DialogEngines > Overview > Dialog

Dialog

DialogEngines conduct goal-oriented consulting conversation about given subjects with individual human users in pseudo natural language.

Product Model

specifications
properties

The target domain of the dialog is an excerpt from an SQL, CSV or XML product data base. The product knowledge defines the scope of the dialog.

User Model

explore
understand
find
reassure

The dialog is with an individual user. User input to the agent and user answers to questions by the agent give the data set which drives the user model. Base assumption is that the user wants information about the domain and a specific solution.

Agent Model

show
suggest
confirm

Agents can have different characteristics, not only age and sex, but also deeper ones relevant for the progress of the user interaction. Modelled are how polite, explanatory, extrovert, pushy and verbose the agent is. The Agent Model is independent of its rendering in text, voice or animation.

Dialog Model

strategy
understanding
initiative

The dialog consists of repeated listening, understanding, problem solving, question asking and result presentation actions. Dialog history and strategy, together of course with the product solution space, define the flow of the dialog.

The dialog produces the control information for voice and animation.

DialogEngines agents can take initiative and talk actively to the user.

DialogEngines agents can ask intelligent questions to the user, taking into account product knowledge and the context of the conversation.

Language Model

meaning
ambiguity, contradictions
expressions

DialogEngines understands user statements using a shallow natural language processing model, the target product knowledge, and additional language knowledge.

Such additional knowledge is domain expertise and semantics for common vocabulary. For example the word "small" will mean different things for different businesses.

Heuristics are used to resolve ambiguities and contradictions. DialogEngines is designed to be practical and useable: like search engines, which sometimes give wrong answers or not all correct answers, DialogEngines language processing is not guaranteed to be sound and complete.

DialogEngines agents make utterances to users according to a knowledge base of example sentences, which can be answers, statements, greetings, questions, requests for confirmation and more.