Adaptive General Search Framework for Games and Beyond

The research field of Artificial General Intelligence (AGI) is concerned with the creation of adaptive programs that can autonomously address tasks of a different nature. Search and planning have been identified as core capabilities of AGI, and have been successful in many scenarios that require sequential decision-making. However, many search algorithms are developed for specific problems and exploit domain-specific knowledge, which makes them not applicable to perform different tasks autonomously. Although some domain-independent search algorithms have been proposed, a programmer still has to make decisions on their design, setup and enhancements. Thus, the performance is limited by the programmer's decisions, which are usually biased. This paper proposes to develop a framework that, in line with the goals of AGI, autonomously addresses a wide variety of search tasks, adapting automatically to each new, unknown task. To achieve this, we propose to encode search algorithms in a formal language and combine algorithm portfolios with automatic algorithm generation. In addition, we see games as the ideal test bed for the framework, because they can model a wide variety of complex problems. Finally, we believe that this research will have an impact not only on the AG I research field, but also on the game industry and on real-world problems.