Elaboration on Two Points Raised in ``Classifier Technology and the Illusion of Progress''

Comment: Elaboration on Two Points Raised in ``Classifier Technology and the Illusion of Progress'' [math.ST/0606441]Comment: Published at http://dx.doi.org/10.1214/088342306000000033 in the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org

Error Analysis and Correction for Weighted A*'s Suboptimality (Extended Version)

Weighted A* (wA*) is a widely used algorithm for rapidly, but suboptimally, solving planning and search problems. The cost of the solution it produces is guaranteed to be at most W times the optimal solution cost, where W is the weight wA* uses in prioritizing open nodes. W is therefore a suboptimality bound for the solution produced by wA*. There is broad consensus that this bound is not very accurate, that the actual suboptimality of wA*'s solution is often much less than W times optimal. However, there is very little published evidence supporting that view, and no existing explanation of why W is a poor bound. This paper fills in these gaps in the literature. We begin with a large-scale experiment demonstrating that, across a wide variety of domains and heuristics for those domains, W is indeed very often far from the true suboptimality of wA*'s solution. We then analytically identify the potential sources of error. Finally, we present a practical method for correcting for two of these sources of error and experimentally show that the correction frequently eliminates much of the error.Comment: Published as a short paper in the 12th Annual Symposium on Combinatorial Search, SoCS 201

Front-to-End Bidirectional Heuristic Search with Near-Optimal Node Expansions

It is well-known that any admissible unidirectional heuristic search algorithm must expand all states whose $f$-value is smaller than the optimal solution cost when using a consistent heuristic. Such states are called "surely expanded" (s.e.). A recent study characterized s.e. pairs of states for bidirectional search with consistent heuristics: if a pair of states is s.e. then at least one of the two states must be expanded. This paper derives a lower bound, VC, on the minimum number of expansions required to cover all s.e. pairs, and present a new admissible front-to-end bidirectional heuristic search algorithm, Near-Optimal Bidirectional Search (NBS), that is guaranteed to do no more than 2VC expansions. We further prove that no admissible front-to-end algorithm has a worst case better than 2VC. Experimental results show that NBS competes with or outperforms existing bidirectional search algorithms, and often outperforms A* as well.Comment: Accepted to IJCAI 2017. Camera ready version with new timing result

When Does Changing Representation Improve Problem-Solving Performance?

The aim of changing representation is the improvement of problem-solving efficiency. For the most widely studied family of methods of change of representation it is shown that the value of a single parameter, called the expulsion factor, is critical in determining (1) whether the change of representation will improve or degrade problem-solving efficiency and (2) whether the solutions produced using the change of representation will or will not be exponentially longer than the shortest solution. A method of computing the expansion factor for a given change of representation is sketched in general and described in detail for homomorphic changes of representation. The results are illustrated with homomorphic decompositions of the Towers of Hanoi problem

Heat and salinity budgets at the Stratus mooring in the southeast Pacific

Author Posting. © American Geophysical Union, 2014. This article is posted here by permission of American Geophysical Union for personal use, not for redistribution. The definitive version was published in Journal of Geophysical Research: Oceans 119 (2014): 8162–8176, doi:10.1002/2014JC010256.The surface layer of the southeast Pacific Ocean (SEP) requires an input of cold, fresh water to balance heat gain, and evaporation from air-sea fluxes. Models typically fail to reproduce the cool sea surface temperatures (SST) of the SEP, limiting our ability to understand the variability of this climatically important region. We estimate the annual heat budget of the SEP for the period 2004–2009, using data from the upper 250 m of the Stratus mooring, located at 85°W 20°S, and from Argo floats. The surface buoy measures meteorological conditions and air-sea fluxes; the mooring line is heavily instrumented, measuring temperature, salinity, and velocity at more than 15 depth levels. We use a new method for estimating the advective component of the heat budget that combines Argo profiles and mooring velocity data, allowing us to calculate monthly profiles of heat advection. Averaged over the 6 year study period, we estimate a cooling advective heat flux of −41 ± 29 W m−2, accomplished by a combination of the mean gyre circulation, Ekman transport, and eddies. This compensates for warming fluxes of 32 ± 4 W m−2 due to air-sea fluxes and 7 ± 9 W m−2 due to vertical mixing and Ekman pumping. A salinity budget exhibits a similar balance, with advection of freshwater (−60 psu m) replenishing the freshwater lost through evaporation (47 psu m) and Ekman pumping (14 psu m).This work was supported by NOAA's Climate Program Office and by NSF grant OCE-0745508.2015-05-2