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Bayesian networks are a popular diagnosis method, whose structures are usually defined by human experts and parameters are learned from data. For the increasing complexity of modern systems, building their structures based on physical behaviors is becoming a difficult task. However, the improvement of data collection techniques motivates learning their structures from data, where greedy search is a typical iterative method. In each iteration, it generates multiple structure candidates by modifying one edge, evaluates these structures by scores based on data and selects the best structure for the next iteration. This method is costly because there are too many structures to be evaluated. To solve this problem, we frame the traditional greedy search by Markov decision process and propose an efficient Bayesian network learning approach by integrating reinforcement learning into it. In our approach, a convolutional neural network is employed as the value function to approximate scores. Before evaluating structures using data, the neural network is used to predict scores. Structure candidates with a low predicted score are discarded. By avoiding unnecessary computation, the cooperation of reinforcement learning and greedy search effectively improves the learning efficiency. Two systems, a 10-tank system with 21 monitored variables and the classic Tennessee Eastman process with 52 variables, are employed to demonstrate our approach. The experiment results indicate that the computation cost of our method was reduced by 30%~50%, and the diagnosis accuracy was almost the same.

The systematic and network-centric warfare becomes complicated currently. It is a challenge problem to design and choose the optimal combat system-of-systems to complete missions. In order to solve this challenge, we propose a super-network model based combat system-of-systems architecture (CSoSA) and a scheme space exploration algorithm in this paper. First, a formal definition of CSoSA is given based on the operational capability generation elements. The framework for CSoSA scheme space exploration problem is then presented. The goal of the problem is to select the optimal architecture with maximized expected rewards and minimized cumulative costs. In addition, a novel and optimal algorithm for CSoSA dynamic exploration is proposed based on the greedy search algorithm. Finally, we evaluated our algorithm through simulation experiments, where the results showed that our algorithm is significantly better than several benchmark algorithms. In general, the proposed architecture framework and the exploration algorithm can assist commanders in making further decisions.

Climate change and deterioration require a continuous effort to reinforce flood defences and meet reliability requirements. To efficiently upgrade flood defence systems, insight in costs and benefits of measures at a system level is required throughout the process of planning and design. Due to the size of flood defence systems the number of possible decisions is large, which hampers system optimization. We describe a greedy search algorithm that can find (near-)optimal combinations of reinforcement measures for dike segments. The algorithm has been validated by comparing results for 2800 different dike segments to an integer programming implementation. The difference in objective value (Total Cost) is only 0.04% on average, which is small compared to other uncertainties in assessment and design of dike segments. The algorithm is applied to a reinforcement project for a dike segment of 41 independent sections, and compared to the common design practice which uses reliability-based requirements on a section level. It is found that the resulting reinforced dike segment is 42% cheaper to construct than the one obtained from the common approach, based on the same input information. This illustrates the practical and societal value of the design approach using a greedy search algorithm in this context.

Segmenting perceptual experience into meaningful events is a key cognitive process that helps us make sense of what is happening around us in the moment, as well as helping us recall past events. Nevertheless, little is known about the underlying neural mechanisms of the event segmentation process. Recent work has suggested that event segmentation can be linked to regional changes in neural activity patterns. Accurate methods for identifying such activity changes are important to allow further investigation of the neural basis of event segmentation and its link to the temporal processing hierarchy of the brain. In this study, we introduce a new set of elegant and simple methods to study these mechanisms. We introduce a method for identifying the boundaries between neural states in a brain area and a complementary one for identifying the number of neural states. Furthermore, we present the results of a comprehensive set of simulations and analyses of empirical fMRI data to provide guidelines for reliable estimation of neural states and show that our proposed methods outperform the current state-of-the-art in the literature. This methodological innovation will allow researchers to make headway in investigating the neural basis of event segmentation and information processing during naturalistic stimulation.HighlightsBoundaries between meaningful events are related to neural state transitions.Neural states are temporarily stable regional brain activity patterns.We introduce novel methods for data-driven detection of neural state boundaries.These methods can identify the location and the number of neural state boundaries.Simulations and empirical data support the reliability and validity of our methods.

Soft spherical tensegrity robots are novel steerable mobile robotic platforms that are compliant, lightweight, and robust. The geometry of these robots is suitable for rolling locomotion, and they achieve this motion by properly deforming their structures using carefully chosen actuation strategies. The objective of this work is to consolidate and add to our research to date on methods for realizing rolling locomotion of spherical tensegrity robots. To predict the deformation of tensegrity structures when their member forces are varied, we introduce a modified version of the dynamic relaxation technique and apply it to our tensegrity robots. In addition, we present two techniques to find desirable deformations and actuation strategies that would result in robust rolling locomotion of the robots. The first one relies on the greedy search that can quickly find solutions, and the second one uses a multigeneration Monte Carlo method that can find suboptimal solutions with a higher quality. The methods are illustrated and validated both in simulation and with our hardware robots, which show that our methods are viable means of realizing robust and steerable rolling locomotion of spherical tensegrity robots.

Collaborative beamforming (CB) enables uplink transmission in a wireless sensor network (WSN) composed of sensors (nodes) and far-away access points (APs). It can also be applied to the case where the sensors are equipped with beam-switching structures (BSSs). However, as the antenna arrays of the BSSs are randomly headed due to the irregular mounting surface, some sensors form beams that do not illuminate a desired AP and waste their limited energy. Therefore, to resolve this problem, it is required to switch the beams toward the desired AP. While an exhaustive search can provide the globally optimal combination, a greedy search (GS) is utilized to solve this optimization problem efficiently. Simulation and experimental results verify that under certain conditions the proposed algorithm can drive the sensors to switch their beams properly and increase the received signal-to-noise ratio (SNR) significantly with low computational complexity and energy consumption.

Scientists are attempting to use models of ever-increasing complexity, especially in medicine, where gene-based diseases such as cancer require better modeling of cell regulation. Complex models suffer from uncertainty and experiments are needed to reduce this uncertainty. Because experiments can be costly and time-consuming, it is desirable to determine experiments providing the most useful information. If a sequence of experiments is to be performed, experimental design is needed to determine the order. A classical approach is to maximally reduce the overall uncertainty in the model, meaning maximal entropy reduction. A recently proposed method takes into account both model uncertainty and the translational objective, for instance, optimal structural intervention in gene regulatory networks, where the aim is to alter the regulatory logic to maximally reduce the long-run likelihood of being in a cancerous state. The mean objective cost of uncertainty (MOCU) quantifies uncertainty based on the degree to which model uncertainty affects the objective. Experimental design involves choosing the experiment that yields the greatest reduction in MOCU. This article introduces finite-horizon dynamic programming for MOCU-based sequential experimental design and compares it with the greedy approach, which selects one experiment at a time without consideration of the full horizon of experiments. A salient aspect of the article is that it demonstrates the advantage of MOCU-based design over the widely used entropy-based design for both greedy and dynamic programming strategies and investigates the effect of model conditions on the comparative performances.

This study considers an actual real-world problem encountered by ARM, the world's leading semiconductor intellectual property (IP) supplier, concerning the multi-year assignment of weeks-long computationally intensive projects (executable pieces of code) across a number of capacity-limited clusters. The quality of a projects-to-cluster assignment is measured in terms of several metrics such as the even utilization of clusters, being able to realize all projects, and spreading projects of different research groups evenly across the clusters. The first (theoretical) contribution of this work is to motivate and formally define this novel application and put it in context with related literature. The second (experimental) contribution of this work is about gaining an understanding about the problem and performing an initial investigation on how diferent algorithm types (random search, an EMOA, and greedy search) fare on the problem. Our study revealed that the problem has many infeasible solutions and is challenging to optimize especially for long planning horizons (more than 3 years). While the EMOA is able to outperform random and greedy search (and also the current approach used at ARM) in terms of solution quality discovered, greedy search was the computationally most efficient approach and suitable for short term planning horizons (up to 1 year).

Date of Conference: 23-26 Sept. 2012 Common Spatial Pattern algorithm (CSP) is widely used in Brain Machine Interface (BMI) technology to extract features from dense electrode recordings by using their weighted linear combination. However, the CSP algorithm, is sensitive to variations in channel placement and can easily overfit to the data when the number of training trials is insufficient. Construction of sparse spatial projections where a small subset of channels is used in feature extraction, can increase the stability and generalization capability of the CSP method. The existing 0 norm based sub-optimal greedy channel reduction methods are either too complex such as Backward Elimination (BE) which provided best classification accuracies or have lower accuracy rates such as Recursive Weight Elimination (RWE) and Forward Selection (FS) with reduced complexity. In this paper, we apply the Oscillating Search (OS) method which fuses all these greedy search techniques to sparsify the CSP filters. We applied this new technique on EEG dataset IVa of BCI competition III. Our results indicate that the OS method provides the lowest classification error rates with low cardinality levels where the complexity of the OS is around 20 times lower than the BE. © 2012 IEEE.

Recently, constraint programming has been proposed as a declarative framework for constraint-based pattern mining. In constraint programming, a problem is modelled in terms of constraints and search is done by a general solver. Similar to most pattern mining algorithms, these solvers typically employ exhaustive depth-first search, where constraints are used to prune the search space and make the search viable. In this paper we investigate the use of a similar declarative approach to the problem of pattern set mining. In pattern set mining one is searching for a small and useful set of patterns. In contrast to pattern mining, however, exhaustive search is not common in pattern set mining; the search space is often far too large to make such an approach practical. In this paper, we investigate an approach which aims to make general pattern set mining feasible by using a recently developed general solver that supports exhaustive as well as heuristic search. The key idea in this solver is that next to a declarative specification of the constraints also a high-level declarative description of the search is given. By separating the model and the search from the solver, the approach offers the advantage of reusing constraints and search strategies declaratively, while also allowing fast heuristic search. ispartof: pages:1104-1111 ispartof: Proceedings of the 11th IEEE International Conference on Data Mining Workshops pages:1104-1111 ispartof: Workshop on Declarative Pattern Mining in conjunction with the 11th IEEE International Conference on Data Mining location:Vancouver, Canada date:11 Dec - 14 Dec 2011 status: published