We consider how to forget a set of atoms in a logic program. Intuitively, when a set of atoms is forgotten from a logic program, all atoms in the set should be eliminated from this program in some way, and other atoms related to them in the program might also be affected. We define notions of strong and weak forgettings in logic programs to capture such intuition and reveal their close connections to the notion of forgetting in classical propositional theories. Based on these notions, we then propose a framework for conflict solving in logic programs, which is general enough to represent many important conflict solving problems. We also study some essential semantic and computational properties in relation to strong and weak forgettings and conflict solving in our framework.

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Worker selection is a significant and challenging issue in crowdsourcing systems. Such selection is usually based on an assessment of the reputation of the individual workers participating in such systems. However, assessing the credibility and adequacy of such calculated reputation is a real challenge. In this paper, we propose a reputation management model which leverages the values of the tasks completed, the credibility of the evaluators of the results of the tasks and time of evaluation of the results of these tasks in order to calculate more dependable quality metrics for workers and evaluators. The model has been implemented and experimentally validated. Index Terms-Reputation, Degree of Fairness, Crowdsourcing

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