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When creating an intent, you can name it anything you like; we go with 'answer_name' here. Below 'Action and parameters', you should give the name of the intent that will actually be used in your program. Here, we also set that to 'answer_name'. Moreover, it is useful to set a context for the intent. A context is set by the requester in order to indicate that we only want to recognize this specific intent, and not another one. Usually, in a social robotics application, the kind of answer we want to get is known. We match the name of the (input)context with the name of the intent, and thus make it 'answer_name' as well. By default, Dialogflow makes the context 'stick' for 5 answers; we can fix this by changing the 5 (at the output context) to a 0. Now we arrive at the most important aspect of the intent: the training phrases. Here, you can give the kinds of input strings you would expect; from these, Dialogflow learns the model it will eventually use. You can identify part of the phrase as a parameter by double-clicking on the relevant word and selecting the appropriate entity from the list. It will then automatically appear below ‘Action and parameters' as well; the ‘parameter name’ there will be passed in the result (we use ‘name’ here). The system has many built-in entities (like 'given name'), but you can define your own entities as well (even through importing CSV files). Our complete intent example thus looks like this (note: using sys.given-name is usually preferred):
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Using the Dialogflow
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component
Let's create a simple demo that prints the transcript and the intent detected by Dialogflow.
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