Decentralized Classification with Assume-Guarantee Planning

We study the problem of decentralized classification conducted over a network of mobile sensors. We model the multiagent classification task as a hypothesis testing problem where each sensor has to almost surely find the true hypothesis from a finite set of candidate hypotheses. Each sensor makes noisy local observations and can also share information on their observations with other mobile sensors in communication range. In order to address the state-space explosion in the multiagent system, we propose a decentralized synthesis procedure that guarantees that each sensor will almost surely converge to the true hypothesis even in the presence of faulty or malicious agents. Additionally, we employ a contract-based synthesis approach that produces trajectories designed to empirically increase information-sharing between mobile sensors in order to converge faster to the true hypothesis. We implement and test the approach on experiments with both physical and simulated hardware to showcase the approach’s scalability and viability in real-world systems. Finally, we run a Gazebo/ROS simulated experiment with 12 agents to demonstrate the scalability of our approach in large environments with many agents.

Authors

• Steven Carr 0002
• Jesse Quattrociocchi
• Suda Bharadwaj
• Steven J. Spencer
• Anup Parikh
• Carol C. Young
• Stephen P. Buerger
• Bo Wu 0005
• Ufuk Topcu

Keywords

• Scalability
• Object detection
• Sensor systems
• Sensors
• Trajectory
• Planning
• Noise measurement
• Hypothesis Testing
• State Space
• Sensor Networks
• Multi-agent Systems
• Real-world Systems
• Almost Surely
• Communication Range
• Mobile Sensors
• True Hypothesis
• Hypothesis Testing Problem
• Sensors In Order
• Convergence Rate
• Cybersecurity
• Simulation Environment
• Faster Convergence
• Focus Of This Work
• Synthesis Reaction
• Physical Security
• Set Of Agents
• Set Of Propositions
• Strategies Of Agents
• Connectivity Constraints
• Sensor Observations
• Uncontrolled Environment