Reasoning Knowledge-Gap in Drone Planning via LLM-based Active Elicitation
Reasoning Knowledge-Gap in Drone Planning via LLM-based Active Elicitation explores An LLM-powered drone planning system that actively elicits minimal information from human operators to improve task success and reduce interaction frequency.. Commercial viability score: 7/10 in Drone Planning.
BUILDER'S SANDBOX
Build This Paper
Use an AI coding agent to implement this research.
OpenAI CodexAI Agent
Lightweight coding agent in your terminal.
Claude CodeAI Agent
Agentic coding tool for terminal workflows.
AntiGravity IDEScaffolding
AI agent mindset installer and workflow scaffolder.
CursorIDE
AI-first code editor built on VS Code.
VS CodeIDE
Free, open-source editor by Microsoft.
Recommended Stack
Startup Essentials
Estimated $9K - $13K over 6-10 weeks.
See exactly what it costs to build this -- with 3 comparable funded startups.
7-day free trial. Cancel anytime.
Discover the researchers behind this paper and find similar experts.
7-day free trial. Cancel anytime.
Related Papers
- Builds On ThisSAGE-LLM: Towards Safe and Generalizable LLM Controller with Fuzzy-CBF Verification and Graph-Structured Knowledge Retrieval for UAV DecisionViability: 6.0
- Builds On ThisRequesting Expert Reasoning: Augmenting LLM Agents with Learned Collaborative InterventionViability: 5.0
- Builds On ThisExploring Plan Space through Conversation: An Agentic Framework for LLM-Mediated Explanations in PlanningViability: 3.0
- Prior WorkMINT: Minimal Information Neuro-Symbolic Tree for Objective-Driven Knowledge-Gap Reasoning and Active ElicitationViability: 7.0