An Introduction to Development of Centralized and Distributed Stochastic Approximation Algorithm with Expanding Truncations

The stochastic approximation algorithm (SAA), starting from the pioneer work by Robbins and Monro in 1950s, has been successfully applied in systems and control, statistics, machine learning, and so forth. In this paper, we will review the development of SAA in China, to be specific, the stochastic approximation algorithm with expanding truncations (SAAWET) developed by Han-Fu Chen and his colleagues during the past 35 years. We first review the historical development for the centralized algorithm including the probabilistic method (PM) and the ordinary differential equation (ODE) method for SAA and the trajectory-subsequence method for SAAWET. Then, we will give an application example of SAAWET to the recursive principal component analysis. We will also introduce the recent progress on SAAWET in a networked and distributed setting, named the distributed SAAWET (DSAAWET).

A Centralized Algorithm with Collision Avoidance for Trajectory Planning in Preflight Stage

In order to better understand pretactical phase flow management with the flight plan centralized processing at its core, based on the flight plan centralized processing system and track-based operation, the aircraft’s 4D trajectory planning challenges require a deeper level of analysis. Firstly, through establishing a flight performance prediction model, in which the flight plan data is extracted and the time when an aircraft passed a specified waypoint is calculated, a 4D flight prediction can be derived. Secondly, the air traffic flow of the waypoint is calculated, and a converging point along a flight route is selected. Through adjusting the time and speed of the aircraft passing this point, conflict between aircraft is avoided. Finally, the flight is verified by CCA1532, with the connecting flight plan centralized processing center set in line with the company’s requirements. The results demonstrate that according to flight plans, the 4D trajectory of the aircraft can be predicted with the nearest minute and second, and the flow of a total of 20 aircraft within one hour before and after the passage of CCA1532 at key point WADUK can be calculated. When there is a conflict of 88 s between the convergence point and flight B, the speed of B aircraft is adjusted from 789 km/h to 778 km/h, and the time of passing the WADUK point is increased by 7 s, thereby realizing the conflict-free trajectory planning of the two flights.

Training of interpreters for International Army Games

This study considers the process of training of language support specialists for International Army Games (IAG) as well as its problems. General information on IAG (history, fields, geography, organisational structure, etc.) is given with its correlation to the language support tasks. In connection with the annually increasing interest in this type of competition (17 participating countries in 2015 and 32 in 2020) and, accordingly, increasing needs for interpreters, justifies the relevance of this study. Specific IAG situations where translation is being carried out are given. The scientific novelty of the study lies in the low degree of coverage of the topic and the lack of a centralized algorithm for the preparation of interpreters for this international event. Considering the nature of the linguistic tasks performed during the preparation and conduct of the IAG, the basic requirements to the specialists being sent are formed. The experience of participation as an interpreter at the IAG is presented and analyzed, the problematic issues in terms of linguistic training and background knowledge are pointed out. A comparison of the two methods of training interpreters involved in the IAG, the main advantages and disadvantages of each of the two methods is given. As a result of the above-mentioned analysis a list of recommendations on the preparation of specialists in linguistic support for IAG is formed, taking into account the specific situations in which the translation is carried out. A conclusion about the further development of this direction and the ways of solving the indicated problems is made.

Observe Before Play: Multi-Armed Bandit with Pre-Observations

We consider the stochastic multi-armed bandit (MAB) problem in a setting where a player can pay to pre-observe arm rewards before playing an arm in each round. Apart from the usual trade-off between exploring new arms to find the best one and exploiting the arm believed to offer the highest reward, we encounter an additional dilemma: pre-observing more arms gives a higher chance to play the best one, but incurs a larger cost. For the single-player setting, we design an Observe-Before-Play Upper Confidence Bound (OBP-UCB) algorithm for K arms with Bernoulli rewards, and prove a T-round regret upper bound O(K2log T). In the multi-player setting, collisions will occur when players select the same arm to play in the same round. We design a centralized algorithm, C-MP-OBP, and prove its T-round regret relative to an offline greedy strategy is upper bounded in O(K4/M2log T) for K arms and M players. We also propose distributed versions of the C-MP-OBP policy, called D-MP-OBP and D-MP-Adapt-OBP, achieving logarithmic regret with respect to collision-free target policies. Experiments on synthetic data and wireless channel traces show that C-MP-OBP and D-MP-OBP outperform random heuristics and offline optimal policies that do not allow pre-observations.

Fair compute loads enabled by blockchain: sharing models by alternating client and server roles

Objective Decentralized privacy-preserving predictive modeling enables multiple institutions to learn a more generalizable model on healthcare or genomic data by sharing the partially trained models instead of patient-level data, while avoiding risks such as single point of control. State-of-the-art blockchain-based methods remove the “server” role but can be less accurate than models that rely on a server. Therefore, we aim at developing a general model sharing framework to preserve predictive correctness, mitigate the risks of a centralized architecture, and compute the models in a fair way Materials and Methods We propose a framework that includes both server and “client” roles to preserve correctness. We adopt a blockchain network to obtain the benefits of decentralization, by alternating the roles for each site to ensure computational fairness. Also, we developed GloreChain (Grid Binary LOgistic REgression on Permissioned BlockChain) as a concrete example, and compared it to a centralized algorithm on 3 healthcare or genomic datasets to evaluate predictive correctness, number of learning iterations and execution time Results GloreChain performs exactly the same as the centralized method in terms of correctness and number of iterations. It inherits the advantages of blockchain, at the cost of increased time to reach a consensus model Discussion Our framework is general or flexible and can also address intrinsic challenges of blockchain networks. Further investigations will focus on higher-dimensional datasets, additional use cases, privacy-preserving quality concerns, and ethical, legal, and social implications Conclusions Our framework provides a promising potential for institutions to learn a predictive model based on healthcare or genomic data in a privacy-preserving and decentralized way.

Decentralized Task Allocation in Multi-Robot Systems via Bipartite Graph Matching Augmented With Fuzzy Clustering

Robotic systems, working together as a team, are becoming valuable players in different real-world applications, from disaster response to warehouse fulfillment services. Centralized solutions to coordinating multi-robot teams often suffer from poor scalability and vulnerability to communication disruptions. This paper develops a decentralized multi-agent task allocation (Dec-MATA) algorithm for multi-robot applications. The task planning problem is posed as a maximum-weighted matching of a bipartite graph, the solution of which using the blossom algorithm allows each robot to autonomously identify the optimal sequence of tasks it should undertake. The graph weights are determined based on a soft clustering process, which also plays a problem decomposition role seeking to reduce the complexity of the individual-agents’ task assignment problems. To evaluate the new Dec-MATA algorithm, a series of case studies (of varying complexity) are performed, with tasks being distributed randomly over an observable 2D environment. A centralized approach, based on a state-of-the-art MILP formulation of the multi-Traveling Salesman problem is used for comparative analysis. While getting within 7–28% of the optimal cost obtained by the centralized algorithm, the Dec-MATA algorithm is found to be 1–3 orders of magnitude faster and minimally sensitive to task-to-robot ratios unlike the centralized algorithm.

Synchronization of distributed robotics control system

The paper presents the novel project that has been proposed and realized together with the implementation of the process of heating thermosetting components based on advanced industrial equipment and software tools. In the fundamental task of optimization, the phenomenon of process synchronization based on a centralized algorithm adapted to the specific requirements of production was taken into account. The proposed research station was optimized and located in the laboratory of the Interdisciplinary Computer Modelling of the University of Rzeszow, that specializes in raising the efficiency of industrial processes