Efficient Intelligent Backtracking Using Linear Programming

This paper proposes a new method for searching two-valued (binary) game trees in games like chess or Go. Lambda-search uses null-moves together with different orders of threat-sequences (so-called lambda-trees), focusing the search on threats and threat-aversions, but still guaranteeing to find the mini-max value (provided that the game-rules allow passing or zugzwang is not a motive). Using negligible working memory in itself, the method seems able to offer a large relative reduction in search space over standard alpha-beta comparable to the relative reduction in search space of alpha-beta over minimax, among other things depending upon how non-uniform the search tree is. Lambda-search is compared to other resembling approaches, such as null-move pruning and proof-number search, and it is explained how the concept and context of different orders of lambda-trees may ease and inspire the implementation of abstract game-specific knowledge. This is illustrated on open-space Go block tactics, distinguishing between different orders of ladders, and offering some possible grounding work regarding an abstract formalization of the concept of relevancy-zones (zones outside of which added stones of any colour cannot change the status of the given problem).

PC-SSRDE: A paradigm crossover-based differential evolution algorithm with search space reduction

Search is considered to be an important functionality in a computational system. Search techniques are applied in file retrievals and indexing. Though there exists various search techniques, binary search is widely used in many applications due to its advantage over other search techniques namely linear and hash search. Binary search is easy to implement and is used to search for an element in a large search space. The worst case time complexity of binary search is O (log 2 n) where n is the number of elements (search space) in the array. However, in binary search, searching is performed on the entire search space. The complexity of binary search may be further reduced if the search space is reduced. This paper proposes an Ant Colony Optimization based Binary Search (ACOBS) algorithm to find an optimal search space for binary search. ACOBS algorithm categorizes the search space and the key element is searched only in a specific category where the key element can exist thereby reducing the search space. The time complexity of ACOBS algorithm is O (log 2 c) where c is the number of elements in the reduced search space and c < n. The proposal is best suited for real time applications where searching is performed on a large domain.KeywordsAnt colony optimizationBinary searchOptimal search space

A new framework for improving MPPT algorithms through search space reduction

In face recognition, searching and retrieval of relevant images from a large database form a major task. Recognition time is greatly related to the dimensionality of the original data and the number of training samples. This demands the selection of discriminant features that produce similar results as the entire set and a reduced search space. To address this issue, a Multi-Level Search Space Reduction framework for large scale face image database is proposed. The proposed approach identifies discriminating features and groups face images sharing similar properties using feature-weighted Fuzzy C-Means approach. A hierarchical tree model is then constructed inside every cluster based on the discriminating features which enables a branch based selection, thereby reducing the search space. The proposed framework is tested on three benchmark and two self-created databases. The experimental results show that the proposed method achieved an average accuracy of 93% and an average search time reduction of 66% compared to existing approaches for search space reduction of face recognition.

A new method for the solution of the packing problem is presented. The method introduces a mating concept to the problem. The mating allows one or more objects to be positioned relative to the other by applying mating conditions that are derived from the geometric features among them. Once mated, objects within a mating pair or a mating group are treated as one object and do not need to be positioned separately. In addition, overlap calculation among objects within the mating group is not necessary. The algorithm thus brings a significant reduction in search space and the overall time taken to converge.

Many constraint problems exhibit dominance relations which can be exploited for dramatic reductions in search space. Dominance relations are a generalization of symmetry and conditional symmetry. However, unlike symmetry breaking which is relatively well studied, dominance breaking techniques are not very well understood and are not commonly applied. In this paper, we present formal definitions of dominance breaking, and a generic method for identifying and exploiting dominance relations via dominance breaking constraints. We also give a generic proof of the correctness and compatibility of symmetry breaking constraints, conditional symmetry breaking constraints and dominance breaking constraints.

Wide area surveillance and reconnaissance in many homeland security applications generally entails an airborne platform providing a desired view of the area of interest. The surveillance needs of large critical infrastructures and key resources that may span for miles, demand a field of view that exceeds that provided by a single airborne sensor. Image registration of aerial imagery can increase the field of view while providing a panoramic view. This increases the field of view without any modification to the optics, focal plane array, and associated hardware. Some of the primary limitations of existing image registration methods are generally (a) image and sensor noise, (b) lack of accuracy in the related correspondence points derivation, and (c) registration algorithms' computational complexity. In this work, we focus on a multi-stage image registration technique utilizing hybrid similarity measures for partially overlapped aerial imagery, in the presence of sensor uncertainty and noise. We show that the presented algorithm provides a reduction in search space, reducing the computational cost. Additionally, here the system noise is modelled and injected into the images to determine the algorithmic performance as a function of signal to noise ratio (SNR). The presented technique is also evaluated in a scenario where a hyper-spectral or multispectral system may have an image in a noisy part of the spectrum but can be registered by using registration parameters from another part of the spectrum.

Intrusion detection systems alert the system administrators of intrusions but, in most cases, do not provide details about which system events are relevant to the intrusion and how the system events are related. We consider intrusions of file systems. Existing tools, like BackTracker, help the system administrator backtrack from the detection point, which is a file with suspicious contents, to possible entry points of the intrusion by providing a graph containing dependency information between the various files and processes that could be related to the detection point. We improve such backtracking techniques by logging certain additional parameters of the file system during normal operations (real-time) and examining the logged informationduring the analysis phase. In addition, we use data flow analysis within the processes related to the intrusion to prune unwanted paths from the dependency graph. This results in significant reduction in search space, search time, and false positives. We also analyze the effort required in terms of storage space and search time.

This work presents a two-phase image registration technique utilizing a hybrid feature-based and an area-based similarity measure of partially overlapped aerial imagery in presence of affine translation and rotation transformations. The resulting selectively guided execution of similarity measures provides a reduction in search space, reducing the computational cost of the proposed algorithm. This multi-stage approach enhances the capability to perform image registration of low resolution imagery where scenes may have many structures but lack well defined structures for conventional feature extraction or lack to have enough variations in the intensity values to diminish statistical dependencies. The inherent statistical attributes of area-based methods are exploited through the sequential use of complex correlation and mutual information on physics-based features.

A bug detection tool is an important tool in software engineering development. Many research papers have proposed techniques for detecting software bug, but there are certain semantic bugs that are not easy to detect. In our views, a bug can occur from incorrect logics that when a program is executed with a particular input, the program will behave in unexpected ways. In this paper, we propose a method and tool for software bugs detection by finding such input that causes an unexpected output guided by the fitness function. The method uses a Hierarchical Similarity Measurement Model (HSM) to help create the fitness function to examine a program behavior. Its tool uses Particle Swarm Optimization (PSO) with Search Space Reduction (SSR) to manipulate input by contracting and eliminating unfavorable areas of input search space. The programs under experiment were selected from four different domains such as financial, decision support system, algorithms and machine learning. The experimental result shows a significant percentage of success rate up to 93% in bug detection, compared to an estimated success rate of 28% without SSR.

This paper addresses the problem of optimizing multiple distributed stream queries that are executing simultaneously in distributed data stream systems. We argue that the static query optimization approach of "plan, then deployment" is inadequate for handling distributed queries involving multiple streams and node dynamics faced in distributed data stream systems and applications. Thus, the selection of an optimal execution plan in such dynamic and networked computing systems must consider operator ordering, reuse, network placement, and search space reduction. We propose to use hierarchical network partitions to exploit various opportunities for operator-level reuse while utilizing network characteristics to maintain a manageable search space during query planning and deployment. We develop top-down, bottom-up, and hybrid algorithms for exploiting operator-level reuse through hierarchical network partitions. Formal analysis is presented to establish the bounds on the search space and suboptimality of our algorithms. We have implemented our algorithms in the IFLOW system, an adaptive distributed stream management system. Through simulations and experiments using a prototype deployed on Emulab, we demonstrate the effectiveness of our framework and our algorithms.

An important factor of performance in test generation is the number of decisions in the search space. The more decisions are made in order to find a test the larger is the search space. This paper introduces an algorithm for the reduction of the number of decisions. Unlike classical test generation methods which perform their search on a netlist of gates, this algorithm works at a higher level. The gates of the circuit to test are partitioned into clusters. The search performed at the cluster level becomes a progressive translation of a set of value assignments to another set of value assignments that steps over cluster boundaries. To be able to step over cluster boundaries sensitization and propagation conditions are used. Experimental data demonstrate the effectiveness of the algorithm in reducing the number of decisions.

Correlating image semantics with its low level features is a challenging task. Although, humans are adept in distinguishing object categories, both in visual as well as in semantic space, but to accomplish this computationally is yet to be fully explored. The learning based techniques do minimize the semantic gap, but unlimited possible categorization of objects in real world is a major challenge to these techniques. This work analyzes and utilizes the strength of a semantically categorized image database to assign semantics to query images. Semantics based categorization of images would result in image hierarchy. The algorithms proposed in this work exploit visual image descriptors and similarity measures in the context of a semantically categorized image database. A novel 'Branch Selection Algorithm' is developed for a highly categorized and dense image database, which drastically reduces the search space. The search space so obtained is further reduced by applying any one of the four proposed 'Pruning Algorithms'. Pruning algorithms maintain accuracy while reducing the search space. These algorithms use an adaptive combination of multiple visual features of an image database to find semantics of query images. Branch Selection Algorithm tested on a subset of 'ImageNet' database reduces search space by 75%. The best pruning algorithm further reduces this search space by 26% while maintaining 95% accuracy.

To identify an individual using a biometric system, the input biometric data has to be typically compared against that of each and every identity in the existing database during the matching stage. The response time of the system increases with the increase in number of individuals (i.e., database size), which is not acceptable in real time monitoring or when working on large scale data. This thesis addresses the problem of reducing the number of database candidates to be considered during matching in the context of iris and ear recognition. In the case of iris, an indexing mechanism based on Burrows Wheeler Transform (BWT) is proposed. Experiments on the CASIA version 3 iris database show a significant reduction in both search time and search space, suggesting the potential of this scheme for indexing iris databases. The ear classification scheme proposed in the thesis is based on parameterizing the shape of the ear and assigning it to one of four classes: round, rectangle, oval and triangle. Experiments on the MAGNA database suggest the potential of this scheme for classifying ear databases.