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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)
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)
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)
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)
The Action Notation Modeling Language (ANML) is being developed to provide a high-level, convenient , and succinct alternative to existing planning languages such as PDDL, the EUROPA modeling language (NDDL), and the ASPEN modeling language (AML). ANML is based on strong notions of action and state (like PDDL and AML), uses a variable/value model (like NDDL(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)