The powerful spreadsheet metaphor extends to symbolic constraints. Cooperative problem solving. We can work with the computer to both formulate and solve our problems. This can be useful in tutorial or cooperative situations. It is particularly important when a complete solution is not possible, and we need to understand why, and how we might modify the constraints.
Constraint satisfaction is a natural medium for negotiation among network agents.
Distributed resources can cooperate to share knowledge and solve problems. Agents that know our own constraints can best represent us as electronic assistants. Of course, that is really only penultimately what we want to be able to say. In pursuit of our Holy Grail, we really want our constraint programming environment itself to evaluate a problem's characteristics and synthesize an optimal solution method for it. Acknowledgment This material is based on work supported by the National Science Foundation under Grant No. IRI-9504316.