Study on Bidding Strategies using Genetic Network Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.8120

@PhdThesis{ChuanYue:thesis,

• author = "Chuan Yue",
• title = "Study on Bidding Strategies using Genetic Network Programming",
• school = "Waseda University",
• year = "2012",
• address = "Japan",
• month = jul,
• keywords = "genetic algorithms, genetic programming, Genetic Network Programming, GNP",
• URL = "http://hdl.handle.net/2065/40075",
• URL = "http://jairo.nii.ac.jp/0069/00023944/en",
• URL = "http://dspace.wul.waseda.ac.jp/dspace/bitstream/2065/40075/1/Honbun-6057.pdf",
• URL = "http://dspace.wul.waseda.ac.jp/dspace/bitstream/2065/40075/2/Shinsa-6057.pdf",
• URL = "http://dspace.wul.waseda.ac.jp/dspace/bitstream/2065/40075/3/Gaiyo-6057.pdf",
• size = "140 pages",
• abstract = "Due to the explosive development of global network structure, electronic commerce is increasingly playing an important role in many organisations and individual consumer's daily life. It offers opportunities to significantly improve the way for businesses interactions between both customers and suppliers. More and more large scale and decentralised e-commerce mechanisms have emerged in industrial and commercial domains in a wide range.

  In particular, among all these applications, online auctions, which are flexible pricing mechanisms over Internet, make the physical limitations of traditional auctions disappear. They gain their extra popularity in the daily life and attract globally dispersed users due to having the characteristics that bargaining and negotiation besides all of the convenience. Thus, on line auctions become one of the most widely studied and employed negotiation mechanisms today. Traditionally, in most current online auction applications, the traders are generally humans who operate all the behaviours to make transactions. These behaviours may involve observing the auctions, analysing the auction information, and bidding the suitable price for the items. However, facing the increasingly demanding requirements and complexity of online trading, this kind of manual operation does not reveal the full potential of this new mode of commerce. Thus, in order to relieve the users and be more effective, exploring possible types and automating the behaviours in the online auction attract high interest.

  Now, in many studies, the agent-oriented auction mechanism, with its emphasis on autonomous actions and flexible interactions, arises as an effective and robust model for the dynamic and sensitive commerce environment. In such systems, the agent acts flexibly on behalf of its owner and is capable of local decision-making based on the environment information and pre-knowledge about the system.

  Among many different types of online auction, two of the most popular and studied types are Multiple Round English Auctions (MREA), which is single side auction, and Continuous Double Auction (CDA), which is double side auction. These auctions are newly emerged in e-commerce era based on the traditional auction types. They allow multiple agents to participate and one agent can deal with several auctions continuously or simultaneously, which are effective auction types to save time and relieve the users. Towards to these types, because there is no centralised system-wide control, the major challenge for automatic bidding strategies is to improve the degree of automation and optimise the agent's bidding behaviour in order to maximise the owner's profit. Most of the related researches have been conducted by using heuristic methods and fixed mathematical functions to compute the final optimal bidding price for the items or to compute how much should bid at each time step. Nevertheless, because auction environments are complicated and highly dynamic due to have many factors affecting each other, these approaches are not flexible enough for the dynamic environment, and there is no dominant strategy.",

• abstract = "Against this background, this thesis is concerned with developing the intelligence of autonomous agent's bidding strategy in order to make the agent to be more efficient and competitive for agent-based online auction mechanisms, especially in MREA and CDA. In order to be more flexible and better exploit the market information, Genetic Network Programming (GNP) is firstly employed to the agent's bidding strategy since its applicability and efficiency have been clarified in complex and dynamic problems in many other fields. GNP is one of the evolutionary optimization techniques developed as an extension of Genetic Algorithm (GA) and Genetic Programming (GP), which uses compact directed graph structures as solutions. Basically speaking, in the proposed method, the GNP population represents the group of potential bidding strategies, and each individual uses the as-if/then decision-making functions to judge the auction information and guides the agent to take the suitable actions under different situations. Thus, it could be flexible and capable to adaptive to various auction situations. During the evolution, the GNP structure will be systematically organized, and finally, the individual which can obtain the highest profit is selected as the optimal bidding strategy at the end of training phase.

  In chapter 2, we introduced the conception of MREA and CDA in detail, which are the study environments in this thesis. The related researches are also introduced.

  In chapter 3, focusing on MREA, the bidding strategy for the auction agents in MREA is proposed using GNP. The performance of GNP-based agents is evaluated and studied in two situations: MREA is no time limit (NTL), and MREA is time limit (TL). Furthermore, according to the amount of the money each agent has, each situation is divided into 2 cases: general case and poorest case. All the participating agents in the simulations use GNP strategy. This chapter aims to study and analyse the capability and effectiveness of GNP for guiding bidding actions through the phenomenon of the simulations. The simulation results reveal that the agents using GNP strategy can understand various environments well through experiences and become smarter through evolution.

  In chapter 4, as an extension of the bidding strategy in chapter 3, in order to improving the agent's intelligence and sensitivity, an enhanced bidding strategy for MREA is developed using GNP. Firstly, the GNP structure is modified to be able to judge more kinds of information and more situations at a time. Secondly, the strategy is improved to be able to consider the bidder's attitude towards to each good, which makes the strategy to be more personalised for each bidder and could make the bidder more satisfied with the auction result and profit. The proposed strategy is compared with the previous GNP strategy and the other conventional strategies in the simulations. The simulation results demonstrated that the proposed method can outperform the previous one and is more competitive than the agents based on mathematical functions.

  In chapter 5, focusing on CDA, GNP with rectify nodes (GNP-RN) has been applied for CDA bidding strategy combined with proposed heuristic rules, which are derived based on the common believes for assisting agent's bidding behaviour. GNP-RN is developed aiming to guide the agent to be competitive under different CDA environments, and maximise the agent's profit without losing chances for trading. Rectify Node (RN) is a newly proposed kind of nodes, which is used for bringing more flexible and various options for bidding action choices. 4 groups of simulations are designed to compare GNP-RN with conventional GNP and other strategies in CDA. In each simulation, the kinds of BibTeX entry too long. Truncated

Genetic Programming entries for Chuan Yue

Citations