KBFS: K-Best-First Search

As location data are widely available to portable devices, trajectory tracking of moving objects has become an essential technology for most location-based services. To maintain such streaming data of location updates from mobile clients, conventional approaches such as time-based regular location updating and distance-based location updating have been used. However, these methods suffer from the large amount of data, redundant location updates, and large trajectory estimation errors due to the varying speed of moving objects. In this paper, we propose a simple but efficient online trajectory data reduction method for portable devices. To solve the problems of redundancy and large estimation errors, the proposed algorithm computes trajectory errors and finds a recent location update that should be sent to the server to satisfy the user requirements. We evaluate the proposed algorithm with real GPS trajectory data consisting of 17 201 trajectories. The intensive simulation results prove that the proposed algorithm always meets the given user requirements and exhibits a data reduction ratio of greater than 87 % when the acceptable trajectory error is greater than or equal to 10 meters.

<p>This paper offers a focused overview of pathfinding algorithms, particularly emphasizing Greedy Best First Search (G-BFS) and Rapidly-Exploring Random Trees (RRT). Their performance is evaluated within a 2D grid setting tailored for Unmanned Aerial Vehicles (UAVs). Divided into two main sections, the study first expounds on the theoretical underpinnings of these algorithms, followed by empirical validation. A series of systematic experiments, involving varied 2D grid dimensions and traversal patterns, facilitates a comparative analysis between G-BFS and RRT. Importantly, the real-world implementation of these algorithms in UAV navigation underscores their practicality, illuminating their respective execution times and resource utilization. While G-BFS thrives in straightforward scenarios, RRT, especially RRT*, displays superior capability in navigating more intricate and expansive terrains, albeit with marginally extended execution durations attributed to its explorative nature.</p>

The use of a policy and a heuristic function for guiding search can be quite effective in adversarial problems, as demonstrated by AlphaGo and its successors, which are based on the PUCT search algorithm. While PUCT can also be used to solve single-agent deterministic problems, it lacks guarantees on its search effort and it can be computationally inefficient in practice. Combining the A* algorithm with a learned heuristic function tends to work better in these domains, but A* and its variants do not use a policy. Moreover, the purpose of using A* is to find solutions of minimum cost, while we seek instead to minimize the search loss (e.g., the number of search steps). LevinTS is guided by a policy and provides guarantees on the number of search steps that relate to the quality of the policy, but it does not make use of a heuristic function. In this work we introduce Policy-guided Heuristic Search (PHS), a novel search algorithm that uses both a heuristic function and a policy and has theoretical guarantees on the search loss that relates to both the quality of the heuristic and of the policy. We show empirically on the sliding-tile puzzle, Sokoban, and a puzzle from the commercial game `The Witness' that PHS enables the rapid learning of both a policy and a heuristic function and compares favorably with A*, Weighted A*, Greedy Best-First Search, LevinTS, and PUCT in terms of number of problems solved and search time in all three domains tested.

Best-first search is a widely used problem solving technique in the field of artificial intelligence. The method has useful applications in operations research as well. Here we describe an application to constrained two-dimensional cutting stock problems of the following type: A stock rectangle S of dimensions (L, W) is supplied. There are n types of demanded rectangles r1, r2, …, rn, with the ith type having length li, width wi, value vi, and demand constraint bi. It is required to produce, from the stock rectangle S, ai copies of ri, 1 ≤ i ≤ n, to maximize a1v1 + a2v2 + · + anvn subject to the constraints ai ≤ bi. Only orthogonal guillotine cuts are permitted. All parameters are integers. A best-first tree search algorithm based on Wang's bottom-up approach is described that guarantees optimal solutions and is more efficient than existing methods.

In this paper, we propose a new method for correcting the depth image, which was obtained by applying a matching scheme to stereo images and often includes parts with large error, based on the sparsity of depth image. When the depth image is obtained by a stereo matching, a small pixel correspondence error causes large estimation errors in the depth image. Also, original depth image can be considered to have sparsity the same as many natural signals without noise. So we correct the depth image that was obtained by the stereo matching, based on the sparsity of the original depth image. First, the depth image parts with a higher probability of containing large estimation errors are selected as the areas in which the depth has relatively large difference from that which was obtained by applying the median filter to the estimated depth image. Second, the depth image is applied with the inpainting procedure based on the data sparsity [1] as shown in Fig. 1 in which the data of the selected area are treated as being lost. In particular the depth image in a region, which corresponds to an object in 3-D space, is wavelet transformed by SA-DWT (Shape-Adaptive Discrete Wavelet Transform). The smaller wavelet coefficients are truncated to zero with a threshold procedure. With decreasing threshold value, the wavelet transform, smaller coefficient zeroing, and the inverse wavelet transform processes are repeated until the processed depth image is converged. Experiments show that the proposed method is able to remove large errors in the depth image which had been obtained by the stereo matching scheme.

Diverse best-first search (DBFS) is a successful algorithm for satisficing planning that is built on top of greedy best-first search and uses stochastic search techniques to select the next ``best'' state that is not always in agreement with the heuristic function. DBFS thus considers a diversity of search directions and avoids search plateaus caused by inaccurate heuristic estimation. However, the scalability of DBFS is still limited by the available memory resource, as in any planning system that uses best-first search at its core. This paper presents the diverse depth-first search (DDFS) algorithm that overcomes the memory bottleneck of DBFS. DDFS requires much less memory than DBFS yet it successfully incorporates DBFS' randomized node selection scheme for effective search diversification. Additionally, DDFS is enhanced with a transposition table to avoid duplicate search effort commonly found in depth-first search. Experimental results in solving problems used in previous International Planning Competitions show that DDFS can solve instances that remain unsolvable by DBFS due to its excessive memory requirements.

Quality and cloud flags of GOSAT/FTS: Fourier Transform Spectrometer onboard Greenhouse gasses Observation Satellite products taking into account cirrus clouds and thick aerosols are considered and proposed. Influence due to cirrus and thick aerosol on estimation of column CO2 and CH4 with GOSAT/FTS data is clarified. Relatively large estimation errors are observed in column CO2 and CH4 retrievals with FTS data in some atmospheric conditions. In order to find such cases, retrieval results and quality/cloud flags in the GOSAT/FTS data products are checked. Through the investigation, it is found that relatively large error is caused by convergence problem due to cirrus clouds and thick aerosols. In the proposed paper, some of the cases of which relatively large estimation error is occurred at the Saga TCCON (The Total Carbon Column Observing Network) site are investigated. Also, a comparative study is conducted between standard products provided by NASA/JPL and the Levenberg-Marquardt based least square method of column CO2 and CH4 retrieval. It is suggested that some improvements of estimation accuracy of column CO2 and CH4 retrieval with GOSAT/FTS data can be expected.

Area reduction factors (ARFs), which are used to convert estimates of extreme point rainfall to estimates of extreme area-averaged rainfall, are central to conventional flood risk assessment. Errors in the estimation of ARFs can result in large errors in subsequent estimates of design rainfall and discharge. This paper presents a critical examination of commonly used ARFs, particularly those from the U.S. Weather Bureau TP-29, demonstrating that they do not adequately represent the true properties of extreme rainfall. This lack of representativeness is due mainly to formulations that mix rainfall observations from different storms and different storm types. Storm catalogs developed from a 10-year high-resolution radar rainfall data are used set to estimate storm-centered ARFs for Charlotte, North Carolina. Storms are classified as either tropical or nontropical to demonstrate that storm type strongly influences spatial rainfall structure. While there appears to be some relationship between ARF str...

A common goal of conventional fisheries management is to maintain fishing mortality at a rate that ensures an adequate level of lifetime egg production (LEP) for population sustainability. However, larvae from young spawners can experience higher mortality rates than larvae of older spawners, reducing the effect of egg production by young females (hereafter, maternal age effects). This reduction leads to an error in LEP that can be accounted for by reducing the fishing mortality rate, but raises the question of the magnitude of these errors if they are present but not accounted for. Calculations using parameters from a typical long-lived fish demonstrated that maternal age effects resulted in large errors in estimates of lifetime reproduction when there was a large contrast in the larval mortality rate extending over the reproductive life span. Errors were small when maternal age effects reduced the reproductive potential of only the very youngest spawners, at ages when a small fraction of females are mature. A specific example using the empirically derived maternal age effect for black rockfish (Sebastes melanops) indicated that errors in traditional management would be small for this species.

Upsurges and downsurges are calculated and plotted for a simple pump discharge line provided with an air chamber. Basic parameters such as pipeline constant, air chamber parameter, pipe wall friction, and orifice resistance are used. The results of this paper can be used to determine the necessary volume of the air chamber. Computer studies indicate that the assumption of the rigid water column and the concentration of pipe friction at the pump end of the pipeline yields reasonably good results at the pump end; however, because of these assumptions, large errors in estimation of both upsurges and downsurges occur at the midpoint and particularly at the quarter point of the pipeline. Pipe friction has a substantially different effect on surges than that of the orifice resistance; these two effects should therefore be considered separately. A differential orifice is recommended and considered; this orifice should have a low resistance to flow out of the chamber.

The analysis of nutrient budgets is a common method for assessing the biogeochemical function of estuaries including denitrification and nutrient retention rates. The Land Ocean Interactions in the Coastal Zone (LOICZ) guidelines for constructing such budgets concentrate on the simplest case where an estuary or embryment is treated as a single box which is well-mixed both vertically and horizontally and at steady-state. We show that these simplifying assumptions can lead to significant and sometimes very large errors in estimates of internal retention (or production) rates. If the load to an estuary varies significantly through the year, the use of time-averaged concentrations of nutrient and salinity to calculate retention rates is shown to cause errors of up to 30% depending on the circumstances. The second case considered examines the consequences of treating an estuary which has significant long-estuary salinity and nutrient gradients as a well-mixed box. In a simple case considered, the calculated internal production from a distributed source is underestimated by a factor of two. The errors are shown to depend on the estuary's mixing and geometrical characteristic as well as on the location of the nutrient sources. The third case considers the errors in the calculated internal retention/production rates of treating an estuary with a two-layer circulation as a single-box system. The potential errors are severe. A comparison between a one-box analysis and a two-box analysis which accounts for the true estuarine circulation shows that the two analyses can yield calculated retention rates of opposite sign. In this situation, one configuration for the estuary would appear to sequester nutrient, whereas the other would be a net exporter of nutrient.

Suboptimal heuristic search algorithms such as weighted A* and greedy best-first search are widely used to solve problems for which guaranteed optimal solutions are too expensive to obtain. These algorithms crucially rely on a heuristic function to guide their search. However, most research on building heuristics addresses optimal solving. In this paper, we illustrate how established wisdom for constructing heuristics for optimal search can fail when considering suboptimal search. We consider the behavior of greedy best-first search in detail and we test several hypotheses for predicting when a heuristic will be effective for it. Our results suggest that a predictive characteristic is a heuristic's goal distance rank correlation (GDRC), a robust measure of whether it orders nodes according to distance to a goal. We demonstrate that GDRC can be used to automatically construct abstraction-based heuristics for greedy best-first search that are more effective than those built by methods oriented toward optimal search. These results reinforce the point that suboptimal search deserves sustained attention and specialized methods of its own.

A new derivation of simple asteroid thermal models is presented, investigating the need to account correctly for Kirchhoff's law of thermal radiation when IR observations contain substantial reflected sunlight. The framework applies to both the NEATM and related thermal models. A new parameterization of these models eliminates the dependence of thermal modeling on visible absolute magnitude H, which is not always available. Monte Carlo simulations are used to assess the potential impact of violating Kirchhoff's law on estimates of physical parameters such as diameter and IR albedo, with an emphasis on NEOWISE results. The NEOWISE papers use ten different models, applied to 12 different combinations of WISE data bands, in 47 different combinations. The most prevalent combinations are simulated and the accuracy of diameter estimates is found to be depend critically on the model and data band combination. In the best case of full thermal modeling of all four band the errors in an idealized model the 1σ (68.27%) confidence interval is −5% to +6%, but this combination is just 1.9% of NEOWISE results. Other combinations representing 42% of the NEOWISE results have about twice the CI at −10% to +12%, before accounting for errors due to irregular shape or other real world effects that are not simulated. The model and data band combinations found for the majority of NEOWISE results have much larger systematic and random errors. Kirchhoff's law violation by NEOWISE models leads to errors in estimation accuracy that are strongest for asteroids with W1, W2 band emissivity ϵ12 in both the lowest (0.605≤ϵ12≤0.780), and highest decile (0.969≤ϵ12≤0.988), corresponding to the highest and lowest deciles of near-IR albedo pIR. Systematic accuracy error between deciles ranges from a low of 5% to as much as 45%, and there are also differences in the random errors. Kirchhoff's law effects also produce large errors in NEOWISE estimates of pIR, particularly for high values. IR observations of asteroids in bands that have substantial reflected sunlight can largely avoid these problems by adopting the Kirchhoff law compliant modeling framework presented here, which is conceptually straightforward and comes without computational cost.

This paper describes an optimal guidance policy for a vehicle to reach a relative position to a target, using information from a single flxed camera. Applying an extended Kalman flltering method, both of a velocity and a position of the vehicle relative to the target, can be estimated. However, estimates of a distance between the vehicle and the target are much worse than those of the other states. Therefore, in this paper, an optimal guidance policy is introduced that can reach the destination while maximizing the predicted accuracy of the range estimation. By limiting vehicle motion to a two dimensionals, the exact solution for control inputs for this optimization problem can be obtained. Simulation results show that the resulting optimal guidance policy gives far more accurate range estimation than a simple linear guidance policy. The automation of unmanned aerial vehicles(UAVs), has progressively developed in recent years. The development of sensors, such as GPS, made a large contribution. In most cases, UAV automated ∞ight control has been achieved by using multi- sensor fusion to estimate vehicle states accurately. However, because this becomes a complex and expensive system and not suitable for small unreusable UAVs, there is a need to make an autonomous ∞ight system which is simpler and less expensive. As seen in nature birds or insects, vision information can be almost exclusively utilized in an autonomous ∞ight system. This has the potential to improve system reliability, performance, and cost. Inspired from this idea, this paper introduces a method to navigate and guide a vehicle to reach a target using vision information from a single camera flxed to the vehicle. In this approach, an extended Kalman fllter(EKF) is utilized to estimate relative velocity and position. A single camera provides measurements of target horizontal and vertical position in its image, with noise. By applying an EKF to these two measurements, estimates of the relative position and velocity are obtained. The EKF estimation performance depends on camera motion, and the estimation will be improved by an appropriate trajectory generation. 1 In the case of the vehicle approaching straight toward the target, the velocity and position in the camera image plane can be estimated with relatively good accuracy. However, the estimation of distance between the vehicle(or camera) and the target includes large errors. If we use these poor range estimates to control a vehicle performing station-keeping with the target, a dangerous overshoot may occur. The reason for this large estimation error is that the vehicle does not necessary have enough lateral motion to estimate this depth. It is well known that the accuracy of range estimation depends on camera translating motion, and the best translation for range estimation is a motion parallel to its image plane. 2 Therefore, if we let the vehicle ∞y to the target with meandering path, we can obtain the more accurate range estimates and avoid the overshoot. Through the EKF, the variance of estimation errors for each state is available. To maximize the estimation accuracy, one should minimize the variance. In other words, to obtain the optimal ∞ight path to the relative position to the target, we have to set the performance index in order to minimize the variance of the estimation error of range and solve the resulting optimization problem subjected to a known camera motion. In this paper, we show that we can solve this problem analytically. Then, comparing simulation results of

There are two major paradigms for linear-space heuristic search: iterative deepening (IDA*) and recursive best-first search (RBFS). While the node regeneration overhead of IDA* is easily characterized in terms of the heuristic branching factor, the overhead of RBFS depends on how widely the promising nodes are separated in the search tree, and is harder to anticipate. In this paper, we present two simple techniques for improving the performance of RBFS while maintaining its advantages over IDA*. While these techniques work well in practice, they do not provide any theoretical bounds on the amount of regeneration overhead. To this end, we introduce RBFScr, the first method for provably bounding the regeneration overhead of RBFS. We show empirically that this improves its performance in several domains, both for optimal and suboptimal search, and also yields a better linear-space anytime heuristic search. RBFScr is the first linear space best-first search robust enough to solve a variety of domains with varying operator costs.