Download or read book Hierarchical Behavior Planning in Distributed Decision Making Systems written by Lu Xu and published by . This book was released on 2006 with total page 117 pages. Available in PDF, EPUB and Kindle. Book excerpt: Abstract: Distributed decision making has become the predominate methodology of handling autonomous systems for our researchers. Behavior planning involves deciding at a certain time what each distributed decision making system should do and where it should go for the benefit of a certain objective. It deals with task coordination and resource allocation. In this dissertation, we focus on mathematically modelling and analysis the behavior planning in distributed decision making systems. We construct an distributed dynamic resource environment where each resource bears its individual properties. In the planning problem, there are two types of distributed decision making systems, Centers and Agents, which have different sensing, communication, and consuming capabilities. To make plans for these systems so that the total resource consumption is maximized, first we propose Instruction Planner, which applies the Lagrangian Relaxation method to relax the capacity constraints and decompose the planning problem. Meanwhile, we develop the "earliest expiration date" scheduling algorithm to make the results feasible so that no capacity constraints are violated in all the plans. Next in order to tack the coupling issue between scheduling and resource allocation more efficiently, we present Incentive Planner. In Incentive Planner, we functionally distribute the planning problem into three levels. In each level, we define its independent function and objective. Then Anticipation Theorem and Incentive Theorem are introduced and proved so that the decisions made in each level based on its local objective forms a Nash solution of the overall objective. In order to extend the successful application of the anticipation and incentives proposed in Incentive Planner, we then introduce these interrelation functions in the reference input distribution problem, which establishes an example for their future research in the hierarchical problems.