Combining Artificial Intelligence Methods : Automating the Playing of DEFCON by Robin Baumgarten MSc in Advanced Computing Individual Project Report

In the commercial video game industry, computer opponents that act intelligently are increasingly important, especially as better graphical effects decline to serve as a driving force for the commercial success of a game. The methods used by developers to create these bots are often obsolete and struggle to scale with the complexity of modern games. Nonetheless the use of modern artificial intelligence techniques used by researchers is rarely seen in video games. In this project, we designed and implemented a computer opponent for the realtime strategy game DEFCON by combining artificial intelligence methods such as case-based reasoning, decision tree algorithms and hierarchical planning. Highlevel strategy plans for matches are automatically created by querying a case base of recorded matches and building a plan decision tree. The development of an automated opponent for a complex video game required the application of many different techniques to receive, store, process and predict game information. For this purpose, alongside a high-level reasoning system, we use secondary AI techniques like simulated annealing and influence mapping to create a reactive and learning bot. We applied these techniques in DEFCON and created a competitive bot that can beat the AI bot developed by Introversion consistently. The importance of small-scale tactics in this game requires careful unit control, which we incorporated through various methods, such as a movement desire model, fleet formations and a synchronous attack algorithm. Extensive testing was conducted to optimise and fine-tune the efficiency of these optimisation algorithms. Comprehensive training of high-level plans enabled our bot to learn potent strategies that provided a win ratio of over 75% against the official AI bot developed for DEFCON by Introversion.