TY - GEN

T1 - Using alternative suboptimality bounds in heuristic search

AU - Valenzano, Richard

AU - Arfaee, Shahab Jabbari

AU - Stern, Roni

AU - Thayer, Jordan

AU - Sturtevant, Nathan R.

PY - 2013/12/13

Y1 - 2013/12/13

N2 - Most bounded suboptimal algorithms in the search literature have been developed so as to be ε-admissible. This means that the solutions found by these algorithms are guaranteed to be no more than a factor of (1 + ε) greater than optimal. However, this is not the only possible form of suboptimality bounding. For example, another possible suboptimality guarantee is that of additive bounding, which requires that the cost of the solution found is no more than the cost of the optimal solution plus a constant γ. In this work, we consider the problem of developing algorithms so as to satisfy a given, and arbitrary, suboptimality requirement. To do so, we develop a theoretical framework which can be used to construct algorithms for a large class of possible suboptimality paradigms. We then use the framework to develop additively bounded algorithms, and show that in practice these new algorithms effectively trade-off additive solution suboptimality for runtime.

AB - Most bounded suboptimal algorithms in the search literature have been developed so as to be ε-admissible. This means that the solutions found by these algorithms are guaranteed to be no more than a factor of (1 + ε) greater than optimal. However, this is not the only possible form of suboptimality bounding. For example, another possible suboptimality guarantee is that of additive bounding, which requires that the cost of the solution found is no more than the cost of the optimal solution plus a constant γ. In this work, we consider the problem of developing algorithms so as to satisfy a given, and arbitrary, suboptimality requirement. To do so, we develop a theoretical framework which can be used to construct algorithms for a large class of possible suboptimality paradigms. We then use the framework to develop additively bounded algorithms, and show that in practice these new algorithms effectively trade-off additive solution suboptimality for runtime.

UR - http://www.scopus.com/inward/record.url?scp=84889826677&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:84889826677

SN - 9781577356097

T3 - ICAPS 2013 - Proceedings of the 23rd International Conference on Automated Planning and Scheduling

SP - 233

EP - 241

BT - ICAPS 2013 - Proceedings of the 23rd International Conference on Automated Planning and Scheduling

T2 - 23rd International Conference on Automated Planning and Scheduling, ICAPS 2013

Y2 - 10 June 2013 through 14 June 2013

ER -