TY - JOUR

T1 - Revisiting the approximation bound for stochastic submodular cover

AU - Hellerstein, Lisa

AU - Kletenik, Devorah

N1 - Publisher Copyright:
© 2018 AI Access Foundation. All rights reserved.

PY - 2018/10/1

Y1 - 2018/10/1

N2 - Deshpande et al. presented a k(lnR+1) approximation bound for Stochastic Submodular Cover, where k is the state set size, R is the maximum utility of a single item, and the utility function is integer-valued. This bound is similar to the (ln Q= + 1) bound given by Golovin and Krause, whose analysis was recently found to have an error. Here Q ≥ R is the goal utility and Q is the minimum gap between Q and any attainable utility Q0 < Q. We revisit the proof of the k(lnR+1) bound of Deshpande et al., fill in the details of the proof of a key lemma, and prove two bounds for real-valued utility functions: k(lnR1 +1) and (lnRE +1). Here R1 equals the maximum ratio between the largest increase in utility attainable from a single item, and the smallest non-zero increase attainable from that same item (in the same state). The quantity RE equals the maximum ratio between the largest expected increase in utility from a single item, and the smallest non-zero expected increase in utility from that same item. Our bounds apply only to the stochastic setting with independent states.

AB - Deshpande et al. presented a k(lnR+1) approximation bound for Stochastic Submodular Cover, where k is the state set size, R is the maximum utility of a single item, and the utility function is integer-valued. This bound is similar to the (ln Q= + 1) bound given by Golovin and Krause, whose analysis was recently found to have an error. Here Q ≥ R is the goal utility and Q is the minimum gap between Q and any attainable utility Q0 < Q. We revisit the proof of the k(lnR+1) bound of Deshpande et al., fill in the details of the proof of a key lemma, and prove two bounds for real-valued utility functions: k(lnR1 +1) and (lnRE +1). Here R1 equals the maximum ratio between the largest increase in utility attainable from a single item, and the smallest non-zero increase attainable from that same item (in the same state). The quantity RE equals the maximum ratio between the largest expected increase in utility from a single item, and the smallest non-zero expected increase in utility from that same item. Our bounds apply only to the stochastic setting with independent states.

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U2 - 10.1613/jair.1.11242

DO - 10.1613/jair.1.11242

M3 - Article

AN - SCOPUS:85055880835

SN - 1076-9757

VL - 63

SP - 265

EP - 279

JO - Journal of Artificial Intelligence Research

JF - Journal of Artificial Intelligence Research

ER -