### Table Living room

### Table 3: Attributes of rooms in the Sisyphus room-assignment problem.

1995

Cited by 103

### Table 2: Runtimes for the Rooms problem

1999

"... In PAGE 22: ... From the rst formula that is found to be true a plan that reaches the goal can be extracted. In Table2 we give statistics on the evaluation of formulae with... In PAGE 24: ... With these blocks world problem instances the solution of the separate problems is very easy for the best classical planners. What makes these problems di cult is that the plans represent all possible executions, and the constraints on the plans are not as tight as in the benchmarks in Table2 or in the separate classical planning problems. However, when considering that the number of elements in the resulting plans is relatively high ( fteen or more for the bigger problems) and the notion of plans is much more complicated than in classical planning, the runtimes are not disappointing.... In PAGE 24: ... This is nicely on par with the fact that conditional planning is on the second level of the polynomial hierarchy, not on the third as the pre x 989 in the encodings might suggest. As shown in Table2 , the runtimes for plan generation can be much less than linear to the number of initial states. None of the early conditional planning algorithms is able to exhibit similar behavior; that is, they produce plans of exponential length and therefore consume exponential time even on simple problems like these.... In PAGE 25: ...theorem-prover implementation. For example for the problems in Table2 , na ve extensions of the Davis-Putnam procedure to QBF consider all of the 2n truth-value assignments to the universally quanti ed variables just to verify that a plan that has been found actually reaches the goal in all cases. Further developments in theorem-proving techniques for QBF and propositional satis ability are likely to improve these runtimes further.... ..."

Cited by 123

### Table 1. Available rooms and their properties.

2006

"... In PAGE 1: ... SCHEDULING PROBLEM We begin with an example of a conference scenario, and use it to illustrate the representation of resources and constraints. Suppose that we need to assign rooms to events at a small one-day conference, which starts at 11:00am and ends at 4:30pm, and that we can use three rooms: auditorium, classroom, and conference room ( Table1 ). These rooms host Scheduling with Uncertain Resources: Representation and Utility Function Ulas Bardak cyprus@cs.... ..."

Cited by 3