Optimizing Dynamic Resource Allocation in Teamwork

摘要

The proposed research was designed to extend prior AFOSR sponsored research (DeShon, Kozlowski et al., 2004) to model optimal human resource allocation to account for learning, performance, and adaptation for complex and dynamic tasks incorporating individual and team goals. Phase 1 was intended to implement an optimal, multiple-criterion (individual and team goals) reinforcement learning model that would compare human performance to optimal model performance. Phase 2 was intended to extend the model to autonomous decision makers by incorporating 'reward' into the decision maker via satiation levels on individual and team goals, with learning and performance compared to the optimal model (Phase I) and human benchmarks. Phase 3 was intended to extend the model to encompass adaptation to changes in reward structure (development of an adaptive, model-based reinforcement learning approach that would be compared to standard reinforcement learning and human performance). Funding restrictions limited work to 50% of phase 1 effort (6 of 12 months at 50% of original budget). Funding ceased at 6 months. Initial project efforts were devoted to redesigning and redeveloping our individual-team resource allocation simulation to incorporate features necessary to implement the reinforcement learning model and to evaluate potential implementations of the Q- learning algorithm within the simulation. This report summarizes the intended research contribution and progress up to funding termination.