AAS 390 Chapter Notes - Chapter 1: Université Libre De Bruxelles, Answer Set Programming, Ibanez

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Frank hutter1, manuel l opez-ib a nez2, chris fawcett3, marius lindauer4, Holger h. hoos3, kevin leyton-brown3, and thomas st utzle2. 1 freiburg university, department of computer science fh@informatik. uni-freiburg. de. 3 university of british columbia, department of computer science. 4 potsdam university, institute of computer science manju@cs. uni-potsdam. de. Modern solvers for hard computational problems often expose parameters that permit customization for high performance on speci c instance types. Since it is tedious and time-consuming to manually optimize such highly parameterized algorithms, recent work in the ai literature has developed automated approaches for this algorithm con g- uration problem [3,16,11,1,13,10]. This line of work has already led to improvements in the state of the art for solving a wide range of problems, including propositional satis ability (sat) [7,12,20,2], mixed integer programming (mip) [9], timetabling [4], Ai planning [21,6], answer set programming (asp) [18], bi-objective tsp [14], and bi-objective owshop scheduling problems [5].

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