be a significant bottleneck to the broader uptake of constraint technology in the real world. In this paper we are concerned with automating the formulation of con-straint satisfaction problems from examples of solutions and non-solutions. We combine techniques from the fields of machine learning

, in various cultural and institutional manifestations, as the industrial society characterized the social structure of both capitalism and statism for most of the twentieth centur y. Social structures are organized around relationships of production/consumption, power, and experience, whose spatio

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Abstract Supporting database schema evolution repre-sents a long-standing challenge of practical and theoretical importance for modern information systems. In this paper, we describe techniques and systems for automating the critical tasks of migrating the database and rewriting the legacy

sorts of goods and evils (see, for example, chapter 7 of Hare [1963] and The issues that arise in automated decision making recall some of the traditional philosophical questions but locate the debate in a new setting by focusing on the need for efficient reasoning in the very complex decisionmaking

these protocols build and maintain substantial network state to cope with the dynamics of low-power wireless links. Recent protocols depart from this design by leveraging synchronous transmissions (ST), whereby multiple nodes simultaneously transmit towards the same receiver, as opposed to pairwise link

In order to extend the potential of application of the syntactic approach to pattern recognition, the efficient use of models capable of describing context-sensitive structural relationships is needed. Moreover, the ability to learn such models from examples is interesting to automate as much

I first thank my thesis advisors Thiemo Voigt and Adam Dunkels for being my excellent advisors, and also for being the great colleagues you still are. Thiemo and Adam have complemented each other superbly, giving me feedback, perspectives, insights, and having made every single new venture truly fun. My deepest thanks also go to my professor Per Gunningberg, Uppsala University. Per has in a seemingly easy way spotted erroneous assumptions, has valued my research contributions, and has connected my own work with another broader world of research. I want to thank all colleagues in the Networked Embedded Systems group:

Public reporting burden for the collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information. Send comments regarding this burden estimate or any other aspect of this collection of information,

In automated causal discovery, the constraint-based approach seeks to learn an (equivalence) class of causal structures (with possibly latent variables and/or selection variables) that are compatible (according to some assumptions, usually the causal Markov and faithfulness assumptions