  Robots can be very useful to automate tasks and reduce the human effort
required. But for the robot to know, how to perform tasks, we need to give it a
clear set of steps to follow. It is nearly impossible to provide a robot with
instructions for every possible task. Therefore we have a Universal Functional
object-oriented network (FOON) which was created and expanded and has a lot of
existing recipe information [1]. But certain tasks are complicated for robots
to perform and similarly, some tasks are complicated for humans to perform.
Therefore weights have been added to functional units to represent the chance
of successful execution of the motion by the robot [2]. Given a set of kitchen
items and a goal node, using Universal FOON, a robot must be able to determine
if the required items are present in the kitchen, and if yes, get the steps to
convert the required kitchen items to the goal node. Now through this paper, we
use two algorithms (IDS and GBFS) to retrieve a task tree (if possible) for a
goal node and a given set of kitchen items. The following would be the
different parts of the paper: Section II FOON creation, where we will discuss
the different terminologies related to FOON and visualization of FOON. In
Section III Methodology we discuss the IDS and GBFS search algorithms and the
two different heuristics implemented and used in GBFS. In Section IV
Experiment/Discussion, we compare the performance of different algorithms. In
the final section V, we specify the references of the papers that have been
cited.
