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There has been considerable research interest into the sol-ubility phase transition, and its eeect on search cost for backtracking algorithms. In this paper we show that a similar easy-hard-easy pattern occurs for local search, with search cost peaking at the phase transition. This is despite problems beyond the phase transition having fewer solutions ,(More)
Vve address the problem of scheduling observations for a collection of earth observing satellites. This scheduling task is a difficult optimization problem, potentially involving many satellites, hundreds of requests, constraints on when and how to service each request, and resources such as instruments, recording devices, transmitters, and ground stations.(More)
Planning research in Artificial Intelligence (AI) has often focused on problems where there are cascading levels of action choice and complex interactions between actions. In contrast, Scheduling research has focused on much larger problems where there is little action choice, but the resulting ordering problem is hard. In this paper, we give an overview of(More)
We investigate an improvement to GSAT which associates a weight with each clause. We change the objective function so that GSAT moves to assignments maximizing the weight of satissed clauses, and each clause's weight is changed when GSAT moves to an assignment in which this clause is unsatissed. We present results showing that this version of GSAT has good(More)
Local search algorithms for combinatorial search problems frequently encounter a sequence of states in which it is impossible to improve the value of the objective function; moves through these regions, called plateau moves, dominate the time spent in local search. We analyze and characterize plateaus for three diierent classes of randomly generated Boolean(More)
Many complex real-world decision problems, such as planning, contain an underlying constraint reasoning problem. The feasibility of a solution candidate then depends on the consistency of the associated constraint problem instance. The underlying constraint problems are invariably dynamic, as higher level decisions result in variables , values, and(More)
Ensembles of random NP-hard problems often exhibit a phase transition in solv-ability with a corresponding peak in search cost 3]. Problem instances from such phase transitions are now used routinely to benchmark algorithms. To study such phase transitions, parameters have been derived either from asymptotic scaling results or from the constrainedness 6].(More)