Representing and Reasoning with Preferences

In the reverse direction, artificial intelligence brings a fresh perspective to some of the questions addressed by social choice. From a computational perspective, may not be feasible. The agent wants a cheap, we can look at how computationally we low-mileage Ferrari, but no such car exists. As we shall see later in may therefore look for the most preferred outcome this article, computational intractability may among those that are feasible. With multiple actually be advantageous in this setting. For agents, their goals may be conflicting. We may therefore look for the outcome an election is possible in theory, but computationally that is most preferred by the agents. Preferences difficult to perform in practice. From a are thus useful in many areas of artificial representational perspective, we can look at intelligence including planning, sche dhow we represent preferences, especially when uling, multiagent systems, combinatorial auctions, the number of outcomes is combinatorially and game playing.