An Application of Optimization Model to Multi-agent Conflict Resolution

Conflict is a natural and very typical phenomenon in every field of human world. It has very significant common characteristics and dynamics, and, therefore, it makes sense to examine them together and comparatively. People get involved in conflicts because their interests or their values are challenged or because their needs are not met [1]. Different kinds or classes of conflict require different conflict resolution strategies. According to their different natures people need different strategies to resolve different sorts of conflicts. Therefore, this paper describes how to represent each agent’s view and expand the ideas of mathematic techniques to Conflict Agents and gives a brief evaluation of each technique in its theory. Keyword: conflict resolution, DAI, agent.

Conflict is inevitable to take place in collaborative design, and conflict resolution plays an important part in complex product collaborative design systems. Firstly, a mathematic description model in collaborative design considering resource conflict, process conflict and design conflict is given, and the mathematic model expresses the origin of conflict and shows that conflict resolution is a multiobjective planning problem. Next, a conflict resolution algorithm considering design objective optimization is presented, and fuzzy value and fuzzy grade are applied in the algorithm. Depending on both considering design variables having fundamental influences on its design objectives and the cost of conflict resolution, the algorithm can improve the quality and efficiency of conflict resolution. Finally, an example of single cylindrical gear reducer is used to show the validity of the algorithm.

NASA researchers, working under the Aviation System Capacity Program and in conjunction with the FAA Free Flight Program Office, have developed a set of decision support tools to assist terminal area air traffic controllers with control of arrival and departure traffic. Two of these tools, the Final Approach Spacing Tool (FAST) and the Expedite Departure Path (EDP) tool, provide air traffic controllers with heading, speed and altitude advisories to assist in spacing aircraft. This paper describes the conflict prediction and resolution algorithm shared by aFAST and EDP to produce conflict-free aircraft trajectories with realistic conflict resolution maneuvers. The process is accomplished in three stages: prediction, classification and resolution. A conflict prediction scheme is documented which incorporates all applicable FAA separation requirements, including automatic detection of separation during transition from staggered to simultaneous parallel approach operations. A method of classifying predicted conflicts with a limited set of criteria is detailed. Finally, a knowledge-based conflict resolution process is presented which allows for resolution of predicted conflicts in a manner consistent with controller practice: including prioritization of resolution tactics and mixture of multiple degrees of freedom to achieve separation. The scheme has been employed in both closed-loop simulations to determine solution stability and controller-in-the-loop simulations to begin development of the resolution tactics knowledge base.

A method for conflict detection and resolution based on the parameterization of the aircraft intents is presented. The method is also based on the use of predefined trajectory patterns, which are in fact flight intents; these patterns include standard airline procedures and air traffic control regulations. The conflict resolution problem is formulated as a parametric optimization problem subject to constraints; the optimality criterium is defined so that the deviation from the intended (preferred) trajectories in the lateral profile is minimized. The resolution trajectory patterns take into account changes of the nominal waypoints (vectoring) and changes of the aircraft speeds. The method is applied to the case of multiple conflicts among commercial transport aircraft in converging traffic in the terminal area; different scenarios are considered, which include locked aircraft, that is, aircraft whose trajectories are known and fixed. The conflict detection and resolution algorithm relies on a kinetic trajectory predictor (nonlinear point-mass model with variable mass), which is accurate, flexible, and transparent, and provides the high-fidelity prediction required in the analysis of the demanding terminal area traffic. The cost of the global conflict resolution process is assessed, in terms of extra distance traveled, extra flight time, and extra fuel consumed for each aircraft.

Stochastic optimal control for aircraft conflict resolution under wind uncertainty

An airborne conflict resolution approach is presented that is capable of providing flight plans forecast to be conflict-free with both area and traffic hazards. This approach is capable of meeting constraints on the flight plan such as required times of arrival (RTA) at a fix. The conflict resolution algorithm is based upon a genetic algorithm, and can thus seek conflict-free flight plans meeting broader flight planning objectives such as minimum time, fuel or total cost. The method has been applied to conflicts occurring 6 to 25 minutes in the future in climb, cruise and descent phases of flight. The conflict resolution approach separates the detection, trajectory generation and flight rules function from the resolution algorithm. The method is capable of supporting pilot-constructed resolutions, cooperative and non-cooperative maneuvers, and also providing conflict resolution on trajectories forecast by an onboard FMC.

The pilot- and controller-in-the-loop simulation described in this paper demonstrates the potential benefits of a near-term trajectory-based operations concept with datalink communications. Near-term operations with datalink are characterized by a fleet of which not all aircraft are equipped with datalink communications (i.e., mixed equipage). The objectives of this simulation were to continue to develop capabilities such as the conflict resolution algorithm, as well as evaluate new capabilities and concepts such as tactical conflict detection and trajectory-based weather avoidance with feedback from experienced pilots and controllers. Conflict resolutions and tactical conflict detections were compared to controller actions. The direction of the conflict resolution maneuver advised by the algorithm agreed with controller actions 79% of the time when both chose to apply the same type of resolution maneuver to the same aircraft. Comparing controller actions to conflicts detected by the tactical conflict detection function revealed no missed alerts and a false alert rate of 11%. Trajectory-based weather avoidance functions received high ratings from the controllers for usefulness and ease of use. In particular, controllers noted weather avoidance reroutes originating from a simulated traffic management coordinator position alleviated their concerns about potentially disrupting traffic flow management plans, thus expediting the issuance of the reroutes.

- Efficient examination scheduling is critical for maintaining academic integrity and optimizing resources in educational institutions. This research focuses on developing an automated exam timetable generation system that dynamically resolves conflicts and accommodates diverse institutional constraints. By leveraging detailed algorithms for faculty invigilation, student schedules, room allocation, and conflict resolution, the system minimizes manual errors and improves operational efficiency. The proposed methodology includes real-time data collection, scheduling optimization, and robust invigilation assignment processes. Expected outcomes of this research include significant reductions in scheduling time, enhanced stakeholder satisfaction, and scalability to accommodate institutional growth. By addressing existing gaps in scheduling methods, this study offers a transformative approach to academic resource management, setting the foundation for advancements in educational technology. Key Words: Examination Scheduling, Timetable Generation, Conflict Resolution, Faculty Availability, Resource Optimization, Educational Technology, Automation, Scalability, Greedy approach, Sophisticated Algorithm

China has been gradually relaxing its ban on the use of low-altitude airspace across the country. To guarantee the high reliability of air traffic management (ATM), conflict detection and conflict resolution (CDR) approaches are indispensable to maintain safe separation between neighbouring small fixed-wing aircraft. In this study, we analyse a temporal and spatial integrated strategy for safety assessment purposes in opening the low-altitude urban airspace of Chinese pilot cities. First, we present a detailed mathematical description of the proposed algorithms based on a spatial grid partitioning system (SGPS). For our system, a conflict detection (CD) algorithm is designed to determine if two trajectories pass through the same grid space within overlapping time windows. A conflict resolution (CR) algorithm integrates a proposed time scheduling-based technique (TST) and vertical change-based technique (VCT), which operate under predetermined basic principles. Then, based on our novel CDR algorithms, a causal model is constructed in graphical modelling and analysis software (GMAS) to generate a state space that can provide a global perspective on scenario dynamics and better understanding of induced conflict occurrences. Finally, simulation results demonstrate that the proposed approach is practical and efficient.

In previous work we provided a conflict resolution algorithm allowing agents in stochastic domains (represented by Markov Decision Processes) to maximally satisfy a set of moral or social norms, where such norms are represented by statements in linear temporal logic (LTL). This required the agent designer to provide weights specifying the relative importance of each norm. In this paper, we propose an inverse norm conflict resolution'' algorithm for learning these weights from demonstration. This approach minimizes a cost function based on the relative entropy between a policy encoding the observed behavior and a policy representing optimal norm-following behavior. We demonstrate the effectiveness of the algorithm in a simple GridWorld domain.

Double patterning lithography (DPL) is seen as one of the most promising solutions for new technology nodes such as 32nm and 22nm. However, DPL faces the challenges of handling layout decomposition and overlay errors. Currently, most DPL solutions use post-layout decomposition which requires multiple iterations and designer intervention to achieve a decomposable layout as designs scale larger. Recent research is starting to consider DPL constraints during the layout design phase especially during the detailed routing phase. In this work, we propose DPL-aware grid-based detailed routing algorithm supported with online conflict resolution. The conflict resolution algorithm uses a graph structure to represent geometrical relations between routed polygons and helps in conflict detection and color assignment. Experimental results indicate that this enhanced algorithm reduces the number of conflicts by 60% on average.

In developing the Flight Director and Surveillance System (FDS) [1] for helicopters, a Helicopter Collision Avoidance System (H-CAS) is proposed a software support to assist helicopter pilots to avoid approach or collision in flight. An automatic traffic advisory and conflict resolution algorithm is formulated following its intrude detection. This paper presents the concept of collision avoidance for helicopters in low altitude. The algorithm considers the point mass nonlinear dynamics of helicopter as a three-point interior boundary problem, and solves this optimal control problem using Bang-Bang switching technique. As for H-CAS, the conflict resolution is generated to separate different priority helicopters. Theoretical analysis for the H-CAS is formulated, and simulations on conflicts and resolutions are demonstrated with feasible results.

This paper discusses the application of the prototyping approach to investigating the requirements on strategies for cooperative planning and conflict resolution of independent agents by means of an example application: the strategic game “Scotland Yard”. The strategies for coordinating the agents, which are parallel algorithms, are developed with a prototyping approach using ProSet-Linda. ProSet-Linda is designed for prototyping parallel algorithms.We concentrate on the techniques employed to elicit the requirements on the algorithms for agent interaction. The example application serves to illustrate the prototyping approach to requirements elicitation by means of a non-trivial instance for investigating algorithms for cooperative planning and conflict resolution.

Personal notes, even when shared with others, often contain highly sensitive information. From a security and privacy point of view, currently available (web) services that upload such personal notes to potentially untrusted third party servers are therefore problematic and we suggest to encrypt all notes before transferring them from the user's personal device. However, synchronization and sharing of encrypted data is a non-trivial issue, because conflict resolution and merging algorithms need to be applied to plain-text content. With Private Notes, we propose an architecture for client-side encryption, merge, and conflict handling of personal notes stored in XML format. We adopt the OpenPGP standard for symmetric and asymmetric encryption and Web-DAV for synchronizing and sharing notes on arbitrary web servers. Specific implementations in the form of a plug-in for the Tomboy desktop note taking application and the Android and iOS mobile platforms demonstrate the ease of use of encrypted notes sharing.

The Internet of Things (IoT) and cloud technologies have significantly facilitated healthcare. In such a context, medical data are collected by the terminals from the patients, manipulated, and stored on the cloud by hospitals (doctors). This brings asymmetry problems in medical data access control, processing, and storage between doctors and patients, which results in medical data sharing face many challenges such as privacy leakage and malicious feedback from cloud servers on queries. To solve these asymmetry problems, this paper proposes a medical data sharing scheme with cloud-chain cooperation and policy fusion in the IoT. Regarding asymmetrical access control rights, a conflict resolution and fusion algorithm that enables co-authorization of medical data by the doctor and the patient is introduced. To balance the symmetry of medical data storage and processing, a cloud-chain cooperation ciphertext retrieval method is proposed by means of two-stage joint searching from cloud servers and the blockchain, which can not only detect malicious medical data feedback from cloud servers, but also improve the data search efficiency. The security analysis showed that this scheme satisfies the confidentiality and verifiability of the retrieved information, and the feasibility of the proposed scheme was demonstrated through experiments.