Conflict Resolution Problem Research Articles

Priority-based intelligent resolution method of multi-aircraft flight conflicts

Abstract The rising demand for air traffic will inevitably result in a surge in both the number and complexity of flight conflicts, necessitating intelligent strategies for conflict resolution. This study addresses the critical challenges of scalability and real-time performance in multi-aircraft flight conflict resolution by proposing a comprehensive method that integrates a priority ranking mechanism with a conflict resolution model based on the Markov decision process (MDP). Within this framework, the proximity between aircraft in a multi-aircraft conflict set is dynamically assessed to establish a conflict resolution ranking mechanism. The problem of multi-aircraft conflict resolution is formalised through the MDP, encompassing the design of state space, discrete action space and reward function, with the transition function implemented via simulation prediction using model-free methods. To address the positional uncertainty of aircraft in real-time scenarios, the conflict detection mechanism introduces the aircraft’s positional error. A deep reinforcement learning (DRL) environment is constructed incorporating actual airspace structures and traffic densities, leveraging the Actor Critic using Kronecker-factored Trust Region (ACKTR) algorithm to determine resolution actions. The experimental results indicate that with 20–30 aircraft in the airspace, the success rate can reach 94% for the training set and 85% for the test set. Furthermore, this study analyses the impact of varying aircraft numbers on the success rate within a specific airspace scenario. The outcomes of this research provide valuable insights for the automation of flight conflict resolution.

CONFLICT RESOLUTION STRATEGY BASED ON DEEP REINFORCEMENT LEARNING FOR AIR TRAFFIC MANAGEMENT

With the continuous increase in flight flows, the flight conflict risk in the airspace has increased. Aiming at the problem of conflict resolution in actual operation, this paper proposes a tactical conflict resolution strategy based on Deep Reinforcement Learning. The process of the controllers resolving conflicts is modelled as the Markov Decision Process. The Deep Q Network algorithm trains the agent and obtains the resolution strategy. The agent uses the command of altitude adjustment, speed adjustment, or heading adjustment to resolve a conflict, and the design of the reward function fully considers the air traffic control regulations. Finally, simulation experiments were performed to verify the feasibility of the strategy given by the conflict resolution model, and the experimental results were statistically analyzed. The results show that the conflict resolution strategy based on Deep Reinforcement Learning closely reflected actual operations regarding flight safety and conflict resolution rules.

General real-time three-dimensional multi-aircraft conflict resolution method using multi-agent reinforcement learning

Reinforcement learning (RL) techniques have been studied for solving the conflict resolution (CR) problem in air traffic management, leveraging their potential for computation and ability to handle uncertainty. However, challenges remain that impede the application of RL methods to CR in practice, including three-dimensional manoeuvres, generalisation, trajectory recovery, and success rate. This paper proposes a general multi-agent reinforcement learning approach for real-time three-dimensional multi-aircraft conflict resolution, in which agents share a neural network and are deployed on each aircraft to form a distributed decision-making system. To address the challenges, several technologies are introduced, including a partial observation model based on imminent threats for generalisation, a safety separation relaxation model for multiple flight levels for three-dimensional manoeuvres, an adaptive manoeuvre strategy for trajectory recovery, and a conflict buffer model for success rate. The Rainbow Deep Q-learning Network (DQN) is used to enhance the efficiency of the RL process. A simulation environment that considers flight uncertainty (resulting from mechanical and navigation errors and wind) is constructed to train and evaluate the proposed approach. The experimental results demonstrate that the proposed method can resolve conflicts in scenarios with much higher traffic density than in today’s real-world situations.

Flight Conflict Resolution and Trajectory Recovery Through Mixed Integer Nonlinear Programming Based on Speed and Heading Angle Change

A scheme for conflict resolution with trajectory recovery is proposed to solve the problem of multi-aircraft flight conflict. First, the conflict resolution problem is modeled as an optimal control problem. The weighted sum of the speed change and the heading angle change is defined as the objective function. The limitations of distance, aircraft performance, and route width are taken as constraints. Second, the optimization problem is resolved by mixed integer nonlinear programming. Conflict resolution with trajectory recovery is then achieved by speed and heading angle changing three times based on the optimal solution; whether the speed and heading angle need to be changed depends on the corresponding weight coefficients in the objective function. Finally, the applicability and superiority of the designed conflict resolution scheme are verified, which is of great significance to the application of conflict resolution with trajectory recovery schemes in automated air traffic control systems.

Three-Dimensional Flight Conflict Detection and Resolution Based on Particle Swarm Optimization

This paper presents a conflict detection and resolution method based on a velocity obstacle method for flight conflicts in a three-dimensional space. With the location and speed information of the two aircraft, the optimal relief strategy is obtained using particle swarm optimization. Aiming at the problem of excessive computational complexity in solving flight conflicts in a three-dimensional space with a cylindrical flight protection zone, an improved method for narrowing the search range is proposed to achieve a rapid solution by simplifying the complicated three-dimensional problem into a two-dimensional problem. The generality and flexibility of the method is effectively verified through simulations in flight conflict scenarios which almost cover all common situations. The experimental results show that the method can accurately determine the conflict time and generate the optimal relief strategy for different scenarios. The improved method of optimizing-search-range can significantly improve the computational efficiency, taking about 0.4 s to find the optimal solution, which can be used in real-time conflict resolution. The study provides a new solution for the flight conflict resolution problem.

Tactical conflict resolution in urban airspace for unmanned aerial vehicles operations using attention-based deep reinforcement learning

Unmanned aerial vehicles (UAVs) have gained much attention from academic and industrial areas due to the significant number of potential applications in urban airspace. A traffic management system for these UAVs is needed to manage this future traffic. Tactical conflict resolution for unmanned aerial systems (UASs) is an essential piece of the puzzle for the future UAS Traffic Management (UTM), especially in very low-level (VLL) urban airspace. Unlike conflict resolution in higher altitude airspace, the dense high-rise buildings are an essential source of potential conflict to be considered in VLL urban airspace. In this paper, we propose an attention-based deep reinforcement learning approach to solve the tactical conflict resolution problem. Specifically, we formulate this task as a sequential decision-making problem using Markov Decision Process (MDP). The double deep Q network (DDQN) framework is used as a learning framework for the host drone to learn to output conflict-free maneuvers at each time step. We use the attention mechanism to model the individual neighbor's effect on the host drone, endowing the learned conflict resolution policy to be adapted to an arbitrary number of neighboring drones. Lastly, we build a simulation environment with various scenarios covering different types of encounters to evaluate the proposed approach. The simulation results demonstrate that our proposed algorithm provides a reliable solution to minimize secondary conflict counts compared to learning and non-learning-based approaches under different traffic density scenarios.

Rules of the Road: Formal Guarantees for Autonomous Vehicles With Behavioral Contract Design

The problem of safe and fair conflict resolution among inertial, distributed agents—particularly in highly interactive settings—is of paramount importance to the autonomous vehicles industry. The difficulty of solving this problem can be attributed to the fact that agents have to reason over other agents' complex behaviors. We propose the idea of using a behavioral contract to capture a set of explicitly defined assumptions about how all agents in the environment make decisions. In this article, we present a behavioral contract for a specific class of agents that can guarantee the safety and liveness (i.e., progress) of all agents operating in accordance with it. The behavioral contract has two main components—an ordered behavioral rulebook that the agent uses to select its intended action and some additional constraints that define when an agent has precedence (or not) to take its intended action. If all of the agents act according to this contract, we can guarantee safety under all traffic conditions and liveness for all agents under “sparse” traffic conditions. The formalism of the contract also enables assignment of blame. We provide proofs of correctness of the behavioral contract and validate our results in simulation.

Conflict Resolution as a Combinatorial Optimization Problem

Within the framework of the mathematical theory of conflicts, we consider a multi-criterial conflict situation using the example of a child–parent conflict. A general method for constructing a conflict diagram is described and possible ways of the conflicting parties influencing each other in this conflict situation are discussed. We introduce criteria that characterize the intensity of conflicts and show that the conflict resolution problem can be reduced to a multi-criterial combinatorial optimization problem. As a method for solving this problem, we consider the Monte Carlo method (Metropolis algorithm). The performed parametric calculations demonstrate the effectiveness of the proposed method.

Multi-vehicle Conflict Resolution in Highly Constrained Spaces by Merging Optimal Control and Reinforcement Learning

We present a novel method to address the problem of multi-vehicle conflict resolution in highly constrained spaces. An optimal control problem is formulated to incorporate nonlinear, non-holonomic vehicle dynamics and exact collision avoidance constraints. A solution to the problem can be obtained by first learning configuration strategies with reinforcement learning (RL) in a simplified discrete environment, and then using these strategies to generate new constraints and initial guesses for the original problem. Simulation results show that our method can explore efficient actions to resolve conflicts in confined space and generate dexterous maneuvers that are both collision-free and kinematically feasible.

Wali Nanggroe Institution (LWN) Revitalization and Conflict Resolution Problems in Aceh

Strengthening the peace in Aceh may be possible by revitalizing the Wali Nanggroe Institution (LWN). Institutional strengthening is necessary to keep Aceh on its peaceful path. However, even LWN's existence has generated debate and differing opinions. This article seeks to answer the following: First, why does LWN deserve to be preserved and why has its revitalization been fraught with difficulties? Second, what is the relationship between the problems LWN faces and the potential role that it plays in conflict resolution? This study employs qualitative research in the form of document studies and in-depth interviews via focus groups with a variety of informants with a focus on specific and privileged institutions. Although LWN has the potential to increase the dignity of the traditional or social side of the people of Aceh, the politicization of institutions, customary discrimination, and the poor implementation of functions has resulted in the emergence of distrust. Because of this low level of mistrust, the institution's potential to serve as a forum for conflict resolution is currently far from being realized.

Dual-Horizon Reciprocal Collision Avoidance for Aircraft and Unmanned Aerial Systems

The aircraft conflict detection and resolution problem has been addressed with a wide range of centralised methods in the past few decades, e.g. constraint programming, mathematical programming or metaheuristics. In the context of autonomous, decentralized collision avoidance without explicit coordination, geometric methods provide an elegant, cost-effective approach to avoid collisions between mobile agents, provided they all share a same logic and a same view of the traffic. The Optimal Reciprocal Collision Avoidance (ORCA) algorithm is a state-of-the art geometric method for robot collision avoidance, which can be used as a Detect & Avoid logic on-board aircraft or Unmanned Aerial Vehicles. However, ORCA does not handle well some degenerate situations where agents operate at constant or near-constant speeds, which is a widespread feature of commercial aircraft or fixed-winged Unmanned Airborne Systems. In such degenerate situations, pairs of aircraft could end up flying parallel tracks without ever crossing paths to reach their respective destination. The Constant Speed ORCA (CS-ORCA) was proposed in 2018 to better handle these situations. In this paper, we discuss the limitations of both ORCA and CS-ORCA, and introduce the Dual-Horizon ORCA (DH-ORCA) algorithm, where two time horizons are used respectively for short-term collision avoidance and medium-term path-crossing. We show that this new approach mitigates the main issues of ORCA and CS-ORCA and yields better performances with dense traffic scenarios.

Implementation Of The Concept Of Sustainable Development In Resolving Indigenous Law Community Conflict

The purpose of this study is to explain the concept of sustainable development that needs to be implemented in resolving conflicts between indigenous peoples and how the government is making concrete efforts to realize the concept of sustainable development. This study uses a socio-legal approach because it uses non-legal elements in its study. The results of the study show that the values ??contained in the concept of sustainable development can be a means of resolving land conflicts of customary law communities. Considering the concept of sustainable development is also in line with the concept of Planning for Sustainable Use of Land Resources. The implementation of the concept of sustainable development in solving problems of conflict resolution of customary law communities is carried out through achieving goals with political will, consistent government customary law and companies, responsiveness of stakeholders including local governments to land conflicts, and the establishment of a sustainable maintenance system.

Online cooperative airspace conflict resolution of unmanned aerial vehicles by space mapping based iterative search method

This paper presents an investigation into the locally centralized conflict resolution problem of unmanned aerial vehicles (UAVs), in which the headings are adjustable variables. Firstly, the geometric characteristics of the conflict resolution problem in two-dimensional space are studied, and the problem is reduced into a nonlinear programing model. Secondly, the nonlinear safe separation constraint is transferred into linear style in the sine value space using the space mapping method. The feasible region of two conflict-related UAVs is transformed into a half-space in the sine value space. The minimum adjustment vector and the maximum adjustable vector are obtained in this space. Thirdly, to search for the local optimal conflict-free solution, a two-layered optimization method is given. In the first layer, feasible conflict-free solutions are obtained by iterative search in virtue of the minimum adjustment vector and the maximum adjustable vector. In the second layer, the local optimal solution is generated using the sequential quadratic programing (SQP) method. The effectiveness of the proposed method is verified by numerical simulations. Compared with the existing geometric guidance theory based methods, the proposed method could effectively generate conflict-free solutions that lead to fewer detours, through which UAVs could coordinate online in highly crowded airspace.

Robust aircraft conflict resolution under trajectory prediction uncertainty

In air traffic control, aircraft velocity may be perturbed due to weather effects or measurements errors and affect trajectory prediction. We address the aircraft conflict resolution problem in the presence of such perturbations. We consider polyhedral uncertainty sets on aircraft velocity, develop a budgeted robust optimization approach and show that it is amenable to mixed-integer optimization. Numerical experiments reveal that perturbations of the order of 5% on aircraft velocities can be accounted for without significantly impacting performance.

Review of Deep Reinforcement Learning Approaches for Conflict Resolution in Air Traffic Control

Deep reinforcement learning (DRL) has been widely adopted recently for its ability to solve decision-making problems that were previously out of reach due to a combination of nonlinear and high dimensionality. In the last few years, it has spread in the field of air traffic control (ATC), particularly in conflict resolution. In this work, we conduct a detailed review of existing DRL applications for conflict resolution problems. This survey offered a comprehensive review based on segments as (1) fundamentals of conflict resolution, (2) development of DRL, and (3) various applications of DRL in conflict resolution classified according to environment, model, algorithm, and evaluating indicator. Finally, an open discussion is provided that potentially raises a range of future research directions in conflict resolution using DRL. The objective of this review is to present a guidance point for future research in a more meaningful direction.

SCHOOL MEDIATION AS A TECHNOLOGY OF CONFLICT RESOLUTION IN SECONDARY EDUCATION INSTITUTIONS

In the article, the author analyzes the problem of conflict resolution in educational institutions. In particular, the author provides an analysis and description of one of the technologies of socio-pedagogical work in educational institutions – mediation. The article analyzes the features of mediation technology as a certain approach to conflict resolution in the conditions of an educational institution among teenagers. The author of the article focused his attention on the characteristics of existing classifications of stages and stages of this technology. So, in particular, the author supports the opinion of N. Bilyk regarding the structure of the mediation process by dividing it into stages: the first stage is the introductory (preparatory) stage; the second stage – stories of opponents; the third stage is the clarification of the causes of the conflict situation, of all participants; the fourth stage, direct decision-making; the fifth stage, direct signing of the agreement, which clearly states how, when and by whom the decisions will be implemented. An important element in the work is the analysis of the algorithm of the socio-pedagogical technology of mediation in resolving conflicts between teenagers in educational institutions (diagnosis, making appropriate decisions, choosing specific technologies, setting goals and objectives, implementing technology, evaluating solutions to the problem). Valuable among the analyzed are the conclusions about the benefits of mediation in the prevention of conflicts among teenagers, since conflicts arise not only in school, but also outside it, in particular, in out-of-school education institutions.

Conflict Resolution Model of Automated Vehicles Based on Multi-Vehicle Cooperative Optimization at Intersections

The traditional conflict resolution algorithm is designed for automated vehicles based on the premise of the right of way, but there is an unclear road right of way at unsignalized intersections, which brings trouble to the decision making of automated vehicles. The objective of this work is to provide our system with evolving cooperative and non-cooperative decisions. We achieve this by integrating game theory into the decision making. When the system decides to drive cooperatively, the joint action is planned to optimize the overall revenue of multi-vehicles based on the cooperative game, considering the conflict relationship with its neighboring vehicles. When the system fails to perform cooperative driving or response timeout, the vehicle-mounted unit would take non-cooperative driving to optimize the trajectory considering only its individual benefit. The proposed model can provide our system with stability and robustness, which effectively solved the conflict resolution problem when the right of way was not clear at intersections. We have implemented some simulation experiments of cooperative and non-cooperative conflict resolution, the results show that the revenue among various interest groups is more balanced with a cooperative conflict resolution method. Compared with the non-collaborative driving decision, the conflict resolution time is shortened, and the average delay of each vehicle at intersections is reduced by 1~2 s, with an average reduction of approximately 5%. The research can provide a reference for collaborative driving of automated vehicles at unsignalized intersections.

The predictive effect of self-compassion on relationship satisfaction and conflict resolution styles in romantic relationships in nursing students.

People who are compassionate to both themselves and others want to alleviate their experiences of pain and those of others. This study aims to evaluate the predictive effect of self-compassion on relationship satisfaction and conflict resolution styles in romantic relationships in nursing students. The sample of this study consists of 356 nursing students. The data are collected by using the Self-Compassion Scale, the Conflict Resolution Styles Scale in Romantic Relationships, and the Relationship Satisfaction Scale. A linear regression model is used to test the predictive effect of self-compassion on relationship satisfaction and conflict resolution styles in nursing students. Mean age of the participant is 20.70 ± 1.90, 68.5% (n = 244) of the nursing students are female, and 33.2% of them (n = 211) are male. Self-compassion is found to be a significant predictor of both relationship satisfaction and conflict resolution styles (positive conflict resolution, negative conflict resolution, and retreat) in romantic relationships (p < .05). Self-compassion is an important factor in relationship satisfaction and conflict resolution. These results of the study can be used to address conflict resolution problems in romantic relationships and further research.

Toward a Normative Turn in Track Two Diplomacy? A Review of the Literature

Almost six decades since the emergence of Track Two diplomacy, a form of informal and unofficial dialogues between conflicting parties facilitated by scholar‐practitioners, scholarship on the field has grown exponentially. Originally conceived of as a discreet complement to Track One official negotiations between armed actors in conflict, Track Two has become an established and professionalized form of broader conflict resolution. Specific scholarship on Track Two has occupied the liminal space between theory and practice, in which theoretical propositions are often born out of observations from practical applications in empirical cases. Analyzing how Track Two theories reflect these paradigms can give greater insights into the theoretical evolution of the field and where it is headed. This begets a need to regularly and systematically survey the relevant literature on Track Two given the large‐scale changes occurring in its practice. This article discusses the evolution of Track Two theories as embedded in conflict resolution paradigms, namely, strategic bargaining and problem solving; conflict transformation; and liberal and post‐liberal peacebuilding. In particular, I focus on how the recent "normative turn" in conflict resolution literature has impacted Track Two scholarship. Following the analytical framework of a mediation process applied to Track Two scholarship, I organize relevant literature into “generations” and compare what are identified as the main constitutive components of a Track Two initiative in order to understand how the theories have evolved in this second generation of literature. My analysis finds that conceptions of Track Two have moved from “narrow” to “broad”: influenced by a normative imperative toward more inclusive and participatory peace processes in conflict resolution, activities labeled as Track Two have expanded from small, discreet dialogues between unofficial actors to include more robust forms of civil society inclusion and participation.

The conflict resolution practice as a community justice alternative in Thailand

ABSTRACT In response to recent issues of prison overcrowding (over 300,000 inmates in prisons with only 110,000 capacity) and court case overloading, Thailand has implemented the conflict resolution practice, including enacting a new law on dispute mediation, as a community justice alternative to reduce cases and prevent them from going into the main criminal justice system. No empirical research has yet been conducted in Thailand to examine the current forms and status of these conflict resolution mechanisms and problems facing the implementation process. Researchers collected qualitative data by interviewing 16 experts and conducting a focus group with 23 experts. The study’s findings revealed five existing mechanisms on conflict resolution across the country, two of which were recently established thanks to the Dispute Mediation Act of 2019. While the study’s findings identified many issues facing the implementation of the conflict resolution process, the main ones were the lack of trained personnel, the lack of adequate data management, insufficient operating budget, and the absence of public awareness and public confidence.