Intelligent Trading Agents for Massively Multi-player Game Economies
نویسندگان
چکیده
As massively multi-player gaming environments become more detailed, developing agents to populate these virtual worlds as capable non-player characters poses an increasingly complex problem. Human players in many games must achieve their objectives through financial skills such as trading and supply chain management as well as through combat and diplomacy. In this paper, we examine the problem of creating intelligent trading agents for virtual markets. Using historical data from EVE Online, a science-fiction based MMORPG, we evaluate several strategies for buying, selling, and supply chain management. We demonstrate that using reinforcement learning to determine policies based on the market microstructure gives trading agents a competitive advantage in amassing wealth. Imbuing agents with the ability to adapt their trading policies can make them more resistant to exploitation by other traders and capable of participating in virtual economies on an equal footing with humans.
منابع مشابه
Multi Responses Optimization Through Game Theory Approach
In this paper, a new approach based on game theory has been proposed to multi responses problem optimization. Game theory is a useful tool for decision making in the conflict of interests between intelligent players in order to select the best joint strategy for them through selecting the best joint desirability. Present research uses the game theory approach via definition of each response as ...
متن کاملGoing Out of Business: Auction House Behavior in the Massively Multi-Player Online Game
The in-game economies of massively multi-player online games (MMOGs) are complex systems that have to be carefully designed and managed. This paper presents the results of an analysis of auction house data from the MMOG Glitch, across a 14 month time period, the entire lifetime of the game. The data comprise almost 3 million data points, over 20,000 unique players and more than 650 products. Fu...
متن کاملGoing out of business: Auction house behavior in the Massively Multi-player Online Game Glitch
The in-game economies of massively multi-player online games (MMOGs) are complex systems that have to be carefully designed and managed. This paper presents the results of an analysis of auction house data from the MMOG Glitch, across a 14 month time period. The data comprise almost 3 million data points, over 20,000 unique players and more than 650 products. Furthermore, an interactive visuali...
متن کاملTraffic modeling of player action categories in a MMORPG
In this paper we present a user action specific modeling of network traffic in a Massively Multiplayer Online RolePlaying Game (MMORPG). We have performed measurements for each of the previously defined action categories for MMORPGs (Trading, Questing, Dungeons, Raiding, and Player versus Player Combat) and formed models based on the obtained traces. Models are implemented through modification ...
متن کاملMulti-Agent Modeling Using Intelligent Agents in the Game of Lerpa
Game-theory has many limitations implicit in its application. By utilizing multi-agent modeling, it is possible to solve a number of problems that are unsolvable using traditional game-theory. In this paper reinforcement learning is applied to neural networks to create intelligent agents. Utilizing intelligent agents, intelligent virtual players learn from each other and their own rewards to pl...
متن کامل