Learning Hierarchical Task Models from Input Traces
We present an implementation of a plan adaptation system, BioPlanner, built for biological pathway prediction across species. BioPlanner formulates a pathway discovery problem as a Hierarchical Task Network (HTN) planning problem and solves it by adapting a plan solution of another well-studied pathway. BioPlanner provides the following functionalities: • It automatically builds HTN planning models for a biological pathway domain from the semantic web biological knowledge bases (KBs). • It retrieves plan cases from the biological KBs. • It generates hypothetical pathways using plan adaptation strategies with the aid of biological domain knowledge. • It evaluates the hypothetical plan candidates, ranks them, and recommends the most likely hypotheses to users. • It employs an information gathering multi-agent system to capture knowledge from heterogeneous sources to help the hypothetical plan generation process. We utilize BioPlanner to predict Signaling Transduction pathways for Mus musculus, Gallus gallus, and Drosophila melanogaster from Homo sapiens.