Multi-agent probabilistic search in a sequential decision-theoretic framework

Consider the task of searching a region for the presence or absence of a target using a team of multiple searchers. This paper formulates this search problem as a sequential probabilistic decision, which enables analysis and design of efficient and robust search control strategies. Imperfect detections of the target's possible locations are made by each search agent and shared with teammates. This information is used to update the evolving decision variable which represents the belief that the target is present in the region. The sequential decision-theoretic formulation presented in this paper provides an analytic framework to evaluate team search systems, as it includes a performance metric (time until decision), a measure of uncertainty (decision confidence thresholds) and imperfect information gathering (detection error). Strategies for cooperative search are evaluated in this context, and comparisons between homogeneous and hybrid search strategies are investigated in numerical studies.

Authors

• Timothy H. Chung
• Joel W. Burdick

Keywords

• USA Councils
• Search problems
• Robot kinematics
• Intelligent robots
• Operations research
• Time measurement
• Humans
• Termination of employment
• Robotics and automation
• Robust control
• Decision-theoretic Framework
• Probabilistic Search
• Search Strategy
• Target Location
• Error Detection
• Target Presentation
• Search For Agents
• Sequential Decision
• Imperfect Information
• Absence Of Target
• Search Problem
• Design Of Control Strategies
• Efficient Control Strategies
• Time Step
• Transition Probabilities
• Random Walk
• Conditional Independence
• False Alarm
• Visual Attention
• Discrete Data
• Search Region
• Positive Detection
• Search Task
• Multiple Agents
• Saccade
• Bayesian Filtering
• Target Probability
• Multiple Observations
• Search Optimization