Abstract: Systems and devices to perform knowledge-driven dynamic conversations and select content within automated agents such as chatbots and virtual assistants are disclosed. In an example, operations to facilitate a knowledge-based conversation session with a human user using an automated agent include: receiving a conversational input regarding a support issue; analyzing the conversational input to determine an intent and applicable entity properties associated with the intent; performing a multi-turn conversation to identify a solution using the intent and the applicable entity properties, by exchanging iterative questions and answers between the automated agent and the user to dynamically recalculate applicability of the solution to the support issue; and outputting information associated with the identified solution.

Abstract: In some examples, iterative sampling based dataset clustering may include sampling a dataset that includes a plurality of items to identify a specified number of sampled items. The sampled items may be clustered to generate a plurality of clusters. Un-sampled items may be assigned from the plurality of items to the clusters. Remaining un-sampled items that are not assigned to the clusters may be identified. A ratio associated with the remaining un-sampled items and the plurality of items may be compared to a specified threshold. Based on a determination that the ratio is greater than the specified threshold, an indication of completion of clustering of the plurality of items may be generated.

Abstract: Systems and devices to perform content authoring and establish conversation models for automated agents such as chatbots and virtual assistants are disclosed. In an example, operations for content authoring, to produce a conversation model, include: identifying respective intents from conversation segments in an unstructured data source; generating a knowledge graph of the conversation model to organize the identified intents; linking the intents in the knowledge graph to properties of the respective conversations, for properties used to guide a subject conversation with the conversation model; and outputting the conversation model, to be usable with the automated agent to conduct the subject conversation with a human user.

Abstract: Systems and devices to perform knowledge-driven dynamic conversations and select content within automated agents such as chatbots and virtual assistants are disclosed. In an example, operations to facilitate a knowledge-based conversation session with a human user using an automated agent include: receiving a conversational input regarding a support issue; analyzing the conversational input to determine an intent and applicable entity properties associated with the intent; performing a multi-turn conversation to identify a solution using the intent and the applicable entity properties, by exchanging iterative questions and answers between the automated agent and the user to dynamically recalculate applicability of the solution to the support issue; and outputting information associated with the identified solution.

Abstract: Generally discussed herein are devices, systems, and methods for virtual agent selection of an option not expressly selected by a user. A method can include receiving, from a virtual agent interface device of the virtual agent device, a response regarding a problem, wherein the response is responsive to a prompt, and wherein the prompt is associated with one or more expected responses, determining whether the response is a match to one of the expected answers by performing one or more of (a) an ordinal match, (b) an inclusive match, (c) an entity match, and (d) a model match, and providing, responsive to a determination that the response is a match, a next prompt, or provide a solution to the problem, the next prompt associated with expected responses to the next prompt.