ontology-query

To query an AI agent's ontology graph, you can retrieve and manage specific rules, spaces, and links by executing Python-based scripts that interact directly with the cognitive architecture for advanced rule retrieval.

Graph querying in AI architecture is used to explore an agent's cognitive structure, enabling detailed analysis of rules and relationships to understand their impact on the AI reasoning and decision-making processes.

You analyze space distribution and connections within an AI ontology by running script-based queries to retrieve specific spaces and links, allowing you to map how different rules interact across the architecture.

Yes, you need Python to perform advanced queries on the ontology graph, as in-depth querying and custom interactions rely on executing Python scripts to retrieve rules and analyze relationships.

Yes, you can review rules within a specific space like the policy space by querying the ontology graph to retrieve associated rules and relationships, mapping their connections and influence on agent behavior.

The primary limitation of script-based interactions for ontology rule management is the requirement for Python script execution, meaning users need scripting knowledge to perform in-depth queries and space distribution analysis.