Multi-Agent Geo-Simulation (MAGS) is a modelling paradigm which has attracted a growing interest from researchers and practitioners for the study of various phenomena in a variety of domains such as traffic simulation, urban dynamics, environment monitoring, as well as changes of land use and cover, to name a few. These phenomena usually involve a large number of simulated actors (implemented as software agents) evolving in, and interacting with, an explicit spatial environment representation commonly called Virtual Geographic Environment (VGE). Since a geographic environment may be complex and large-scale, the creation of a VGE is difficult and needs large quantities of geometrical data originating from the environment characteristics (terrain elevation, location of objects and agents, etc.) as well as semantic information that qualifies space (building, road, park, etc.). CurrentMAGS approaches usually consider the environment as a monolithic structure, which considerably reduces the capacity to handle largescale, real world geographic environments as well as agent's spatial reasoning capabilities. Moreover, the problem of path planning in MAGS involving complex and large-scale VGEs has to be solved in real time, often under constraints of limited memory and CPU resources. Available path planners provide agents with obstacle-free paths between two located positions in the VGE, but take into account neither the environment's characteristics (topologic and semantic) nor the agents' types and capabilities. In addition, agents evolving in a VGE lack for mechanisms and tools that allow them to acquire knowledge about their virtual environment in order to make informed decisions. In this thesis, we propose a novel approach to automatically generate a semantically-enriched and geometrically-precise representation of the geographic environment that we call Informed Virtual Geographic Environment (IVGE). Our IVGE model efficiently organizes the geographic features, precisely captures the real world complexity, and reliably represents large-scale geographic environments. We also provide a new hierarchical path planning algorithm which leverages the enriched description of the IVGE in order to support agents' reasoning capabilities while optimising computation costs and taking into account both the virtual environment's characteristics and the agents' types and capabilities. Finally, we propos an environment knowledge management approach to support the agents' spatial decision making process while interacting with the IVGE.

Disk contains: HAZARDS version 5.5, designed to predict asteroid or comet impacts with the Earth.

[8x10 Full Color Version]Are we alone in the Universe? It is an age old question. Most people want to "believe" or suggest that "we cant be the only ones..." . Well until now we only had our belief system to tell us we are not alone. The Populated Universe adds a science perspective to this, the question for all time. It takes the reader through the very reasons, based on science, that life is likely teeming in the Universe. Written for the lay person, The Populated Universe begins with a look at our own world. We have had 5 major extinction level events here. Find the surprising reason why that fact may be an oddity in the Universe. Yet still here we are. Did you know that 250 million years ago animals that would become mammals (precursors to US) were dominant on the planet until they and 98% of all life was wiped out? Dinosaurs then ruled for millions of years until another extinction event 65 million years ago when mammals rose once again to fill the empty niches. is the Universe telling us something about mammals perhaps being a universal life form here and elsewhere? By looking at the chronology of life here on Earth we can make some educated guesses as to what path life elsewhere in the Universe may have taken. Did you know some planets present only one face to their star like the Moon does to us on Earth? What does that mean for life evolving there? The Populated Universe will take you through these different possibilities. Finally it will take the reader to the search here at home for extraterrestrial life that may have reached us here on Earth. All based on the possibilities of science. Interstellar travel is more promising than one may think and NASA is already funding labs to look at this very real prospect. In a sense after reading The Populated Universe, the grand Universe may just feel a little smaller...

Resource Description Framework (RDF) is a graph-based data model used to publish data as a Web of Linked Data. RDF is an emergent foundation for large-scale data integration, the problem of providing a unified view over multiple data sources. An Entity Name System (ENS) is a thesaurus for entities, and is a crucial component in a data integration architecture. Populating a Linked Data ENS is equivalent to solving an Artificial Intelligence problem called instance matching, which concerns identifying pairs of entities referring to the same underlying entity. This publication presents an instance matcher with 4 properties, namely automation, heterogeneity, scalability and domain independence. Automation is addressed by employing inexpensive but well-performing heuristics to automatically generate a training set, which is employed by other machine learning algorithms in the pipeline. Data-driven alignment algorithms are adapted to deal with structural heterogeneity in RDF graphs. Domain independence is established by actively avoiding prior assumptions about input domains, and through evaluations on 10 RDF test cases. The full system is scaled by implementing it on cloud infrastructure using MapReduce algorithms. Resource Description Framework (RDF) is a graph-based data model used to publish data as a Web of Linked Data. RDF is an emergent foundation for large-scale data integration, the problem of providing a unified view over multiple data sources. An Entity Name System (ENS) is a thesaurus for entities, and is a crucial component in a data integration architecture. Populating a Linked Data ENS is equivalent to solving an Artificial Intelligence problem called instance matching, which concerns identifying pairs of entities referring to the same underlying entity. This publication presents an instance matcher with 4 properties, namely automation, heterogeneity, scalability and domain independence. Automation is addressed by employing inexpensive but well-performing heuristics to automatically generate a training set, which is employed by other machine learning algorithms in the pipeline. Data-driven alignment algorithms are adapted to deal with structural heterogeneity in RDF graphs. Domain independence is established by actively avoiding prior assumptions about input domains, and through evaluations on 10 RDF test cases. The full system is scaled by implementing it on cloud infrastructure using MapReduce algorithms.

This edited volume opening the new series Revisiting Communism: Collectivist Economic Thought in Historical Perspective focuses on the concepts of ownership, the cornerstone of political economy in Soviet-type societies. The authors’ main objective is to contribute to the still unwritten chapter on collectivism in the history books of modern economic thought. They trace the lengthy evolution of economic ideas of property reform under communism leading from the doctrine of blanket nationalization to projects of moderate privatization in eight countries of Eastern Europe and China. The comparative analysis sheds light upon the tireless attempts of reform-minded economists in communist countries to populate the no man’s land of “social property” with quasi-private economic actors such as bodies of workers’ self-management and managers of state-owned companies. For a long time, these were expected to crowd out the communist nomenklatura from its actual ownership position without challenging the primacy of collective property rights. The fact that even the most radical reformers came to the conclusion that such surrogate owners would not be able to break the power of the ruling elite only on the eve of the 1989 revolutions demonstrates the immense strength of collectivist ideas. The authors coin the term “trap of collectivism” to warn those demanding nationalization or other forms of non-private ownership today: it is rather easy, even with the best intentions, to walk into this trap but it may take long decades to break out from it.

Roughly every third Wikipedia article contains an infobox - a table that displays important facts about the subject in attribute-value form. The schema of an infobox, i.e., the attributes that can be expressed for a concept, is defined by an infobox template. Often, authors do not specify all template attributes, resulting in incomplete infoboxes. With iPopulator, we introduce a system that automatically populates infoboxes of Wikipedia articles by extracting attribute values from the article's text. In contrast to prior work, iPopulator detects and exploits the structure of attribute values for independently extracting value parts. We have tested iPopulator on the entire set of infobox templates and provide a detailed analysis of its effectiveness. For instance, we achieve an average extraction precision of 91% for 1,727 distinct infobox template attributes.