Utility Functions for Ceteris Paribus Preferences

Computational Intelligence (Impact Factor: 1). 04/2004; 20(2):158 - 217. DOI: 10.1111/j.0824-7935.2004.00235.x
Source: DBLP

ABSTRACT Ceteris paribus (all-else equal) preference statements concisely represent preferences over outcomes or goals in a way natural to human thinking. Although deduction in a logic of such statements can compare the desirability of specific conditions or goals, many decision-making methods require numerical measures of degrees of desirability. To permit ceteris paribus specifications of preferences while providing quantitative comparisons, we present an algorithm that compiles a set of qualitative ceteris paribus preferences into an ordinal utility function. Our algorithm is complete for a finite universe of binary features. Constructing the utility function can, in the worst case, take time exponential in the number of features, but common independence conditions reduce the computational burden. We present heuristics using utility independence and constraint-based search to obtain efficient utility functions.

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    ABSTRACT: Specific preference statements may reverse general prefer- ence statements, thus constituting a change of attitude in par- ticular situations. We define a semantics of preference rever- sal by relaxing the popular ceteris-paribus principle. We char- acterize preference reversal as default reasoning and we link it to prioritized Pareto-optimization, which permits a natu- ral computation of preferred solutions. The resulting method simplifies elicitation, representation, and utilization of com- plex preference relations and may thus enable a more realistic preference handling in personalized decision support systems and in preference-based intelligent systems.
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    ABSTRACT: Existing preference reasoning systems have been successful in simple domains. Broader success requires more natural and more expressive preference representations. This thesis develops a representation of logical preferences that combines numerical tradeoff ratios between partial outcome descriptions with qualitative preference information. We argue our system is unique among preference reasoning systems; previous work has focused on qualitative or quantitative preferences, tradeoffs, exceptions and generalizations, or utility independence, but none have combined all of these expressions under a unified methodology. We present new techniques for representing and giving meaning to quantitative tradeoff statements between different outcomes. The tradeoffs we consider can be multi-attribute tradeoffs relating more than one attribute at a time, they can refer to discrete or continuous domains, be conditional or unconditional, and quantified or qualitative. We present related methods of representing judgments of attribute importance. We then build upon a methodology for representing arbitrary qualitative ceteris paribus preference, or preferences "other things being equal," as presented in [MD04]. (cont.) Tradeoff preferences in our representation are interpreted as constraints on the partial derivatives of the utility function. For example, a decision maker could state that "Color is five times as important as price, availability, and time," a sentiment one might express in the context of repainting a home, and this is interpreted as indicating that utility increases in the positive color direction five times faster than utility increases in the positive price direction. We show that these representations generalize both the economic notion of marginal rates of substitution and previous representations of preferences in AI. Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2007. Includes bibliographical references (p. 125-129).
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    ABSTRACT: An e-barter multi-agent system consists of a set of agents exchanging goods. In contrast to e-commerce systems, transactions do not necessarily involve the exchange of money. Agents are equipped with a utility function to simulate the preferences of the customers that they are representing. They are grouped into local markets, according to the localities of the corresponding customers. Once these markets are saturated (i.e. no more exchanges can be performed) new agents, representing those local markets, are generated and combined into new markets. By reiteratively applying this process we finally get a global market.Even though a formalism to define e-barter architectures has been already introduced, that framework had a strong drawback: Neither transaction nor shipping costs were considered. In this paper we extend that framework to deal with systems where fees have to be paid to the owner of the system. These fees depend on the goods involved in the corresponding exchanges. In addition, shipping costs have also to be paid. These modifications complicate the setting because the utility that customers receive after exchanging goods is not directly given by the original utility function. That is, the returned utility after an exchange is performed has to be computed as a combination of the former utility and the derived costs. In particular, some exchanges may be disallowed because those costs exceed the increase of utility returned by the new basket of goods.
    Proceedings of the 2003 ACM Symposium on Applied Computing (SAC), March 9-12, 2003, Melbourne, FL, USA; 01/2003

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