Machine learning and heuristic search

我的研究目标是在机器学习和启发式搜索领域发展科学的理解和改进的算法，并探索这些领域技术的实际应用。机器学习算法使用训练数据或过去的经验来提高其在特定任务中的表现。在我的研究中，任务是与单个对手打扑克，机器学习将用于识别对手的优势和劣势，并相应地调整其玩法，以最大限度地提高其对特定对手的胜利。这是非常具有挑战性的，因为学习必须非常迅速（总共只有100-200手将与这个对手进行比赛），并且学习算法可用的信息是嘈杂和不完整的。启发式搜索算法是一种快速寻找图中两个节点之间最优路径的方法。例如，如果图形是路线图，则可以使用启发式搜索算法快速找到从当前位置到特定目的地的最短路线。启发式搜索比标准方法更快，因为它使用了到目标距离的估计，称为启发式函数。我的研究探讨了加速启发式搜索的各种方法。我正在研究的一种方法是将启发式函数分解为一组更小的启发式函数的和。我的目的是了解什么时候这是可能的，什么时候它优于其他方法。我还在研究预测给定启发式函数将产生多少加速的技术。

My research aims to develop scientific understanding and improved algorithms in the areas of machine learning, and heuristic search, and to explore practical applications of techniques in these areas.A machine learning algorithm uses training data, or past experience, to improve its performance at a specific task.  In my research the task is playing poker against a single opponent, and machine learning will be used to identify the opponent's strengths and weaknesses and adapt its play accordingly in order to maximize its winnings against this specific opponent. This is very challenging because learning must be very rapid (only 100-200 hands will be played against this opponent in total), and the information available to the learning algorithm is noisy and incomplete.A heuristic search algorithm is a fast method for finding optimal paths between two nodes in a graph.  For example, if the graph is a road map, a heuristic search algorithm could be used to quickly find the shortest route from your current location to a particular destination.  Heuristic search is faster than standard methods because it uses an estimate of the distance to the goal, called a heuristic function.  My research investigates various ways of speeding up heuristic search.  One of the methods I am investigating is to decompose the heuristic function into the sum of a set of smaller heuristic functions. My aim is to understand when this is possible, and when it is superior to other methods.  I am also investigating techniques for predicting how much speedup will be produced by a given heuristic function.