9,121 research outputs found
Additive Pattern Database Heuristics
We explore a method for computing admissible heuristic evaluation functions
for search problems. It utilizes pattern databases, which are precomputed
tables of the exact cost of solving various subproblems of an existing problem.
Unlike standard pattern database heuristics, however, we partition our problems
into disjoint subproblems, so that the costs of solving the different
subproblems can be added together without overestimating the cost of solving
the original problem. Previously, we showed how to statically partition the
sliding-tile puzzles into disjoint groups of tiles to compute an admissible
heuristic, using the same partition for each state and problem instance. Here
we extend the method and show that it applies to other domains as well. We also
present another method for additive heuristics which we call dynamically
partitioned pattern databases. Here we partition the problem into disjoint
subproblems for each state of the search dynamically. We discuss the pros and
cons of each of these methods and apply both methods to three different problem
domains: the sliding-tile puzzles, the 4-peg Towers of Hanoi problem, and
finding an optimal vertex cover of a graph. We find that in some problem
domains, static partitioning is most effective, while in others dynamic
partitioning is a better choice. In each of these problem domains, either
statically partitioned or dynamically partitioned pattern database heuristics
are the best known heuristics for the problem
Hierarchy construction schemes within the Scale set framework
Segmentation algorithms based on an energy minimisation framework often
depend on a scale parameter which balances a fit to data and a regularising
term. Irregular pyramids are defined as a stack of graphs successively reduced.
Within this framework, the scale is often defined implicitly as the height in
the pyramid. However, each level of an irregular pyramid can not usually be
readily associated to the global optimum of an energy or a global criterion on
the base level graph. This last drawback is addressed by the scale set
framework designed by Guigues. The methods designed by this author allow to
build a hierarchy and to design cuts within this hierarchy which globally
minimise an energy. This paper studies the influence of the construction scheme
of the initial hierarchy on the resulting optimal cuts. We propose one
sequential and one parallel method with two variations within both. Our
sequential methods provide partitions near the global optima while parallel
methods require less execution times than the sequential method of Guigues even
on sequential machines
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
Strengthening Canonical Pattern Databases with Structural Symmetries
Symmetry-based state space pruning techniques have proved to greatly improve heuristic search based classical planners. Similarly, abstraction heuristics in general and pattern databases in particular are key ingredients of such planners. However, only little work has dealt with how the abstraction heuristics behave under symmetries. In this work, we investigate the symmetry properties of the popular canonical pattern databases heuristic. Exploiting structural symmetries, we strengthen the canonical pattern databases by adding symmetric pattern databases, making the resulting heuristic invariant under structural symmetry, thus making it especially attractive for symmetry-based pruning search methods. Further, we prove that this heuristic is at least as informative as using symmetric lookups over the original heuristic. An experimental evaluation confirms these theoretical results
Faster optimal and suboptimal hierarchical search
In problem domains for which an informed admissible heuristic function is not available, one attractive approach is hierarchical search. Hierarchical search uses search in an abstracted version of the problem to dynamically generate heuristic values. This thesis makes three contributions to hierarchical search. First, we propose a simple modification to the state-of-the-art algorithm Switchback that reduces the number of expansions (and hence the running time) by approximately half, while maintaining its guarantee of optimality. Second, we propose a new algorithm for suboptimal hierarchical search, called Switch. Empirical results suggest that Switch yields faster search than straightforward modifications of Switchback, such as weighting the heuristic. Finally, we propose a modification to our optimal algorithm that uses multiple additive abstractions in order to improve performance of both optimal and suboptimal hierarchical search on some domains
Additive Pattern Databases for Decoupled Search
Abstraction heuristics are the state of the art in optimal classical planning as heuristic search. Despite their success for explicit-state search, though, abstraction heuristics are not available for decoupled state-space search, an orthogonal reduction technique that can lead to exponential savings by decomposing planning tasks. In this paper, we show how to compute pattern database (PDB) heuristics for decoupled states. The main challenge lies in how to additively employ multiple patterns, which is crucial for strong search guidance of the heuristics. We show that in the general case, for arbitrary collections of PDBs, computing the heuristic for a decoupled state is exponential in the number of leaf components of decoupled search. We derive several variants of decoupled PDB heuristics that allow to additively combine PDBs avoiding this blow-up and evaluate them empirically
Robust Minutiae Extractor: Integrating Deep Networks and Fingerprint Domain Knowledge
We propose a fully automatic minutiae extractor, called MinutiaeNet, based on
deep neural networks with compact feature representation for fast comparison of
minutiae sets. Specifically, first a network, called CoarseNet, estimates the
minutiae score map and minutiae orientation based on convolutional neural
network and fingerprint domain knowledge (enhanced image, orientation field,
and segmentation map). Subsequently, another network, called FineNet, refines
the candidate minutiae locations based on score map. We demonstrate the
effectiveness of using the fingerprint domain knowledge together with the deep
networks. Experimental results on both latent (NIST SD27) and plain (FVC 2004)
public domain fingerprint datasets provide comprehensive empirical support for
the merits of our method. Further, our method finds minutiae sets that are
better in terms of precision and recall in comparison with state-of-the-art on
these two datasets. Given the lack of annotated fingerprint datasets with
minutiae ground truth, the proposed approach to robust minutiae detection will
be useful to train network-based fingerprint matching algorithms as well as for
evaluating fingerprint individuality at scale. MinutiaeNet is implemented in
Tensorflow: https://github.com/luannd/MinutiaeNetComment: Accepted to International Conference on Biometrics (ICB 2018
Tiresias: Online Anomaly Detection for Hierarchical Operational Network Data
Operational network data, management data such as customer care call logs and
equipment system logs, is a very important source of information for network
operators to detect problems in their networks. Unfortunately, there is lack of
efficient tools to automatically track and detect anomalous events on
operational data, causing ISP operators to rely on manual inspection of this
data. While anomaly detection has been widely studied in the context of network
data, operational data presents several new challenges, including the
volatility and sparseness of data, and the need to perform fast detection
(complicating application of schemes that require offline processing or
large/stable data sets to converge).
To address these challenges, we propose Tiresias, an automated approach to
locating anomalous events on hierarchical operational data. Tiresias leverages
the hierarchical structure of operational data to identify high-impact
aggregates (e.g., locations in the network, failure modes) likely to be
associated with anomalous events. To accommodate different kinds of operational
network data, Tiresias consists of an online detection algorithm with low time
and space complexity, while preserving high detection accuracy. We present
results from two case studies using operational data collected at a large
commercial IP network operated by a Tier-1 ISP: customer care call logs and
set-top box crash logs. By comparing with a reference set verified by the ISP's
operational group, we validate that Tiresias can achieve >94% accuracy in
locating anomalies. Tiresias also discovered several previously unknown
anomalies in the ISP's customer care cases, demonstrating its effectiveness
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