论文标题
使用知识检索搜索算法提取任务树
Extracting task trees using knowledge retrieval search algorithms in functional object-oriented network
论文作者
论文摘要
面向对象的网络（FOON）已开发为一种知识表示方法，机器人可以使用该方法来执行任务计划。可以将FOON视为图形，可以通过知识检索过程为机器人提供有序的计划以检索任务树。我们比较了两种搜索算法，以评估其在提取任务树时的性能：具有两个不同的启发式功能，迭代加深搜索（IDS）和贪婪的最佳优点搜索（GBF）。然后，我们确定哪种算法能够使用最少数量的功能单元获得各种烹饪食谱的任务树。初步结果表明，根据提供给搜索算法的食谱，每种算法的性能都比其他算法更好。
The functional object-oriented network (FOON) has been developed as a knowledge representation method that can be used by robots in order to perform task planning. A FOON can be observed as a graph that can provide an ordered plan for robots to retrieve a task tree, through the knowledge retrieval process. We compare two search algorithms to evaluate their performance in extracting task trees: iterative deepening search (IDS) and greedy best-first search (GBFS) with two different heuristic functions. Then, we determine which algorithm is capable of obtaining a task tree for various cooking recipes using the least number of functional units. Preliminary results show that each algorithm can perform better than the other, depending on the recipe provided to the search algorithm.
