Suggestion Strategies for Constraint - BasedMatchmaker Agents ? ? Eugene

In this paper we describe a paradigm for content-focused matchmaking, based on a recently proposed model for constraint acquisition and satisfaction. Matchmaking agents are conceived as constraint-based solvers that interact with other, possibly human, agents (Customers). The Matchmaker provides potential solutions (\suggestions") based on partial knowledge, while gaining further information about the problem itself from the other agent through the latter's evaluation of these suggestions. The dialog between Matchmaker and Customer results in iterative improvement of solution quality, as demonstrated in simple simulations. We also show empirically that this paradigm supports \sug-gestion strategies" for nding acceptable solutions more eeciently or for increasing the amount of information obtained from the Customer. This work also indicates some ways in which the tradeoo between these two metrics for evaluating performance can be handled.