In this task, you are given a part of an article. Your task is to generate headline (title) for this text. Preferred headlines are under fifteen words.

Input: Consider Input: This paper develops the idea of membership function assignment for OWL (Web Ontology Language) ontology elements in order to subsequently generate fuzzy rules from this ontology. The task of membership function assignment for OWL ontology elements had already been partially described, but this concerned the case , when several OWL ontologies of the same domain were available, and they were merged into a single ontology. The purpose of this paper is to present the way of membership function assignment for OWL ontology elements in the case, when there is the only one available ontology. Fuzzy rules, generated from the OWL ontology, are necessary for supplement of the SWES (Semantic Web Expert System) knowledge base. SWES is an expert system, which will be able to extract knowledge from OWL ontologies , found in the Web, and will serve as a universal expert for the user.

Output: Membership Function Assignment for Elements of Single OWL Ontology


Input: Consider Input: We consider the design of online learning algorithms in a general learning setting, with the goal of obtaining computationally efficient no-regret algorithms, when given access to an oracle for the offline optimization version of the problem. We present an algorithm that we call Generalized Follow-thePerturbed-Leader (Generalized FTPL) and provide conditions under which it achieves vanishing regret and is efficiently implementable with an oracle. Our second main contribution is the introduction of a new online adversarial auction-design framework for revenue maximization and an application of our oracle-efficient learning results to the adaptive optimization of auctions. Our learning algorithm is a generalization of the classic FTPL algorithm of Kalai and Vempala [27], playing at every iteration the historically best-performing action after adding some perturbation to the performance of each of its actions. The crux of our design is adding perturbations in a manner that leads to oracle-efficiency. We reduce this to designing a translation matrix, which translates a low dimensional vector with independent noise components into a high dimensional vector of perturbations on the learner’s action space. Our approach generalizes prior work on oracle-efficient online learning [11, 22, 27, 35], ranging from online combinatorial optimization, learning in simultaneous auctions, and contextual learning. Our auction-design framework considers an auctioneer learning an optimal auction rule in an online manner, every day observing an adversarially chosen vector of valuations. The auctioneer’s goal is to achieve revenue that competes with the revenue of the optimal auction in hindsight among those in some target class. We give oracle-efficient learning results for: (1) VCG auctions with bidder-specific reserves in single-parameter settings with matroid constraints, (2) envy-free item pricing in multi-item auctions with unlimited supply, and (3) s-level auctions of Morgenstern and Roughgarden [28] for singleitem settings. The last result implies good regret against the optimal overall auction when valuations are coming from a fast mixing Markov chain, that is independent across bidders. We also extend our results to the case when the learner observes side information on the bidders before running the auction (contextual learning). We present additional extensions to contextual learning and learning with approximate oracles, implemented by FPTAS or Maximal-in-Range algorithms. We provide further applications in online welfare maximization in multi-unit auctions and in no-regret learning in simultaneous item auctions, answering an open question from prior work. ∗Microsoft Research, New York, mdudik@microsoft.com †Computer Science Department, Carnegie Mellon University, nhaghtal@cs.cmu.edu ‡Microsoft Research, New York, haipeng@microsoft.com §Microsoft Research, New York, schapire@microsoft.com ¶Microsoft Research, New England, vasy@microsoft.com ‖Microsoft Research, New York, jenn@microsoft.com ar X iv :1 61 1. 01 68 8v 1 [ cs .L G ] 5 N ov 2 01 6

Output: Oracle-Efficient Learning and Auction Design


Input: Consider Input: We consider the question of the stability of evolutionary algorithms to gradual changes,<lb>or drift, in the target concept. We define an algorithm to be resistant to drift if, for<lb>some inverse polynomial drift rate in the target function, it converges to accuracy 1 − ǫ<lb>with polynomial resources, and then stays within that accuracy indefinitely, except with<lb>probability ǫ at any one time. We show that every evolution algorithm, in the sense of<lb>Valiant [19], can be converted using the Correlational Query technique of Feldman [9], into<lb>such a drift resistant algorithm. For certain evolutionary algorithms, such as for Boolean<lb>conjunctions, we give bounds on the rates of drift that they can resist. We develop some<lb>new evolution algorithms that are resistant to significant drift. In particular, we give an<lb>algorithm for evolving linear separators over the spherically symmetric distribution that is<lb>resistant to a drift rate of O(ǫ/n), and another algorithm over the more general product<lb>normal distributions that resists a smaller drift rate. The above translation result can be also interpreted as one on the robustness of the notion of<lb>evolvability itself under changes of definition. As a second result in that direction we show<lb>that every evolution algorithm can be converted to a quasi-monotonic one that can evolve<lb>from any starting point without the performance ever dipping significantly below that of<lb>the starting point. This permits the somewhat unnatural feature of arbitrary performance<lb>degradations to be removed from several known robustness translations.


Output: Evolution with Drifting Targets