Optimization-Based Agent Simulations for Evaluating the SPEYES System
Abstract
This paper presents an optimization-based agent-driven Distributed Dynamic Decision-making (DDD) simulation model to evaluate the SPEYES (Security and Patrolling Enablers Yielding Effective SASO - Support and Stability Operations) system. The key challenge is to quantify the force multiplying effect of SPEYES technologies, which span sensing, situation awareness/command and control (SA/C2), and shaping components. The performance improvements were measured in terms of timeliness, effectiveness, and efficiency of operations. The behaviors of optimization-based agents were first calibrated to those of human-in-the-loop simulations. The agent-driven simulation results indicated that integration of SPEYES sensing, SA/C2, and shaping technologies provided significant performance improvements to the force across all measures. Even at 50%-reduced force, the SPEYES system maintained significant performance improvements over regular operations with a full force and without SPEYES, thus confirming the force multiplier effect of SPEYES technologies. The findings are confirmed by human-in-the-loop simulations.
Document Details
- Document Type
- Technical Report
- Publication Date
- Jun 01, 2005
- Accession Number
- ADA464130
Entities
People
- Candra Meirina
- David Lee Kleinman
- Feili Yu
- Georgiy M. Levchuk
- Krishna R. Pattipati
- Robert L. Popp
- Sui Ruan
Organizations
- University of Connecticut