Abstract: A method for controlling an artificial intelligence (AI) device can include obtaining, via a processor in the AI device, a knowledge base including a plurality of nodes, and flattening, via the processor, the knowledge base by transforming the knowledge base into a first plurality of documents to form a first index, each of the first plurality of documents identifying a node within the knowledge base. Also, the method can further include receiving, via the processor, a user query, retrieving, via the processor, a subset of documents based on the first plurality of documents and the user query, and performing, via the processor, a task related to knowledge base question answering (KBQA) based on the subset of documents.

Abstract: A method for controlling an artificial intelligence (AI) device can include obtaining, via a processor in the AI device, a knowledge base including a plurality of nodes, and flattening, via the processor, the knowledge base by transforming the knowledge base into a first plurality of documents, each of the first plurality of documents identifying a node within the knowledge base. Also, the method can further include receiving, via the processor, a user query, performing, via the processor, matching based on the user query and the first plurality of documents to generate a ranked results list, reducing, via the processor, the ranked results list based on a reducing operation to generate a reduced list, and outputting, via the processor, linked entities based on the reduced list.

Abstract: A robot that includes an RL agent that is configured to learn a policy to maximize the cumulative reward of a task, to determine one or more features that are minimally correlated with each other. The features are then used as pseudo-rewards, called feature rewards, where each feature reward corresponds to an option policy, or skill, the RL agent learns to maximize. In an example, the RL agent is configured to select the most relevant features to learn respective option policies from. The RL agent is configured to, for each of the selected features, learn the respective option policy that maximizes the respective feature reward. Using the learned option policies, the RL agent is configured to learn a new (second) policy for a new (second) task that can choose from any of the learned option policies or actions available to the RL agent.

Abstract: A robot that includes an RL agent that is configured to learn a policy to maximize the cumulative reward of a task, to determine one or more features that are minimally correlated with each other. The features are then used as pseudo-rewards, called feature rewards, where each feature reward corresponds to an option policy, or skill, the RL agent learns to maximize. In an example, the RL agent is configured to select the most relevant features to learn respective option policies from. The RL agent is configured to, for each of the selected features, learn the respective option policy that maximizes the respective feature reward. Using the learned option policies, the RL agent is configured to learn a new (second) policy for a new (second) task that can choose from any of the learned option policies or actions available to the RL agent.