Tree Search Process Research Articles

The application of chessboard game based on integrated learning and UCT algorithm in mental health and emotional regulation

Go plays a very prominent role in quality education and mental health education. Go can cultivate character, make friends, improve interpersonal relationships, and promote mental health. To apply it to mental health and emotional regulation, the AlphaGo series algorithms are analyzed. To address the unstable deep reinforcement learning training, low training data utilization, model sensitivity to hyper-parameters, and heavy parameter tuning burden, the algorithm is optimized by combining ensemble learning and advanced deep reinforcement learning. Then, the weighted voting method is applied to the selection step of sub nodes in the Monte Carlo tree search process. At the same time, asynchronous parallelization improvements are implemented to improve the efficiency of the Monte Carlo tree search algorithm. Finally, a board game based on ensemble learning and improved Monte Carlo tree search algorithm is obtained. Through experimental analysis, the average winning rate of the system was 93.36 %. The number of users who were very satisfied with the game was 83.96 %. After intervention, the depression and anxiety scores of the experimental group students were significantly lower than the control group (P < 0.05). The designed game can effectively alleviate user anxiety and depression, providing new ideas for the psychotherapy.

Cooperative Merging Strategy in Mixed Traffic Based on Optimal Final-State Phase Diagram With Flexible Highway Merging Points

The cooperation between connected and automated vehicles (CAVs) has emerged as a promising way to improve traffic efficiency and safety for ramp merging on highways. Existing research mostly focused on the collaboration of individual CAVs, while the cooperative merging strategy in mixed traffic considering vehicle platoons has been less explored. To address the above problem, this study aims to build connections between sequence scheduling and motion planning in mixed traffic, where individual CAVs, CAVs platoons, and mixed platoons coexist. First, optimal control strategies are presented for vehicles with flexible merging points based on Pontryagin’s minimum principle (PMP), and the final vehicle states in variable conditions are summarized in a phase diagram. Subsequently, the optimal final-state phase diagram is introduced into the passing sequence tree search process, which is designed for different vehicle groups in mixed traffic. Heuristic pruning rules are added to the depth-first search strategy to facilitate finding the optimal solution. Finally, an event-triggered receding horizon optimization algorithm is developed for continuous implementation. The numerical simulations are conducted in multiple traffic volumes, and the simulation results reveal that our proposed algorithm significantly improves the overall traffic efficiency and reduces vehicle-passing delays compared with the traditional FIFO-based cooperation method.

A New Approach for Anonymizing Transaction Data with Set Values

This article proposes a new method that can guarantee strong privacy while minimizing information loss in transactional data composed of a set of each attribute value in a relational database, which is not generally well-known structured data. The proposed scheme adopts the same top-down partitioning algorithm as the existing k-anonymity model, using local generalization to optimize safety and CPU execution time. At the same time, the information loss rate, which is a disadvantage of the existing local generalization, is further improved by reallocating transactions through an additional bottom-up tree search process after the partitioning process. Our scheme shows a very fast processing time compared to the HgHs algorithm using generalization and deletion techniques. In terms of information loss, our scheme shows much better performance than any schemes proposed so far, such as the existing local generalization or HgHs algorithm. In order to evaluate the efficiency of our algorithm, the experiment compared its performance with the existing local generalization and the HgHs algorithm, in terms of both execution time and information loss rate. As a result of the experiment, for example, when k is 5 in k-anonymity for the dataset BMS-WebView-2, the execution time of our scheme is up to 255 times faster than the HgHs algorithm, and with regard to the information loss rate, our method showed a maximum rate of 62.37 times lower than the local generalization algorithm.

Complexity‐reduced maximum‐likelihood hybrid detection for MIMO systems

AbstractFor wireless communications, the multiple‐input multiple‐output (MIMO) system efficiently makes use of the spectrum and enhances the transmission throughput. In this work, the maximum‐likelihood (ML) detection for the MIMO system is studied, and two ML detection algorithms are first considered for the MIMO system, including the sphere decoding (SD) algorithm and an algorithm based on differential metrics (DMs). Each of the two algorithms has its advantages and disadvantages. The two algorithms are first modified such that they are based on the same signal model. Then, a new ML detection algorithm is proposed for the MIMO system based on the hybrid operation of the two modified algorithms on the tree search process, in which both the branch‐and‐bound principle and indicative functions are applied to remove unnecessary searches. The proposed algorithm can attain the ML detection with lower average complexity over low and high ranges of signal‐to‐noise ratio (SNR), as the authors verify by simulations. The proposed ML detection can also generate soft output, and anti‐phase sequences are exploited to further reduce the complexity.

Enhancing Feedback Steering Controllers for Autonomous Vehicles With Deep Monte Carlo Tree Search

Steering control is a vital function for autonomous vehicles, whose performance largely determines the driving safety. The widely used feedback steering controllers degrade significantly when vehicles drive at high speeds. This is mainly because these controllers can not effectively exploit nonlinear vehicle dynamics and future road information. Hence, the steering actions generated by these controllers can not achieve accurate and stable path tracking performance. To overcome this limitation, we propose a deep learning based Monte Carlo Tree Search (DeepMCTS) method to enhance the feedback controllers. Due to the accuracy of the learning-based model in characterizing nonlinear vehicle dynamics and the lookahead search ability of MCTS, the control performance of the feedback controllers can be enhanced significantly. Different from MPC methods, which leverage vehicle dynamics and future road information via online optimization, our method relies on the efficient tree search process. Hence, our method can avoid heavy computational burden. Testing results validate the proposed method can achieve better tracking accuracy and stability in high speeds scenarios.

Space-Time Tree Search for Long-Term Trajectory Prediction

The pedestrian trajectory prediction is critical for autonomous driving, intelligent navigation, and abnormal behavior detection. With the booming of artificial intelligence (AI), many researchers have employed deep learning technologies to solve the pedestrian trajectory prediction problem and obtained relatively better performance in the short-term trajectory prediction. However, long-term trajectory prediction is still challenging to achieve high prediction accuracy. In this work, we propose a space-time tree search (STTS) method for long-term pedestrian trajectory prediction. Compared with existing methods only considering the problem from the space dimension, the proposed method formulates the trajectory prediction problem as a joint space-time tree search process by mapping the environment to a grid map. Since the human’s trajectory is relative to space and time dimensions, the trajectory prediction accuracy can be improved by the two dimensions. Then, a space-time reward trained neural network is employed to extract the pedestrian’s intent with both the scene image and the historical trajectory as input and outputs the prior search probabilities. Finally, the tree search can obtain the optimal predicted trajectory according to the prior probabilities, significantly improving the tree search efficiency. After testing, our proposed method can perform better than existing methods.

Scene mover

We propose a novel approach for automatically generating a move plan for scene arrangement. 1 Given a scene like an apartment with many furniture objects, to transform its layout into another layout, one would need to determine a collision-free move plan. It could be challenging to design this plan manually because the furniture objects may block the way of each other if not moved properly; and there is a large complex search space of move action sequences that grow exponentially with the number of objects. To tackle this challenge, we propose a learning-based approach to generate a move plan automatically. At the core of our approach is a Monte Carlo tree that encodes possible states of the layout, based on which a search is performed to move a furniture object appropriately in the current layout. We trained a policy neural network embedded with a LSTM module for estimating the best actions to take in the expansion step and simulation step of the Monte Carlo tree search process. Leveraging the power of deep reinforcement learning, the network learned how to make such estimations through millions of trials of moving objects. We demonstrated our approach for moving objects under different scenarios and constraints. We also evaluated our approach on synthetic and real-world layouts, comparing its performance with that of humans and other baseline approaches.

A resource‐tuned QoS multicast routing protocol

SummaryThis paper presents a novel framework for quality‐of‐service (QoS) multicast routing with resource allocation that represents QoS parameters, jitter delay, and reliability, as functions of adjustable network resources, bandwidth, and buffer, rather than static metrics. The particular functional form of QoS parameters depends on rate‐based service disciplines used in the routers. This allows intelligent tuning of QoS parameters as functions of allocated resources during the multicast tree search process, rather than decoupling the tree search from resource allocation. The proposed framework minimizes the network resource utilization while keeping jitter delay, reliability, and bandwidth bounded. This definition makes the proposed QoS multicast routing with resource allocation problem more general than the classical minimum Steiner tree problem. As an application of our general framework, we formulate the QoS multicast routing with resource allocation problem for a network consisting of generalized processor sharing nodes as a mixed‐integer quadratic program and find the optimal multicast tree with allocated resources to satisfy the QoS constraints. We then present a polynomial‐time greedy heuristic for the QoS multicast routing with resource allocation problem and compare its performance with the optimal solution of the mixed‐integer quadratic program. The simulation results reveal that the proposed heuristic finds near‐optimal QoS multicast trees along with important insights into the interdependency of QoS parameters and resources.

Maximum-Likelihood Detection for MIMO Systems Based on Differential Metrics

The multiple-input multiple-output (MIMO) system makes efficient use of spectrum and increases the transmission throughput in wireless communications. The sphere decoding (SD) is an efficient algorithm that enables the maximum-likelihood (ML) detection for the MIMO system. However, the SD algorithm has variable complexity, and its complexity increases rapidly with decreasing signal-to-noise ratio (SNR). In this paper, we propose a novel ML detection algorithm for the MIMO system based on differential metrics. We define the differential metrics and derive the associated recursive calculation. We then give the indicative functions, which can be used to possibly find some ML-detected bits of the initial sequence. The indicative functions are further applied to implement an efficient tree search for ML detection. The proposed algorithm does not need QR decomposition and matrix inversion. The tree search process needs only the additive operation, while the number of multiplications before the tree search is constant. Our algorithm can achieve the exact ML detection as the SD algorithm. Unlike the SD algorithm, the complexity of our algorithm reduces with decreasing SNR, whereas at high SNR, the complexity is nearly constant. We also give the convergence analysis for the SD and proposed algorithms, and the simulation verifies our analysis. For the proposed algorithm, the number of necessary memory is constant during the tree search, and the implementation by parallel processing is possible. The soft output of ML-detected bits can also be generated in our algorithm.

Performance Analysis of Layer Pruning on Sphere Decoding in MIMO Systems

Sphere decoding (SD) for multiple-input and multiple-output systems is a well-recognized approach for achieving near-maximum likelihood performance with reduced complexity. SD is a tree search process, whereby a large number of nodes can be searched in an effort to find an estimation of a transmitted symbol vector. In this paper, a simple and generalized approach called layer pruning is proposed to achieve complexity reduction in SD. Pruning a layer from a search process reduces the total number of nodes in a sphere search. The symbols corresponding to the pruned layer are obtained by adopting a QRM-MLD receiver. Simulation results show that the proposed method reduces the number of nodes to be searched for decoding the transmitted symbols by maintaining negligible performance loss. The proposed technique reduces the complexity by 35% to 42% in the low and medium signal-to-noise ratio regime. To demonstrate the potential of our method, we compare the results with another well-known method — namely, probabilistic tree pruning SD.

Recovering a relation from a decomposition using constraint satisfaction

A purely distributive representation of a relation is characterized by invariance under changes in the sorted order of the tuples of the relation. Natural join, intersection, and union operations can be performed on distributive representations of the operands with very high parallelism because there is no need for any search, nor for prior sorting. What is obtained is a distributive representation of the result relation, from which the relation itself can be recovered, in any desired sorted order, by a serial tree-search process. This paper describes a particular distributive representation scheme, and demonstrates that the associated recovery process corresponds to solving a constraint satisfaction problem. It is shown that by pruning the tree-search, it is possible to carry out the recovery process in a time which is linear in the cardinality of the recovered relation, in some cases. Although the recovery process may occasionally obtain additional spurious tuples, not present in the relation, it is shown that the occurrence of these spurious tuples may be made extremely rare by an appropriate choice of system parameters.

Parallelizing object recognition on the hypercube

Abstract In this paper a parallel Interpretation Tree search algorithm for object recognition on the Intel iPSC/2 hypercube multicomputer is presented. The input to the algorithm is a set of sensory measurements consisting of sensor locations and the directions of the surface normal. Such data is typical of sparse range or tactile sensors. At this time the algorithm is capable of handling two-dimensional objects or three-dimensional objects located on a plane with all sensory measurements parallel to the plane. The objects are typically those which a robot would encounter in an industrial scene during the process of automated inspection or automated assembly. The algorithm is able to handle polyhedra or objects which can be approximated as piecewise combination of polyhedra. Both single- and multiple-object scenes are considered. Three strategies for mapping the Interpretation Tree search process on the hypercube are considered. The performance of the algorithm for each of these mapping strategies is examined in terms of parameters such as speed-up, processor utilization, inter-processor communication overhead and load sharing between processors. It is shown that the requirement for uniform load sharing leads to increased inter-processor communication. Experimental results are presented and future directions are outlined.

Constraint satisfaction algorithms1

AbstractConstraint satisfaction problems are ubiquitous in artificial intelligence and many algorithms have been developed for their solution. This paper provides a unified survey of some of these, in terms of three classes: (i) tree search, (ii) arc consistency (AC), and (iii) hybrid tree search/arc consistency algorithms. It is shown that several important algorithms, when slightly rearranged, are of the latter hybrid form, but with arc consistency components that do not necessarily achieve full arc consistency at the tree nodes. Accordingly, we define several newpartialAC procedures, AC1/5, AC1/4, AC1/3, and AC½, analogous to the well‐knownfullAC algorithms which Mackworth has called AC1, AC2, and AC3. The fractional suffixes on our AC algorithms are roughly proportional to the degree of partial arc consistency they achieve. Unlike traditional versions, our AC algorithms (full and partial) are presented in a parameterized form to allow them to be embedded efficiently at the nodes of a tree search process. Algorithm complexities are compared empirically, using then‐queens problem and a new version called confusedn‐queens. Gaschnig's Backmarking (a tree search algorithm) and Haralick's Forward Checking (a hybrid algorithm) are found to be the most efficient. For the hybrid algorithms, we find that it pays to do little arc consistency processing at the nodes, incurring more nodes, but sufficiently reducing the work per node so as to obtain less work over the whole tree. The unified view taken here suggests several new algorithms. Preliminary results show one of these to be the best algorithm so far.

Transition matrices in the measurement and control of synchronous sequential machines

This paper develops a matrix approach to the solution of the measurement and control problems in synchronous sequential machines. The control problem in sequential machines is concerned with finding input sequences that take a given machine from a known initial state to a predesignated terminal state. There may be many different measurement problems of interest in sequential machines. One important measurement problem is the identification of the unknown initial state of the machine, called the initial-state identification problem or diagnosing problem; another important measurement problem is the identification of the terminal state of the machine, known as the terminal-state identification problem or homing problem, of which a special case is the synchronizing problem. These measurement problems may be solved by applying predetermined input sequences to the machine and noting the resulting output sequences. The conventional approach to the solution of these measurement and control problems is to make use of the information contained in the transition table of the machine in conjunction with its response tree, which is basically an exhaustive tree search process. In the present paper, instead of using the transition table and the corresponding response tree, we use only the transition matrix representation of the machine and its higher order forms to solve the aforementioned problems. The suggested approach is very systematic and completely algorithmic, and thus lends itself to easy computer implementation.