A context-aware knowledge acquisition for planning applications using ontologies
This work addresses the problem of improving automated planning for agents in dynamic environments, though it appears incremental as it builds on existing ontology-based methods without specifying broad SOTA gains.
The paper tackles the challenge of enabling automated agents to react intelligently to unexpected events in real execution environments by developing a domain-independent approach that uses ontologies and semantic operations to acquire and integrate context-aware knowledge, resulting in more human-like behavior for handling such events.
Automated planning technology has developed significantly. Designing a planning model that allows an automated agent to be capable of reacting intelligently to unexpected events in a real execution environment yet remains a challenge. This article describes a domain-independent approach to allow the agent to be context-aware of its execution environment and the task it performs, acquire new information that is guaranteed to be related and more importantly manageable, and integrate such information into its model through the use of ontologies and semantic operations to autonomously formulate new objectives, resulting in a more human-like behaviour for handling unexpected events in the context of opportunities.