AI & Machine Learning
Intent-Driven UAM Rescheduling
Due to the restricted resources, efficient scheduling in vertiports has received much more attention in the field of Urban Air Mobility (UAM). For the scheduling problem, we utilize a Mixed Integer Linear Programming (MILP), which is often formulated in a resource-restricted project scheduling problem (RCPSP). In this paper, we show our approach to handle both dynamic operation requirements and vague rescheduling requests from humans. Particularly, we utilize a three-valued logic for interpreting ambiguous user intents and a decision tree, proposing a newly integrated system that combines Answer Set Programming (ASP) and MILP. This integrated framework optimizes schedules and supports human inputs transparently. With this system, we provide a robust structure for explainable, adaptive UAM scheduling.
Executive Impact
This research presents a novel approach to Urban Air Mobility (UAM) scheduling, enabling dynamic adjustments and human-AI collaboration.
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Scheduling Efficiency Increase
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Ambiguity Resolution Rate
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Operator Trust Score
Key Takeaways for Enterprise Integration:
UAM scheduling is formulated as an RCPSP.
An ASP-based intent recognition mechanism utilizes three-valued logic.
An explainable system integrates human intent and automated scheduling.
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Resource-Constrained Project Scheduling Problem (RCPSP)
RCPSP is a common scheduling problem where tasks must be completed using limited resources, often within a project deadline. It's often solved using Mixed Integer Linear Programming (MILP). The paper uses it as the foundational scheduling problem for UAM.
Relevance: Core to UAM task scheduling and resource allocation.
Answer Set Programming (ASP)
ASP is a declarative programming paradigm used for knowledge representation and reasoning. The paper leverages ASP for interpreting ambiguous user intents, handling constraints, and generating explanations for decisions.
Relevance: Enables intent recognition, ambiguity resolution, and system explainability (XAI).
Three-Valued Logic
A logical system that extends traditional binary logic (true/false) with a third truth value, 'unknown'. This is crucial for interpreting vague or ambiguous user inputs in the intent recognition mechanism.
Relevance: Key to handling uncertainty and ambiguity in human-operator requests.
Explainable AI (XAI)
XAI refers to methods and techniques that allow human users to understand the results of AI systems. The proposed system integrates ASP to provide transparency in its decision-making process, building user trust.
Relevance: Crucial for building trust and effective human-AI collaboration in dynamic UAM operations.
Enterprise Process Flow
Request for Rescheduling
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Categorize the Request
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Get Proper Logical rules
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Validity Check
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Get a Proper Decision Tree
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Interpret the Request (Recognizing Intent)
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Get the Proper Operation for the Intent
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Reschedule
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Update Current Schedule
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Interaction Step to Clarity (avg.)
The interactive intention extraction process (Algorithm 1) converges quickly, often resolving ambiguity in a single interaction step, leading to efficient user experience. This rapid clarification is crucial for real-time UAM operations.
| Option Category | Change Focus | Key Features |
|---|---|---|
| Full Rescheduling | All future tasks |
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| Partial Rescheduling | Affected tasks only |
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| Temporal Shift | Start time adjustments |
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| Resource Reallocation | Modify resource amounts |
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Rescheduling Task 4 Example
A user requested changing Task 4's start time to τ* = 4 (from baseline 5) and reducing Resource 1 usage to R4,1 = 1 (from baseline 2).
Challenge: The system needed to interpret the user's ambiguous request regarding global vs. local optimization and precedence links.
Solution: Utilizing the interactive intention extraction process with three-valued logic and decision trees, the system clarified the user's intent to allow global optimization and keep precedence links.
Result: After updating the resource requirements and fixing Task 4 at τ4 = 4 while maintaining local precedence, the system successfully generated a feasible, intent-aligned schedule with a new makespan of 21 (compared to 19 baseline).
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Estimated Annual Savings
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Hours Reclaimed Annually
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Implementation Roadmap
A phased approach to integrating intent-driven UAM rescheduling into your operations.
Phase 1: Intent Recognition & ASP Model Development
Develop the ASP module for interpreting ambiguous user intents using three-valued logic and decision trees. Integrate the interaction history mechanism.
Duration: 4-6 Weeks
Phase 2: MILP Scheduler Integration & Constraint Adjustment
Integrate the MILP-based RCPSP solver. Implement the logic for dynamic constraint adjustment based on clarified user intent (precedence and scope).
Duration: 6-8 Weeks
Phase 3: Explainability and User Interface Development
Build the front-end interface for interactive intent clarification and display of explanations (E). Conduct preliminary user testing for clarity and trust.
Duration: 5-7 Weeks
Phase 4: Robustness Testing & Dynamic Scenario Validation
Extensive testing across various dynamic UAM operational scenarios, focusing on resilience to unexpected events and complex human requests. Refine ambiguity resolution.
Duration: 7-9 Weeks
Phase 5: Performance Optimization & Scalability
Optimize the computational performance of both ASP and MILP components for real-time UAM environments. Address scalability for larger vertiport networks.
Duration: 6-8 Weeks
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