The srai element allows your bot to recursively call categories after transforming the user’s input. So you can define a template that calls another category. The acronym “srai” has no official meaning, but is sometimes defined as symbolic reduction or symbolic recursion.
This has a wide range of uses:
- Simplifying an input using fewer words
- Remove unnecessary words from the input (reduction)
- Linking many synonymous inputs to the same template
- Correcting spelling errors on the part of the client
- Replacing colloquial expressions with ordinary English
The most typical use case of the srai element is to “reduce” an input by removing unecessary words, or by translating the input in to a shorter, more concise version.
<category> <pattern>HELLO GOOD DAY</pattern> <template><srai>HI</srai></template> </category> <category> <pattern>BONJOUR</pattern> <template><srai>HI</srai></template> </category> <category> <pattern>GUTEN TAG</pattern> <template><srai>HI</srai></template> </category> <category> <pattern>HI</pattern> <template>Hello there!</template> </category>
In the case that the user’s input is “Hello good day,” “Bonjour,” or “Guten Tag,” the first three categories will recurse, and give the bot a new input “HI”. This will then match the fourth category, returning its response to the user.
<category> <pattern>I SEE NOW THAT YOU ARE *</pattern> <template><srai>YOU ARE <star/></srai></template> </category> <category> <pattern>YOU ARE A ROBOT</pattern> <template>Yes I am!</template> </category>
Removing unnecessary words from the input is another way to develop reductions. Reductions make writing AIML and adding to your bot a much more enjoyable process! The more reductions you have, the better your bot will be at providing relevants responses.
Synonyms can be addressed using
<category> <pattern> _ DAD *</pattern> <template><srai><star/> FATHER <star index="2"/></srai></template> </category>
Anytime the user input contains the word “dad”, the bot will replace it with “father” and recurse using the new input. The benefit to this technique is to reduce categories. For instance, Thesaurus.com lists 52 synonyms for the word “good”. To account for this, you would need 52 additional categories for every one that contains the word “good”. If your bot has 100 patterns that contain the word “good”, that is 5200 additional categories you would have to write. Using the synonyms technique, you can reduce the number to just 52. You can reduce that down to just 1 by using a set of synonyms!
NOTE: once a word has been defined as a synonym, you cannot use it in patterns. The leading underscore ensures that the bot translates the synonym before doing anything else.
People are bad at spelling and typing, which may cause your bot to fail when trying to find a match. You can use
<srai> to account for common spelling mistakes, or colloquialisms, such as the following:
<category> <pattern>HOW DO I SING UP</pattern> <template><srai>HOW DO I SIGN UP</srai></template> </category> <category> <pattern>HOW R U</pattern> <template><srai>HOW ARE YOU</srai></template> </category>
Note, spelling errors and synonyms can also be handling using normalization.
Returning Text and Recursing
The previous examples of
<srai> returned no text of their own. Your template, however, can return both text and
<category> <pattern>HOWDY</pattern> <template><srai>HI</srai> Are you a cowboy?</template> </category>
This would return a response “Hello there! Are you a cowboy?” if a client inputs “Howdy”.
Recursively Building a Complex Response
Another use case of the srai element is to provide a recursive response to build a more complex response based on elements within the user’s input. For example, you have a bot named “Buddy”:
User: You can say that again Buddy!
Bot: Once more? “that”.
Your bot may not have a specific response to the pattern “YOU CAN SAY THAT AGAIN BUDDY”. Instead you can build a response to the user input from partial phrase categories:
In step 1, the patterns with “_” match first based on AIML 2.0 wildcard priorities (from highest to lowest), for example:
# AGAIN > _ AGAIN > THAT AGAIN > ^ AGAIN > * AGAIN
Whatever matches either wild-card symbol becomes the value of
|Step||Normalized Input||Matching Pattern||Template||Response|
|1||YOU CAN SAY THAT AGAIN BUDDY||_ <bot name="name" />||<sr />|
|2||YOU CAN SAY THAT AGAIN||_ AGAIN||Once more? <sr />||Once more?|
|3||YOU CAN SAY THAT||YOU CAN *||<sr />||Once more?|
|4||SAY THAT||SAY *||"<person />".||Once more? "that".|
Steps 1 through 3 illustrate the common AIML templates that use the abbreviated `
The categories with the patterns “_ <bot name=”name” />” and “YOU CAN *” simply reduce the sentence to whatever matches the wildcard, as illustrated by steps 1 and 3.
<sr/> with an ordinary text response, as step 2 with the pattern “_ AGAIN”. The phrase “Once more?” becomes part of any reply ending in “AGAIN”.
The category in step 4 with “SAY *” is a default that often produces logically correct but amusing dialogue:
User: Say Hello in Swedish.
Bot: “Hello in Swedish.”
or as in this case:
User: Say that.
<person/> tag in this examples allows for the transforming of any pronouns. (See AIML reference for more details).
User: Say you are a fish again.
Bot: Once more? “I am a fish”.
In this particular case, you might not want pronoun transformation; it’s just included as an example for illustration.
Why reduce a complex sentence structure to simpler forms? These 4 categories can respond to a large number of inputs, not just the example, and can make a bot’s response seem more human-like.