Abstract not found

Abstract. Watching television tends to be a social activity. So, adaptive television needs to adapt to groups of users rather than to individual users. In this paper, we discuss different strategies for combining individual user models to adapt to groups, some of which are inspired by Social Choice Theory. In a first experiment, we explore how humans select a sequence of items for a group to watch, based on data about the individuals’ preferences. The results show that humans use some of the strategies such as the Average Strategy (a.k.a. Additive Utilitarian), the Average Without Misery Strategy and the Least Misery Strategy, and care about fairness and avoiding individual misery. In a second experiment, we investigate how satisfied people believe they would be with sequences chosen by different strategies, and how their satisfaction corresponds with that predicted by a number of satisfaction functions. The results show that subjects use normalization, deduct misery, and use the ratings in a non-linear way. One of the satisfaction functions produced reasonable, though not completely correct predictions. According to our subjects, the sequences produced by five strategies give satisfaction to all individuals in the group. The results also show that subjects put more emphasis than expected on showing the best rated item to each individual (at a cost of misery for another individual), and that the ratings of the first and last items in the sequence are especially important. In a final experiment, we explore the influence viewing an item can have on the ratings of other items. This is important for deciding the order in which to present items. The results show an effect of both mood and topical relatedness.

The paper is a theoretical study of a generalization of the lexicographic rule for combining ordering relations. We define the concept of priority operator: a priority operator maps a family of relations to a single relation which represents their lexicographic combination according to a certain priority on the family of relations. We present four kinds of results. We show

. In this paper we propose the deontic logic dus, that formalizes reasoning about prescriptive obligations in update semantics. In dus the definition of logical validity of obligations is not based on truth values but on action dynamics. You know the meaning of a normative sentence if you know the change it brings about in the ideality relation of anyone the news conveyed by the norm applies to. 1 The logic of norms One of the first topics discussed in the development of deontic logic was the question whether norms have truth values. For example, Von Wright (1981, 1998) was hesitant to call deontic formulas `logical truths,' because "it seems to be a matter of extralogical decision when we shall say that `there are' or `are not' such and such norms." Alchourr'on and Bulygin discussed the possibility of a logic of norms, which they distinguish from the logic of normative propositions. "One such issue is the problem of the possibility of a logic of norms. Some authors think that there ...

This article accompanies a tutorial talk given at the 40th ACM STOC conference. In it, we give a brief introduction to Fourier analysis of boolean functions and then discuss some applications: Arrow’s Theorem and other ideas from the theory of Social Choice; the Bonami-Beckner Inequality as an extension of Chernoff/Hoeffding bounds to higher-degree polynomials; and, hardness for approximation algorithms.

Abstract: We give a brief introduction to the basic notions of Fourier analysis on the

preference description language PDL . This language allows us to combine qualitative and quantitative, penalty based preferences in a flexible way. This makes it possible to express complex preferences which are needed in many realistic optimization settings. We show that several preference handling methods described in the literature are special cases of our approach. We also demonstrate that PDL expressions can be compiled to logic programs which can be used as tester programs in a generate-and-improve method for finding optimal answer sets.

Abstract. We propose a number of techniques for obtaining a global ranking from data that may be incomplete and imbalanced — characteristics that are almost universal to modern datasets coming from e-commerce and internet applications. We are primarily interested in cardinal data based on scores or ratings though our methods also give specific insights on ordinal data. From raw ranking data, we construct pairwise rankings, represented as edge flows on an appropriate graph. Our statistical ranking method exploits the graph Helmholtzian, which is the graph theoretic analogue of the Helmholtz operator or vector Laplacian, in much the same way the graph Laplacian is an analogue of the Laplace operator or scalar Laplacian. We shall study the graph Helmholtzian using combinatorial Hodge theory, which provides a way to unravel ranking information from edge flows. In particular, we show that every edge flow representing pairwise ranking can be resolved into two orthogonal components, a gradient flow that represents the l2-optimal global ranking and a divergence-free flow (cyclic) that measures the validity of the global ranking

. In this paper we study the logic of goals, which are formalized as desires with an utilitarian semantics. In our framework goals have a dual character, because they are constraints on utility functions as well as constructors of these utility functions. The non-monotonic reasoning related to the constructors reflects that goals are used as heuristic approximations of preferences in decision making and planning. Moreover, our framework is based on bipolar additive preferences, where bipolarity means that goals can either result in a gain of utility if achieved, or a loss of utility if not achieved. The framework is used to illustrate different types of context-dependence and conflicts of goals. 1 Introduction Decision theory has become widely accepted in the AI community as a useful framework for planning and decision making [9]. In the context of qualitative decision theory [15, 18, 2] recently several logics for goals and desires have been proposed [8, 7, 3, 15, 17, 16, 14, 12, 20...

This paper describes an efficient reduction of the learning problem of ranking to binary classification. The reduction is randomized and guarantees a pairwise misranking regret bounded by that of the binary classifier, improving on a recent result of Balcan et al. (2007) which ensures only twice that upper-bound. Moreover, our reduction applies to a broader class of ranking loss functions, admits a simple proof, and the expected time complexity of our algorithm in terms of number of calls to a classifier or preference function is also improved from Ω(n 2) to O(n log n). In addition, when the top k ranked elements only are required (k ≪ n), as in many applications in information extraction or search engine design, the time complexity of our algorithm can be further reduced to O(k log k+n). Our reduction and algorithm are thus practical for realistic applications where the number of points to rank exceeds several thousands. Much of our results also extend beyond the bipartite case previously studied. To further complement them, we also derive lower bounds for any deterministic reduction of ranking to binary classification, proving that randomization is necessary to achieve our reduction guarantees. 1