A simple introduction to building creative Twitter "bots" with Tracery and CheapBotsDoneQuick.com that explores the specific topic of pizza construction, naming and recommendation.

2. Create an account on Twitter, log in to Twitter with this
account, then visit CheapBotsDoneQuick.com and Sign in.

3. Open your grammar with a { in the JSON window …
The main rule of your Tracery grammar
is called “origin.” The name of each rule
is a non-terminal symbol that can be
expanded using the right-hand-side. We
refer to non-terminals by encasing them
in # … # on the right-hand side of a rule.

4. Next, write rules to expand the non-terminals #meat#
and #veggie# in our earlier rule for making a pizza
Add as many options as you want to the right-
hand-side of each rule; all are equally likely

5. Ditto for the non-terminals #cheese# and #sauce#
Add as much variety as your imagination will allow …
When Tracery expands a rule (to generate a
pizza, say), it randomly picks from the right-
hand-side options for each non-terminal.

6. We now write a rule for the last non-terminal, #base#,
and close then our grammar with a final }
Every non-terminal in every rule now
has its own explicit rule definition, so our
grammar is ready to start generating.

7. This handy button on the bottom right allows you to
test the grammar, and tweet the results you like …
Use this drop-down menu to select the
frequency with which your bot will tweet
the outputs of its Tracery grammar.

8. Finally, save your bot on CheapBotsDoneQuick.com
BotPizzaKitchen
Choose to make your grammar visible to users of the site.

9. Now, what if we want our grammar
to give a name to each pizza?
I can
think of
a few …

10. We can make a name by joining two namelets
But our names bear no specific relation to their pizzas

11. If only the expansions of #Meat#, #Cheese#
and the other ingredients were influenced by
the expansions for #Prefix# and #Suffix#
But Tracery grammars are Context-Free: every non-
terminal like #Meat# is expanded in a null context,
independently of all others. No Context, No Influence!

12. Here’s what we aim for: the Cave wall prefix
suits Quorn, while Cowboy is apt for Ranch
sauce. The name influences the ingredients.
Here’s a naïve solution: write specific pizza rules that
hardcode relations across non-terminals. So #Prefix_A#
generates names that go with #Meat_A#, and so on.

14. *&$^$%
Flippin’
Nora!

15. Pre-Name Ingredients Prefix Suffix Post-Name Ingredients
So split the generator into two self-contained components

16. Meat Prefix Suffix Vegetable on Cheese & Sauce on Base
Meat & Vegetable Prefix Suffix on Cheese & Sauce on Base
Meat & Vegetable on Cheese Prefix Suffix on Sauce on Base
Meat & Vegetable on Cheese & Sauce Prefix Suffix on Base
We’ll need four high-level rules
for four different kinds of split.

17. Suffix Vegetable on Cheese & Sauce on Base
Suffix Vegetable on Cheese & Sauce on Base
Suffix Vegetable on Cheese & Sauce on Base
Suffix Vegetable on Cheese & Sauce on Base
Each self-contained component has internal agreement
that can vary in the number of dependency pairings
Meat Prefix
1 Possibility 4 Agreement Possibilities
Yeah!

18. Our new top-level rules are below, and produce named
pizzas as above. In total, the new grammar has 100 rules.

19. You can download the grammar and this
spreadsheet from our Github repository
All the data for our grammar is stored in a spreadsheet

20. What a
great flippin’
pantry!

21. CheapBotsDoneQuick allows us
to make our bots responsive to
tweets that explicitly @ mention
them. So let’s make our bot
deliver pizza recommendations
that match specific tastes.
I could
murder a
seafood
pizza!

22. First, we
need to add a
new column
to our DB …

23. The type of a pizza is a function of the type of its
ingredients. So for each qualification on ingredients,
generate new rules for pizzas and their elements.

25. Maker
Specialized
Namer
Pizza-specific
Combined
Grammar

26. The named-pizza maker has 100 rules.
This jumps to 800 rules when we add
qualifications, and to 1500 rules when
we add negative qualifications as well.

27. Your pizza,
M’lord.
But our Tracery grammar still fits into
CheapBotsDoneQuick.com so let’s press
on with the delivery of speciality pizzas.
Choose the Reply option for
your bot (still in Beta) so you
can configure how it will reply
to @ mentions from others.

28. {
“.”: “#pizza#”
}
Response grammars use
a simpler JSON format,
with just one element
on the right-hand-side.
But you CAN refer to
non-terminals in the
main grammar here.
The left-hand-side of a response rule
is not a non-terminal, but a string that
must literally match part of an @mention

29. Each qualification of an
ingredient is listed on the
left. The corresponding
pizza non-terminal is on
the right.
These response rules are applied in the given order, so
it is important that the negative qualifications (e.g.
“no meat”) are listed before the positives (e.g. “meat”).

30. Each left-hand-side can also be a regular expression. Use
“.” to match any string. Use [x|X] for a choice between
“x” and “X”, as in the case-insensitive texts above.

31. Your
F**king
Wine, Sir.
Let’s consider how our pizza bot
might usefully interact with others.
How about another bot that suggests wine
pairings for our newly invented pizzas?

32. We need to
whip up a cocktail
of complementary
grammars.
One will map from foods to
wines, and another will listen for the
triggers to exploit this mapping.

33. To start, we should codify our wine-pairing
knowledge in an explicit form, as in this very
spreadsheet …

34. Each food-feature-rule (non-terminals of the
form “feature bev”) maps onto its own specialist
list of recommendations
Add a separate rule for each feature of the food on
which a wine pairing can be predicated …

35. Now, we need a means of invoking each food-
feature-rule, so for each food feature, add a
stimulus:response pair to the response grammar.

36. Now we need to get the bicameral parts
of the grammar to talk to each other.
We must make sure that every pizza
created by our bot mentions the name
of a recommendation bot, such as itself

37. Now our recommender can be triggered
by any mention of specific ingredients
(or a whole class of ingredients)
in our pizza descriptions …
… and respond accordingly, as below.

38. But our
wine grammar
offers pairings
on the basis
of just a single
ingredient!
Fancy a drink
with your pizza?
Flippin’ heck,
you’re right!

39. Solution:
consider all
pairwise combos
of ingredients
that suggest the
same wine!
For each
combo, add an
explicit recomm-
endation rule to
the response
grammar.

40. Notice the use of the wildcard characters .+ here. The dot .
matches any character while the + means one or more uses

41. Now our
response
grammar can
recommend wine
for a pairing of
ingredients.