Enabling Integration and Interaction for Decentralized Artificial Intelligence in Airline Disruption Management

• URL: http://arxiv.org/abs/2104.03349v1
• Date: Wed, 7 Apr 2021 18:59:02 GMT
• Title: Enabling Integration and Interaction for Decentralized Artificial Intelligence in Airline Disruption Management
• Abstract summary: Airline disruption management traditionally seeks to address three problem dimensions: aircraft scheduling, crew scheduling, and passenger scheduling, in that order. We provide a discussion and demonstration of an agnostic and systematic paradigm for enabling simultaneously-integrated recovery of all problem dimensions during airline disruption management.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Airline disruption management traditionally seeks to address three problem dimensions: aircraft scheduling, crew scheduling, and passenger scheduling, in that order. However, current efforts have, at most, only addressed the first two problem dimensions concurrently and do not account for the propagative effects that uncertain scheduling outcomes in one dimension can have on another dimension. In addition, existing approaches for airline disruption management include human specialists who decide on necessary corrective actions for airline schedule disruptions on the day of operation. However, human specialists are limited in their ability to process copious amounts of information imperative for making robust decisions that simultaneously address all problem dimensions during disruption management. Therefore, there is a need to augment the decision-making capabilities of a human specialist with quantitative and qualitative tools that can rationalize complex interactions amongst all dimensions in airline disruption management, and provide objective insights to the specialists in the airline operations control center. To that effect, we provide a discussion and demonstration of an agnostic and systematic paradigm for enabling expeditious simultaneously-integrated recovery of all problem dimensions during airline disruption management, through an intelligent multi-agent system that employs principles from artificial intelligence and distributed ledger technology.

Related papers

• Toward a Decision-Assurance Layer for AI-Assisted Flight Planning in Air Traffic Management [37.2360395854375]
  This paper proposes the AI Trust and Assurance Layer (ATAL), a model-agnostic decision assurance architecture.<n>ATAL evaluates whether AI-generated flight-planning outputs are sufficiently reliable for operational use.<n>Although demonstrated in aviation, the framework is also transferable to other safety-critical decision-support domains.
  arXiv  Detail & Related papers  (2026-09-11T21:32:05Z)
• ComBodied Agents: a New Paradigm of Human-Centric Agentic AI [121.60938707072069]
  We introduce Combodied Agents, a human-centered paradigm that perceives, models, predicts, and supports individual human-state trajectories over time.<n>We unify fragmented capabilities across personal assistants, health agents, AI companions, and adaptive human--AI systems into a closed loop.
  arXiv  Detail & Related papers  (2026-08-11T13:35:58Z)
• Communication-Efficient Digital-Twin Coordination for Heterogeneous LLM Embodied Agents over Computing Power Networks [48.90302419915548]
  Embodied agent teams powered by heterogeneous large language models (LLMs) are being widely deployed in smart factories, warehouses, and service robotics.<n>We propose LDT-Coord, a networked coordination framework built upon a lightweight digital twin (DT)<n>Specifically, each agent independently selects its intended action and reports both the action decision and a structured temporal constraint over shared resources to the DT server.
  arXiv  Detail & Related papers  (2026-07-10T12:10:31Z)
• Occluding the Solution Space: Planner-Agnostic Adversarial Attacks on Tolerance-Aware Manipulation [57.9825059129472]
  We propose a planner-agnostic attack framework for tolerance-aware manipulation.<n>Our approach shifts the evaluation paradigm to task-level feasibility over goal regions.<n>Online, we formulate the attack as a budgeted maximum-coverage optimization.
  arXiv  Detail & Related papers  (2026-07-04T08:14:44Z)
• Solving the Aircraft Disassembly Scheduling Problem [2.2817442144155207]
  An efficient scheduling of the disassembly procedure is crucial to ensure the profitability of the process and practice.<n>This article presents the problem in details and proposes two approaches to solve the problem: a Constraint Programming model and a MIP model.
  arXiv  Detail & Related papers  (2026-05-22T13:01:16Z)
• AstroReason-Bench: Evaluating Unified Agentic Planning across Heterogeneous Space Planning Problems [71.89040853616602]
  We introduce AstroReason-Bench, a benchmark for evaluating agentic planning in Space Planning Problems (SPP)<n>AstroReason-Bench integrates multiple scheduling regimes, including ground station communication and agile Earth observation, and provides a unified agent-oriented interaction protocol.<n>We find that current agents substantially underperform specialized solvers, highlighting key limitations of generalist planning under realistic constraints.
  arXiv  Detail & Related papers  (2026-01-16T15:02:41Z)
• Disaster Management in the Era of Agentic AI Systems: A Vision for Collective Human-Machine Intelligence for Augmented Resilience [13.155469498169866]
  Disaster Copilot is a vision for a multi-agent artificial intelligence system designed to overcome these systemic challenges.<n>The proposed architecture utilizes a central orchestrator to coordinate diverse sub-agents, each specializing in critical domains such as predictive risk analytics, situational awareness, and impact assessment.
  arXiv  Detail & Related papers  (2025-10-16T02:02:10Z)
• Higher Satisfaction, Lower Cost: A Technical Report on How LLMs Revolutionize Meituan's Intelligent Interaction Systems [67.18731675163589]
  We introduce WOWService, an intelligent interaction system tailored for industrial applications.<n>With the integration of LLMs and multi-agent architectures, WOWService enables autonomous task management and collaborative problem-solving.<n> WOWService is deployed on the Meituan App, achieving significant gains in key metrics.
  arXiv  Detail & Related papers  (2025-10-15T08:35:51Z)
• OmniEVA: Embodied Versatile Planner via Task-Adaptive 3D-Grounded and Embodiment-aware Reasoning [50.45036742963495]
  We introduce OmniEVA, an embodied versatile planner that enables advanced embodied reasoning and task planning.<n>A Task-Adaptive 3D Grounding mechanism enables context-aware 3D grounding for diverse embodied tasks.<n>An Embodiment-Aware Reasoning framework incorporates task goals and embodiment constraints into the reasoning loop, resulting in planning decisions that are both goal-directed and executable.
  arXiv  Detail & Related papers  (2025-09-11T10:32:22Z)
• Robot-Gated Interactive Imitation Learning with Adaptive Intervention Mechanism [48.41735416075536]
  Interactive Imitation Learning (IIL) allows agents to acquire desired behaviors through human interventions.<n>We propose the Adaptive Intervention Mechanism (AIM), a novel robot-gated IIL algorithm that learns an adaptive criterion for requesting human demonstrations.
  arXiv  Detail & Related papers  (2025-06-10T18:43:26Z)
• Constrained Human-AI Cooperation: An Inclusive Embodied Social Intelligence Challenge [47.74313897705183]
  CHAIC is an inclusive embodied social intelligence challenge designed to test social perception and cooperation in embodied agents. In CHAIC, the goal is for an embodied agent equipped with egocentric observations to assist a human who may be operating under physical constraints. We benchmark planning- and learning-based baselines on the challenge and introduce a new method that leverages large language models and behavior modeling.
  arXiv  Detail & Related papers  (2024-11-04T04:41:12Z)
• Agent-Oriented Planning in Multi-Agent Systems [54.429028104022066]
  We propose a novel framework for agent-oriented planning in multi-agent systems, leveraging a fast task decomposition and allocation process. We integrate a feedback loop into the proposed framework to further enhance the effectiveness and robustness of such a problem-solving process.
  arXiv  Detail & Related papers  (2024-10-03T04:07:51Z)
• Interactive Speculative Planning: Enhance Agent Efficiency through Co-design of System and User Interface [38.76937539085164]
  This paper presents a human-centered efficient agent planning method -- Interactive Speculative Planning. We aim at enhancing the efficiency of agent planning through both system design and human-AI interaction.
  arXiv  Detail & Related papers  (2024-09-30T16:52:51Z)
• Tradeoffs When Considering Deep Reinforcement Learning for Contingency Management in Advanced Air Mobility [0.0]
  Air transportation is undergoing a rapid evolution globally with the introduction of Advanced Air Mobility (AAM) Increased levels of automation are likely necessary to achieve operational safety and efficiency goals. This paper explores the use of Deep Reinforcement Learning (DRL) which has shown promising performance in complex and high-dimensional environments.
  arXiv  Detail & Related papers  (2024-06-28T19:09:55Z)
• QuadrupedGPT: Towards a Versatile Quadruped Agent in Open-ended Worlds [51.05639500325598]
  We introduce QuadrupedGPT, designed to follow diverse commands with agility comparable to that of a pet. Our agent shows proficiency in handling diverse tasks and intricate instructions, representing a significant step toward the development of versatile quadruped agents.
  arXiv  Detail & Related papers  (2024-06-24T12:14:24Z)
• Online Decision Mediation [72.80902932543474]
  Consider learning a decision support assistant to serve as an intermediary between (oracle) expert behavior and (imperfect) human behavior. In clinical diagnosis, fully-autonomous machine behavior is often beyond ethical affordances.
  arXiv  Detail & Related papers  (2023-10-28T05:59:43Z)
• An Emergency Disposal Decision-making Method with Human--Machine Collaboration [20.84526084548098]
  In modern operational environments, all job plans are affected by emergency events such as equipment failures and resource shortages. The use of unmanned systems to assist decision-making can improve resolution efficiency, but their decision-making is not interpretable. We propose a collaborative human--machine method for resolving unplanned events using two phases: task filtering and task scheduling.
  arXiv  Detail & Related papers  (2023-05-29T15:53:20Z)
• Innovations in the field of on-board scheduling technologies [64.41511459132334]
  This paper proposes an onboard scheduler, that integrates inside an onboard software framework for mission autonomy. The scheduler is based on linear integer programming and relies on the use of a branch-and-cut solver. The technology has been tested on an Earth Observation scenario, comparing its performance against the state-of-the-art scheduling technology.
  arXiv  Detail & Related papers  (2022-05-04T12:00:49Z)
• Uncertainty Quantification and Propagation for Airline Disruption Management [0.0]
  Disruption management during the airline scheduling process can be compartmentalized into proactive and reactive processes. This paper introduces an uncertainty transfer function model (UTFM) framework that characterizes uncertainty for proactive airline disruption management.
  arXiv  Detail & Related papers  (2021-02-09T21:57:04Z)
• A game-theoretic analysis of networked system control for common-pool resource management using multi-agent reinforcement learning [54.55119659523629]
  Multi-agent reinforcement learning has recently shown great promise as an approach to networked system control. Common-pool resources include arable land, fresh water, wetlands, wildlife, fish stock, forests and the atmosphere.
  arXiv  Detail & Related papers  (2020-10-15T14:12:26Z)
• Differentially Private Multi-Agent Planning for Logistic-like Problems [70.3758644421664]
  This paper proposes a novel strong privacy-preserving planning approach for logistic-like problems. Two challenges are addressed: 1) simultaneously achieving strong privacy, completeness and efficiency, and 2) addressing communication constraints. To the best of our knowledge, this paper is the first to apply differential privacy to the field of multi-agent planning.
  arXiv  Detail & Related papers  (2020-08-16T03:43:09Z)
• On Algorithmic Decision Procedures in Emergency Response Systems in Smart and Connected Communities [21.22596396400625]
  Emergency Response Management (ERM) is a critical problem faced by communities across the globe. We argue that the crucial period of planning for ERM systems is not post-incident, but between incidents. We propose two partially decentralized multi-agent planning algorithms that utilizes and exploit the structure of the dispatch problem.
  arXiv  Detail & Related papers  (2020-01-21T07:04:38Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.