  In this experiment, three different search algorithms are implemented for the
purpose of extracting a task tree from a large knowledge graph, known as the
Functional Object-Oriented Network (FOON). Using a universal FOON, which
contains knowledge extracted by annotating online cooking videos, and a desired
goal, a task tree can be retrieved. The process of searching the universal FOON
for task tree retrieval is tested using iterative deepening search and greedy
best-first search with two different heuristic functions. The performance of
these three algorithms is analyzed and compared. The results of the experiment
show that iterative deepening performs strongly overall. However, different
heuristics in an informed search proved to be beneficial for certain
situations.
