行動代理者規劃逐漸被視為資訊擷取系統中重要的技術之一，可在行動計算環境中提供最小成本的位置感知之服務。雖然霍普菲爾-坦克類神經網路被提出於解決旅行推銷員問題，在研究文獻中對於行動代理者成本最佳化之探討卻甚少考慮有效資源的時間限制。因此，我們假設霍普菲爾-坦克類神經網路可以被利用來解決行動代理者規劃問題。為了驗證此構想，我們改良霍普菲爾-坦克類神經網路和設計一個新的能量函數，不但處理計算環境之動態時間特色，尤其是在安排行動代理者行程時之伺服器效能和網路延遲；而且也滿足基於位置之限制，如旅行行動代理者行程之起訖點必為其本地端網站。更轉換能量函數為李亞普諾夫函數形式以確保收斂至穩定狀態和合法解的存在，設計神經元間之連結加權值計算方式和在動態網路中狀態變數之活化函數以搜尋合法解。此外，並推導目的函數以計算合法解之完成時間和預測最佳行程路徑。模擬並實驗在不同因素，如時間變數和能量函數參數值，以評估提出之模型和演算法。實驗結果顯示，本研究提出的模型和演算法具有快速的計算能力，對於基於位置和時間限制之分散式行動代理者問題，其迅速產生的最佳解可相當接近最小值。本創新之方法所提供的時空整合技術與知識，可望有效改善解決最佳化問題之效率。

Mobile agent planning (MAP) is increasingly viewed as an important technique of information retrieval systems to provide location aware services of minimum cost in mobile computing environment. Although Hopfield-Tank neural network has been proposed for solving the traveling salesperson problem, little attention has been paid to the time constraints on resource validity for optimizing the cost of the mobile agent. Consequently, we hypothesized that Hopfield-Tank neural network can be used to solve the MAP problem. To test this hypothesis, we modify Hopfield-Tank neural network and design a new energy function to not only cope with the dynamic temporal features of the computing environment, in particular the server performance and network latency when scheduling mobile agents, but also satisfy the location-based constraints such as the starting and end node of the routing sequence must be the home site of the traveling mobile agent. In addition, the energy function is reformulated into a Lyapunov function to guarantee the convergent stable state and existence of the valid solution. The connection weights between the neurons and the activation function of state variables in the dynamic network are devised in searching for the valid solutions. Moreover, the objective function is derived to estimate the completion time of the valid solutions and predict the optimal routing path. Simulations study was conducted to evaluate the proposed model and algorithm for different time variables and various coefficient values of the energy function. The experimental results quantitatively demonstrate the computational power and speed of the proposed model by producing solutions that are very close to the minimum costs of the location-based and time-constrained distributed MAP problem rapidly. The spatio-temporal technique proposed in this work is an innovative approach in providing knowledge applicable to improving the effectiveness of solving optimization problems.

CHAPTER 1 INTRODUCTION 1
CHAPTER 2 LITERATURE REVIEW 5
2.1 THE MOBILE AGENT 6
2.2 THE MAP PROBLEM 8
2.3 ARTIFICIAL NEURAL NETWORK 11
2.4 HOPFIELD NEURAL NETWORK 14
2.5 HOPFIELD-TANK NEURAL NETWORK 17
CHAPTER 3 THE MOBILE AGENT PLANNING PROBLEM 22
CHAPTER 4 THE MOBILE AGENT PLANNING MODEL 28
4.1 THE STATE VARIABLES 29
4.2 THE CONSTRAINTS AND PROBLEM GOAL 31
4.3 THE MAP ENERGY FUNCTION 40
4.4 THE CONNECTION WEIGHT MATRICES 46
4.5 THE ACTIVATION FUNCTION 48
4.6 THE MAP ALGORITHM 49
CHAPTER 5 SIMULATION 50
5.1 SIMULATION DESIGN 50
5.2 SIMULATION RESULTS 53
CHAPTER 6 CONCLUSION 63
REFERENCES 65
APPENDIX 67

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