scholarly journals A Bilinear Programming Approach for Multiagent Planning

2009 ◽  
Vol 35 ◽  
pp. 235-274 ◽  
Author(s):  
M. Petrik ◽  
S. Zilberstein
Keyword(s):  
Reduction Method ◽  
Optimal Solution ◽  
Computation Time ◽  
Bilinear Programming ◽  
Programming Approach ◽  
Performance Bound ◽  
Wide Range ◽  
Multiagent Planning ◽  
Dimensionality Reduction Method ◽  
Bilinear Programs

Multiagent planning and coordination problems are common and known to be computationally hard. We show that a wide range of two-agent problems can be formulated as bilinear programs. We present a successive approximation algorithm that significantly outperforms the coverage set algorithm, which is the state-of-the-art method for this class of multiagent problems. Because the algorithm is formulated for bilinear programs, it is more general and simpler to implement. The new algorithm can be terminated at any time and-unlike the coverage set algorithm-it facilitates the derivation of a useful online performance bound. It is also much more efficient, on average reducing the computation time of the optimal solution by about four orders of magnitude. Finally, we introduce an automatic dimensionality reduction method that improves the effectiveness of the algorithm, extending its applicability to new domains and providing a new way to analyze a subclass of bilinear programs.

scholarly journals Supporting Operations Management Decisions with LP Parametric Analyses Using AIMMS

2020 ◽  
Vol 28 (2) ◽  
pp. 91-100
Author(s):  
Imre Dimény ◽  
Tamás Koltai
Keyword(s):  
Decision Making ◽  
Operations Management ◽  
Business Strategy ◽  
Optimal Solution ◽  
Computation Time ◽  
Valuable Insight ◽  
Business Analytics ◽  
Hard Time ◽  
Wide Range ◽  
Parametric Analyses

Organizations all over the world use Business Analytics (BA) to gain insight in order to drive business strategy and planning. With the increasing amount of available data larger models are created to support decision making, but managers also must deal with the uncertainty of the input parameters. In this perspective Linear Programming (LP) models have two valuable properties: the required computation time allows large models to be solved and further valuable insight can be gained about the problem using sensitivity analysis. There is a wide range of tools available to solve LP problems. Many of these tools use an implementation of the simplex method and provides an optimal solution related sensitivity information. The sensitivity information generated by such solvers are often used by managers in the decision making process. There are situations when managers may have a hard time taking decision based on the information provided by most of the commercially available LP solvers. If the optimal solution of the primal problem (dual degeneracy) or the dual problem (primal degeneracy) is not unique, the resulting sensitivity information can be misleading for managers. In other cases, the resulted ranges may be too tight for decision support, thus information about a wider range is required. In this paper parametric analysis information is recommended to complete the traditional LP results in order to increase the insight of operations managers when using LP models for operation improvement.

Improved forward kinematic analysis of a reptile-like four-legged walking robot using a novel dimensionality-reduction method

2016 ◽  
Vol 230 (15) ◽  
pp. 2745-2754 ◽  
Author(s):  
Liangwen Wang ◽  
Caidong Wang ◽  
Wenliao Du ◽  
Xinjie Wang ◽  
Guizhong Xie ◽  
...  
Keyword(s):  
Dimensionality Reduction ◽  
Kinematic Analysis ◽  
Reduction Method ◽  
Computation Time ◽  
Walking Robot ◽  
Constraint Condition ◽  
Joint Angles ◽  
High Order Equation ◽  
Forward Kinematic ◽  
Dimensionality Reduction Method

A new forward kinematic analysis is proposed to describe the motion of a reptile-like four-legged walking robot using a new dimensionality-reduction method. The three standing legs (assuming one leg is swinging) contain nine driven joints. Only six of these joints, however, are independently driven joints. The remaining joints are redundant driven joints. Finding the redundant driven joint angles has been a key problem in improved forward kinematic analysis of a reptile-like forward kinematic analysis. Solving the associated high-order equation, which is derived using the analytic method, is problematic and slow. In this paper, we use a new dimensionality reduction method to solve this problem. First, we deduced the formulas for the redundant driven joint angles. Then, one of the formulas is transformed to take into account the constraint condition. We then use iteration to find solutions for the remaining equations that satisfy the constraint condition. With the help of MATLAB, a solving system for the forward kinematic analysis of this robot is introduced. Our results show two improvements over the conventional method: shorter computation time and higher precision.

scholarly journals A model for spectroscopic food sample analysis using data sonification

2021 ◽  
Author(s):  
Hsein Kew
Keyword(s):  
Dimensionality Reduction ◽  
Classification Accuracy ◽  
Reduction Method ◽  
Real Life ◽  
Principal Component ◽  
Relevant Information ◽  
Analysis Model ◽  
Linear Discriminant ◽  
Audio Output ◽  
Dimensionality Reduction Method

AbstractIn this paper, we propose a method to generate an audio output based on spectroscopy data in order to discriminate two classes of data, based on the features of our spectral dataset. To do this, we first perform spectral pre-processing, and then extract features, followed by machine learning, for dimensionality reduction. The features are then mapped to the parameters of a sound synthesiser, as part of the audio processing, so as to generate audio samples in order to compute statistical results and identify important descriptors for the classification of the dataset. To optimise the process, we compare Amplitude Modulation (AM) and Frequency Modulation (FM) synthesis, as applied to two real-life datasets to evaluate the performance of sonification as a method for discriminating data. FM synthesis provides a higher subjective classification accuracy as compared with to AM synthesis. We then further compare the dimensionality reduction method of Principal Component Analysis (PCA) and Linear Discriminant Analysis in order to optimise our sonification algorithm. The results of classification accuracy using FM synthesis as the sound synthesiser and PCA as the dimensionality reduction method yields a mean classification accuracies of 93.81% and 88.57% for the coffee dataset and the fruit puree dataset respectively, and indicate that this spectroscopic analysis model is able to provide relevant information on the spectral data, and most importantly, is able to discriminate accurately between the two spectra and thus provides a complementary tool to supplement current methods.

scholarly journals Temporal concatenation for Markov decision processes

2021 ◽  
pp. 1-28
Author(s):  
Ruiyang Song ◽  
Kuang Xu
Keyword(s):  
Markov Decision Processes ◽  
Large Scale ◽  
Optimal Solution ◽  
Upper Bounds ◽  
Black Box ◽  
Decision Processes ◽  
Optimal Solutions ◽  
Wide Range ◽  
Markov Decision ◽  
Speed Up

We propose and analyze a temporal concatenation heuristic for solving large-scale finite-horizon Markov decision processes (MDP), which divides the MDP into smaller sub-problems along the time horizon and generates an overall solution by simply concatenating the optimal solutions from these sub-problems. As a “black box” architecture, temporal concatenation works with a wide range of existing MDP algorithms. Our main results characterize the regret of temporal concatenation compared to the optimal solution. We provide upper bounds for general MDP instances, as well as a family of MDP instances in which the upper bounds are shown to be tight. Together, our results demonstrate temporal concatenation's potential of substantial speed-up at the expense of some performance degradation.

scholarly journals Plant Leaf Recognition through Local Discriminative Tangent Space Alignment

2016 ◽  
Vol 2016 ◽  
pp. 1-5 ◽  
Author(s):  
Chuanlei Zhang ◽  
Shanwen Zhang ◽  
Weidong Fang
Keyword(s):  
Dimensionality Reduction ◽  
Manifold Learning ◽  
Tangent Space ◽  
Reduction Method ◽  
Plant Leaf ◽  
Local Patch ◽  
Dimensionality Reduction Method

Manifold learning based dimensionality reduction algorithms have been payed much attention in plant leaf recognition as the algorithms can select a subset of effective and efficient discriminative features in the leaf images. In this paper, a dimensionality reduction method based on local discriminative tangent space alignment (LDTSA) is introduced for plant leaf recognition based on leaf images. The proposed method can embrace part optimization and whole alignment and encapsulate the geometric and discriminative information into a local patch. The experiments on two plant leaf databases, ICL and Swedish plant leaf datasets, demonstrate the effectiveness and feasibility of the proposed method.

scholarly journals An Analysis of Heterogeneity in German Speaking Birdwatchers Reveals Three Distinct Clusters and Gender Differences

Birds ◽  
2021 ◽  
Vol 2 (3) ◽  
pp. 250-260
Author(s):  
Christoph Randler
Keyword(s):  
Cluster Analysis ◽  
Optimal Solution ◽  
Social Bonding ◽  
Cluster Solution ◽  
Novice Group ◽  
Wide Range ◽  
German Speaking ◽  
Group Members ◽  
Bird Group ◽  
And Gender

The purpose of this study was to segment birdwatchers into clusters. Members from a wide range of bird related organizations, from highly specialized birders as well as Facebook bird group members were studied to provide a diverse dataset (n = 2766; 50.5% men). Birding specialization was measured with a battery of questionnaires. Birding specialization encompassed the three constructs of skill/competence, behavior, personal and behavioral commitment. Additionally, involvement, measured by centrality to lifestyle, attraction, social bonding, and identity, was used. The NbClust analyses showed that a three-cluster solution was the optimal solution. Then, k-means cluster analysis was applied on three groups: casual/novice, intermediate, and specialist/advanced birdwatchers. More men than women were in the specialist/advanced group and more women than men in the casual/novice group. As a conclusion, this study confirms a three-cluster solution for segmenting German birdwatchers based on a large and diverse sample and a broad conceptualization of the construct birding specialization. These data can be used to address different target audiences (novices, advanced birders) with different programs, e.g., in nature conservation.

Towards Intelligent Road Traffic Management over Weighted Large Graphs Hybrid Meta-heuristic-Based Approach

2022 ◽  
Vol 24 (3) ◽  
pp. 0-0
Keyword(s):  
Traffic Management ◽  
Large Scale ◽  
Road Traffic ◽  
Optimization Technique ◽  
Shortest Paths ◽  
Hybrid Genetic Algorithm ◽  
Optimal Solution ◽  
Computation Time ◽  
Exact Algorithm ◽  
On The Road

This paper introduces a new approach of hybrid meta-heuristics based optimization technique for decreasing the computation time of the shortest paths algorithm. The problem of finding the shortest paths is a combinatorial optimization problem which has been well studied from various fields. The number of vehicles on the road has increased incredibly. Therefore, traffic management has become a major problem. We study the traffic network in large scale routing problems as a field of application. The meta-heuristic we propose introduces new hybrid genetic algorithm named IOGA. The problem consists of finding the k optimal paths that minimizes a metric such as distance, time, etc. Testing was performed using an exact algorithm and meta-heuristic algorithm on random generated network instances. Experimental analyses demonstrate the efficiency of our proposed approach in terms of runtime and quality of the result. Empirical results obtained show that the proposed algorithm outperforms some of the existing technique in term of the optimal solution in every generation.

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