Integration of Game and Multi-Agent Theory on Intelligent Recommendation System of Organic Vegetables Planting

• Main Authors: Yi-Chi Lee, 李宜錡
• Other Authors: Chih-Yao Lo
• Format: Others
• Language: zh-TW
• Published: 2008
• Online Access: http://ndltd.ncl.edu.tw/handle/90514166662004915888

碩士 === 育達商業技術學院 === 資訊管理所 === 96 === Because the people are fastidious more and more regarding the diet healthy demand, therefore has accomplished the organic agriculture starting. But the majority farmers when are engaged in the organic planter, does the regular session faced with - what need to plant to a question to be only then good. Therefore, how effectiveness, and chooses the appropriate planter crops precisely, is direction which is worth discussing. In addition, in all appropriate planter's crops, considered that its relative economic value, takes in various seasons should basis of the priority selection, achieves goal of the entire year planter most greatly economic profit, is also another ponder topic. Taking organic vegetables farming as an example, this research uses knowledge-based and rule-based methods, while applying the game theory and multi-agent theory, this study develops a set of graphic intellectual suggestion mechanism with ASP.NET and MS-SQL. In the first stage of the study, we apply the knowledge base and the rule base composed for this study, we filter the suitable crops for each season, and order the list of crops in the order of suitability before we propose the planting suggestion for the entire year. Next, we design a realistic game theory and multi-agent theory to operate a negotiation process for a more effective system, which considers the organic plantations’ affect between each crop and the limitation of the system, as well as the crop shifting cost. In the end, we construct a multi-agent game theory of negotiation in order to analyze the maximum profit and propose a one year with a maximum profit. In this system, a merge of game theory and multi-agent system has been tested and verified to give suggestions that are 84.25% as effective, compared to the suggestions provided by human professionals. Other than this, the system’s greatest contribution is that, the mechanism may act as a front system of e-learning application, thus increase the level of the organic farming techniques. This research because of knowledge library and pattern union breaks original carries on the appraisal, the decision scheme recommendation system model purely. Besides domain knowledge knowledge library and model let the user in face several possibilities in the choices, provides is more objective a more effective suggestion. In the furture, it is expected to be applied to other crops planting suggestion.