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Course Outline
Introduction to Multi-Agent Systems for Government
- Overview of agents, environments, and interaction models in the context of public sector operations
- Examination of cooperation, competition, and autonomy within agentic systems relevant to government applications
- Applications in logistics, robotics, and decision-making for government agencies
Core Concepts of Agent Architecture for Government
- Comparison of reactive versus deliberative agents in governmental contexts
- Communication protocols and coordination models suitable for public sector use
- Knowledge representation and shared state management within government systems
Implementing Agents in Python for Government
- Building agents using the Mesa framework to support government initiatives
- Modeling environments and interactions tailored to public sector needs
- Simulating agent behavior and visualizing results for government decision-making
Coordination and Communication in Government Systems
- Message passing and shared memory architectures for efficient government operations
- Negotiation, consensus-building, and task allocation strategies for public sector projects
- Coordination algorithms (contract net, market-based, swarm models) applicable to government workflows
Learning and Adaptation in Multi-Agent Systems for Government
- Reinforcement learning techniques for multiple agents in governmental contexts
- Analysis of cooperative versus competitive learning dynamics within public sector systems
- Utilizing PettingZoo and Stable-Baselines3 for multi-agent reinforcement learning (MARL) in government applications
Distributed Computing and Scaling for Government
- Using Ray for distributed multi-agent simulations to enhance public sector operations
- Managing concurrency and synchronization in government systems
- Parallelizing computation and handling shared resources within governmental frameworks
Human–Agent Collaboration for Government
- Designing user interfaces for human-in-the-loop coordination in government processes
- Developing hybrid workflows with AI-assisted decision support for public sector tasks
- Addressing ethical and operational considerations in human-agent collaboration for government
Capstone Project for Government
- Design and implement a multi-agent system in Python to address a specific government challenge
- Demonstrate coordination and learning among agents within a public sector context
- Present simulation results and performance insights relevant to government operations
Summary and Next Steps for Government
Requirements
- Strong proficiency in Python programming for government applications
- Comprehensive understanding of reinforcement learning and AI agent design
- Familiarity with distributed systems and networking concepts
Audience
- System architects tasked with designing collaborative or distributed AI systems for government use
- Researchers focusing on coordination and collective intelligence in public sector contexts
- Engineers developing hybrid human–agent or multi-agent workflows for government operations
28 Hours
