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Trust plays a key role in social interactions. Explicitly modeling trust is therefore an important aspect of social network analysis in settings such as reputation management systems, recommendation systems, and viral marketing. Within the social sciences, trust is known to depend on network structure, context, individual actors’ attributes, and group memberships and affiliations. Furthermore, trust is often measured quantitatively, according to degrees of trust, rather than as a binary indicator. In this paper, we propose trust modeling as a rich challenge for statistical relational learning (SRL). Additionally, we show that probabilistic soft logic (PSL) is particularly well-suited for this problem. PSL, like many SRL languages, provides an intuitive framework for capturing the relational aspects of trust modeling, while its soft truth values easily accommodate varying strengths of trust. We model various sociological theories of trust in PSL and experimentally compare the resulting PSL programs to existing trust prediction methods, demonstrating the ease of model development and showing that these interpretable first-order logic models produce results of competitive quality. ispartof: pages:1-8 ispartof: International Workshop on Statistical Relational AI pages:1-8 ispartof: International Workshop on Statistical Relational AI location:Catalina Island, California, USA date:18 Aug - 18 Aug 2012 status: published

We propose a partially informed depth-first search algorithm to cope with the Job Shop Scheduling Problem with makespan minimization. The algorithm is built from the well-known P. Brucker's branch and bound algorithm. We improved the heuristic estimation of Brucker's algorithm by means of constraint propagation rules and so devised a more informed heuristic which is proved to be monotonic. We conducted an experimental study across medium and large instances. The results show that the proposed algorithm reaches optimal solutions for medium instances taking less time than branch and bound and that for large instances it reaches much better lower and upper bounds when both algorithms are given the same amount of time, This research has been supported by the Spanish Ministry of Science and Education under research project MEC-FEDER TIN2007-67466-C02-01 and by the Principality of Asturias under grant FICYT-BP09105.

We propose an integration of a fragment or propositional dynamic logic with an epistemic logic so as to represent logically the interactions between action and knowledge that are fundamental to planning order incomplete knowledge and partial observability. This logic, called EDL, enables us representing incomplete knowledge, nondeterministic actions, observations, sensing actions and conditional plans; it also enables a logical expression of several frequently made assumptions about the nature of the problem such as determinism, full observability, unobservability, or pure knowledge gathering. Plan verification corresponds to testing the validity of a given formula of this logic. The allowed plans are conditional, and a key point of our framework is that a plan is meaningful if the branching conditions bear on the knowledge of the agent only, and not on the real world (to which tha agent may not have access); this leads us considering “plans that reason” which may contain branching conditions referring to implicit knowledge to be evaluated at execution time.