Extending planning knowledge using ontologies for goal opportunities

TL;DR

This paper presents a domain-independent method that enhances goal-directed planning by integrating new object types through ontologies, enabling better adaptation to environmental changes and improved planning outcomes.

Contribution

It introduces a novel approach using ontologies and semantic measures to extend planning knowledge with new object types, improving goal opportunity detection.

Findings

The approach effectively incorporates new object types into planning tasks.

It improves the generation of goal opportunities in dynamic environments.

The method is domain-independent and adaptable to various planning scenarios.

Abstract

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.