Abstract: An agent-based system may be automatically generated from a specification provided by a user or third-party process. An agent generator may map the specification to a canonical model identifying one or more tasks to be performed by the agent-based system as ontological concepts. The agent generator may generate one or more candidate agents using the canonical model. The candidate agents may comprise one or more interconnected data transforms, which may comprise data access transforms, preprocessing transforms, machine learning transforms, and/or structural transforms. The agent generator iteratively modifies the agent-based system until a termination criteria is satisfied. The termination criteria may provide a selection mechanism whereby a performance of the plurality of candidate agents may be evaluated. An optimal agent may be selected using, inter alia, the performance of the agent-based system.

Abstract: Concepts relevant to natural language content may be identified using an ontology. The natural language content may be tokenized and normalized. Using the tokenized content, one or more candidate concepts within the ontology may be identified. Concepts relevant to the natural language content may be selected using the identified concepts and the relationships between concepts defined within the ontology. A spreading activation process may be used to identify related concepts. The spreading activation process may be iterative and/or may reach multiple generations of connected concepts within the ontology. The relevant concepts associated with the natural language content may be used to index the natural content, identify related content, provide targeting advertising related to the natural language content, and the like.

Abstract: An agent-based system may be automatically generated from a specification provided by a user or third-party process. An agent generator may map the specification to a canonical model identifying one or more tasks to be performed by the agent-based system as ontological concepts. The agent generator may generate one or more candidate agents using the canonical model. The candidate agents may comprise one or more interconnected data transforms, which may comprise data access transforms, preprocessing transforms, machine learning transforms, and/or structural transforms. The agent generator iteratively modifies the agent-based system until a termination criteria is satisfied. The termination criteria may provide a selection mechanism whereby a performance of the plurality of candidate agents may be evaluated. An optimal agent may be selected using, inter alia, the performance of the agent-based system.