US 12,412,071 B2
Creating satisficing planners with deep learning
Michael Katz, Goldens Bridge, NY (US); and Patrick Christoph Ferber, Au (DE)
Assigned to International Business Machines Corporation, Armonk, NY (US)
Filed by International Business Machines Corporation, Armonk, NY (US)
Filed on Dec. 27, 2021, as Appl. No. 17/646,088.
Prior Publication US 2023/0206027 A1, Jun. 29, 2023
Int. Cl. G06N 3/042 (2023.01); G06N 3/08 (2023.01); H04L 41/14 (2022.01)
CPC G06N 3/042 (2023.01) [G06N 3/08 (2013.01); H04L 41/14 (2013.01)] 11 Claims
OG exemplary drawing
 
1. A computer-implemented method comprising:
receiving a planner application data set that includes information indicative of contexts in which a newly-designed satisficing planner is expected to be applied;
receiving a machine learning algorithm that has been trained to design satisficing planners; and
applying the machine learning algorithm to the planner application data set to obtain a newly-designed satisficing planner design, with the newly-designed satisficing planner design including the information indicative of a plurality of satisficing planner components to be included in the newly-designed satisficing planner design and an order in which the satisficing components are to be run, wherein the applying of the machine learning algorithm includes the following operations:
for each given satisficing planner component of the plurality of satisficing planner components, determining a component type from a plurality of component types for the given satisficing planner component, wherein the plurality of component types are selected from a list consisting of heuristic function types, search algorithm types, and refinement algorithm types, and wherein the refinement algorithm types comprise a search boosting component subtype and a search pruning component; and
for each given satisficing planner component of the plurality of satisficing planner components, determining a component candidate, from among a plurality of component candidates, of the determined component type of the given satisficing planner component.