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Chatbot technologies use machine learning to map the users' natural language to a predefined intention .

Basic principle of Intents

Common systems such as Google Dialogflow [1], IBM Watson Assistant [2] and Microsoft LUIS [3] usually work according to the Intents principle.

Relevant user intentions/intents are defined in advance for the use case of the chatbot. This is done by storing possible user inputs and a corresponding response. User queries are assigned to one or more of the already defined intents on the basis of the user's statement(intent matching). The NLP services usually specify a Confidence Level which indicates how confident the system is in assigning the user's statement to an intent.

Components of an Intent

An intent mainly consists of the following components:

1. Intent name
2. utterances
3. Response

Further components may be added depending on the technology used:

1. context
2. Events
3. actions
4. Parameters / contained entities
5. fulfillment

Example Intent

Intent:

Telephone number

Utterances:

"What's your phone number?"

"Where can I reach you by phone?"

"Give me the phone number, please."

"I'm afraid I couldn't find your number anywhere. Could you please tell me?"

Response:

"Sure! Our phone number is 0931 123456789."

Important Intents

• Default Fallback Intent
• Welcome Intent

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Sources

[1] https://dialogflow.com/docs/intents

[2] https://docs.microsoft.com/en-us/azure/cognitive-services/luis/luis-concept-utterance

[3] https://cloud.ibm.com/docs/services/assistant?topic=assistant-intents