The ability to make decisions and to assess potential courses of action is a corner-stone of many AI applications, and usually this requires explicit information about the decision-maker's preferences. In many applications, preference elic-itation is a serious bottleneck. The user either does not have the time, the knowledge, or the expert support required to specify complex multi-attribute utility functions. In such cases, a method that is based on intuitive, yet expressive, preference statements is required. In this paper we suggest the use of TCP-nets, an enhancement of CP-nets, as a tool for representing, and reasoning about qualitative preference statements. We present and motivate this framework, define its semantics, and show how it can be used to perform constrained optimization.
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