RI: Small: General Intelligence through Algorithm Invention and Selection

创造更好的人工智能在社会的不同领域有很多应用，从自动驾驶汽车和飞机到生产规划、机器控制和音乐创作。目前大多数人工智能研究都集中在创建只能做一件事或解决一个问题的算法上。 为了实现通用人工智能，我们必须学习如何创建可以解决许多不同问题的算法，而无需人类为每个问题调整算法。该项目的研究旨在了解如何创建这种人工通用智能。其基本思想是构建算法，这些算法可以创建自己的更具体的算法，并学会为正确的问题自动选择正确的算法。为了开发这些算法，我们需要大量的好问题来测试它们。游戏被广泛用于测试AI算法，因为它们模拟了现实世界的问题，但速度快，易于执行。本研究项目的目标是研究如何通过在线随机搜索算法，结合在线选择算法，创造更通用的人工智能。本研究项目将在一组经典游戏和一个实时策略游戏上测试开发的通用算法。换句话说，该项目将研究在树搜索算法空间中的遗传编程与算法选择（也称为超进化）的结合，以创造更一般的解决问题的能力。这些能力将通过在两个不同的试验台上进行的一系列实验进行评估。 这项研究的成功完成将阐明算法空间搜索作为创建更通用人工智能的方法的潜力，并产生许多算法。这包括将被设计用于搜索算法和在算法之间搜索的算法，以及将通过在算法空间中搜索而发现的新算法。所产生的方法预计最终将普遍适用于大量的问题。

Creating better artificial intelligence has plenty of applications in different areas of society, from self-driving cars and aircraft to production planning, control of machines and music composition. Most current artificial intelligence research focuses on creating algorithms that can only do a single thing, or solve a single problem. To achieve artificial general intelligence we must learn how to create algorithms that can solve many different problems, without a human having to adjust the algorithm for every problem. The research in this project aims to understand how such artificial general intelligence can be created. The basic idea is to build algorithms that can create their own more specific algorithms, and learn to automatically select the right algorithm for the right problem. In order to develop these algorithms, we need a large set of good problems to test them on. Games are widely used to test AI algorithms, because they model real-world problems but are fast and easy to execute. The general algorithms developed in this project will be tested on a set of classic games, and a real-time strategy game.This research project aims to investigate how we can create more general artificial intelligence through online stochastic search for algorithms, combined with and informed by online selection among discovered algorithms. In other words, the project will investigate the combination of genetic programming in the space of tree search algorithms with algorithm selection, also called hyper-heuristics, for creating more general problem-solving abilities. These capabilities will be evaluated through a sequence of experiments on two different test beds. Successful completion of the research will clarify the potential of search in algorithm space as a method for creating more general artificial intelligence, and produce a number of algorithms. This includes both the algorithms that will be designed for searching for algorithms and searching among algorithms, as well as the new algorithms that will be discovered by the search in algorithm space. The methods produced are expected to ultimately be generally applicable to a large number of problems.