursement and money laundering.

The problem of organizing information for multidocument summarization so that the generated summary is coherent has received relatively little attention. While sentence ordering for single document summarization can be determined from the ordering of sentences in the input article, this is not the case for multidocument summarization where summary sentences may be drawn from different input articles. In this paper, we propose a methodology for studying the properties of ordering information in the news genre and describe experiments done on a corpus of multiple acceptable orderings we developed for the task. Based on these experiments, we implemented a strategy for ordering information that combines constraints from chronological order of events and topical relatedness. Evaluation of our augmented algorithm shows a significant improvement of the ordering over two baseline strategies.

Our corpus comprises 752 texts, culled from newspapers of U.S. border states (approximately 75 texts per state). Immigration is a national issue in the United States; Because four states border Mexico, we selected four however, regional implications differ because of matching states (of the 11) that border Canada. To do so, immigrants' varying effects on local economies. These we considered the following criteria for all 15 terrestrial implications are made manifest in the reportage of local border states: total population, immigrant population, newspapers, which, while ostensibly portraying length of international border, and political leaning. These "objective" language, may reveal the narrative of local data were input into a custom PERL script designed to perspectives on national issues.

IEA / AIE-11 will be held June 28-July 4, 2011, in Syracuse, This page includes forthcoming AAAI sponsored conferences, conferences presented New York USA. by AAAI Affiliates, and conferences held in cooperation with AAAI. IE'11 will be held July 5-8, 2011 at Nottingham The Twenty-Fourth International Florida 2011 Robotics: Science and Systems AAAI Spring Symposium Series will be AI Research Society Conference. RSS 2011 will be held held March 21-23, 2011 in Stanford, Flairs-2011 will be held May 18-20, June 27-30, 2011, at the University of California. ICAPS 2011 will be held June 11-16, Artificial Intelligence. UAI 2011 will ICWSM-11 will be held in July 17-21 in 2011 in Freiburg, Germany.