Design of a GIS-based Assistant Software Agent for the Incident Commander to Coordinate Emergency Response Operations
Problem: This paper addresses the design of an intelligent software system for the IC (incident commander) of a team in order to coordinate actions of agents (field units or robots) in the domain of emergency/crisis response operations. Objective: This paper proposes GICoordinator. It is a GIS-based assistant software agent that assists and collaborates with the human planner in strategic planning and macro tasks assignment for centralized multi-agent coordination. Method: Our approach to design GICoordinator was to: analyze the problem, design a complete data model, design an architecture of GICoordinator, specify required capabilities of human and system in coordination problem solving, specify development tools, and deploy. Result: The result was an architecture/design of GICoordinator that contains system requirements. Findings: GICoordinator efficiently integrates geoinformatics with artifice intelligent techniques in order to provide a spatial intelligent coordinator system for an IC to efficiently coordinate and control agents by making macro/strategic decisions. Results define a framework for future works to develop this system.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Agents That Teach: Towards Designing Incidental Learning Back into AI-Assisted Software Development
AI coding agents are rapidly reshaping how software is built, with developers increasingly delegating substantial coding tasks to autonomous agents in pursuit of higher productivity. While these gains are real, they come…
FireCommander: An Interactive, Probabilistic Multi-agent Environment for Heterogeneous Robot Teams
The purpose of this tutorial is to help individuals use the \underline{FireCommander} game environment for research applications. The FireCommander is an interactive, probabilistic joint perception-action reconnaissance …
Combinatorial Optimizationreinforcement-learningReinforcement Learning (RL)Learning Graph-Enhanced Commander-Executor for Multi-Agent Navigation
This paper investigates the multi-agent navigation problem, which requires multiple agents to reach the target goals in a limited time. Multi-agent reinforcement learning (MARL) has shown promising results for solving th…
Hierarchical Reinforcement LearningMulti-agent Reinforcement Learningreinforcement-learningReinforcement Learning (RL)"I'm Not Reading All of That": Understanding Software Engineers' Level of Cognitive Engagement with Agentic Coding Assistants
Over-reliance on AI systems can undermine users' critical thinking and promote complacency, a risk intensified by the emergence of agentic AI systems that operate with minimal human involvement. In software engineering, …
Exploring LLM-based Agents for Root Cause Analysis
The growing complexity of cloud based software systems has resulted in incident management becoming an integral part of the software development lifecycle. Root cause analysis (RCA), a critical part of the incident manag…
DiagnosticManagementRetrieval