Extending planning knowledge using ontologies for goal opportunities
This work addresses a specific bottleneck in AI planning for systems that need to adapt to unforeseen environmental changes, representing an incremental advancement.
The paper tackles the limitation of existing goal-directed planning approaches that cannot handle unanticipated changes involving new object types, by introducing a domain-independent method that extends planning knowledge using ontologies, semantic measures, and alignment to incorporate new data and formulate goal opportunities, resulting in better-valued plans.
Approaches to goal-directed behaviour including online planning and opportunistic planning tackle a change in the environment by generating alternative goals to avoid failures or seize opportunities. However, current approaches only address unanticipated changes related to objects or object types already defined in the planning task that is being solved. This article describes a domain-independent approach that advances the state of the art by extending the knowledge of a planning task with relevant objects of new types. The approach draws upon the use of ontologies, semantic measures, and ontology alignment to accommodate newly acquired data that trigger the formulation of goal opportunities inducing a better-valued plan.