Normal form games are one of the most familiar representations for modeling interactions among multiple agent. However, modeling many realistic interactions between agents results in games that are extremely large. In these cases computing standard solutions like Nash equilibrium may be intractable. To overcome this issue the idea of abstraction has been investigated, most prominently in research on computer Poker. Solving a game using abstraction requires using some method to simplify the game before it is analyzed. We study a new variation for solving normal form games using abstraction that is based on finding and solving suitable sub games. We compare this method with several variations of a common type of abstraction based on clustering similar strategies.

Creation of summaries of events of interest from multitude of unstructured data is a challenging task commonly faced by intelligence analysts while seeking increased situational awareness. This paper proposes a framework called Storyboarding that leverages unstructured text and images to explain events as sets of sub-events. The framework first generates a textual context for each human face detected from images and then builds a chain of coherent documents where two consecutive documents of the chain contain a common theme as well as a context. Storyboarding helps analysts quickly narrow down large number of possibilities to a few significant ones for further investigation. Empirical studies on Wikipedia documents, images and news articles show that Storyboarding is able to provide deeper insights on events of interests.

The Power TAC simulation emphasizes the strategic problems that broker agents face in managing the economics of a smart grid. The brokers must make trades in multiple markets and to be successful, brokers must make many good predictions about future supply, demand,and prices. Clearing price prediction is an important part of the broker’s wholesale market strategy because it helps the broker to make intelligent decisions when purchasing energy at low cost in a day-ahead market. I describe my work on using machine learning methods to predict prices in the Power TAC wholesale market, which will be used in future bidding strategies.

MetaShare is a knowledge-based system that supports the creation of data management plans and provides the functionality to support researchers as they implement those plans. MetaShare is a community-based, user-driven system that is being designed around the parallels of the scientific data life cycle and the development cycle of knowledge-based systems. MetaShare will provide recommendations and guidance to researchers based on the practices and decisions of similar projects. Using formal knowledge representation in the form of ontologies and rules, the system will be able to generate data collection, dissemination, and management tools to facilitate tasks with respect to using and sharing scientific data. MetaShare, which is initially targeting the research community at the University of Texas at El Paso, is being developed on a Web platform, using Semantic Web technologies. This paper presents a roadmap for the development of MetaShare, justifying the functionality and implementation decisions. In addition, the paper presents an argument concerning the return on investment for researchers and the planned evaluation for the system.