Extracting task trees using knowledge retrieval search algorithms in functional object-oriented network
Year: 2022
Type: preprint
Abstract: 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.
Source: arXiv (Cornell University)
Author: Lewis, Tyree
Institution: University of South Florida
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4309214393