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Create	an	account	on	Twitter,	log	in	to	Twitter	with	this	
account,	then	visit	CheapBotsDoneQuick.com	and	Sign	in.
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.
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
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.
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.
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.
Finally,	save	your	bot	on	CheapBotsDoneQuick.com		
BotPizzaKitchen	
Choose	to	make	your	grammar	visible	to	users	of	the	site.
Now,	what	if	we	want	our	grammar	
to	give	a	name	to	each	pizza?		
	
I	can		
think	of		
a	few	…
We	can	make	a	name	by	joining	two	namelets		
But	our	names	bear	no	specific	relation	to	their	pizzas
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!
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.
*&$^$%	
	
Flippin’	
Nora!
Pre-Name	Ingredients		Prefix	 Suffix	Post-Name	Ingredients		
So	split	the	generator	into	two	self-contained	components
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.
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!
Our	new	top-level	rules	are	below,	and	produce	named	
pizzas	as	above.	In	total,	the	new	grammar	has	100	rules.
You	can	download	the	grammar	and	this	
spreadsheet	from	our	Github	repository	
All	the	data	for	our	grammar	is	stored	in	a	spreadsheet
What	a		
great	flippin’	
pantry!
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!
First,	we	
need	to	add	a	
new	column	
to	our	DB	…
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.
Maker	
Specialized	
Namer	
Pizza-specific	
Combined	
Grammar
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.
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.
{
“.”: “#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
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”).
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.
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?
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.
To	start,	we	should	codify	our	wine-pairing	
knowledge	in	an	explicit	form,	as	in	this	very	
spreadsheet	…
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	…
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.
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
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.
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!
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.
Notice	the	use	of	the	wildcard	characters	.+	here.	The	dot	.	
matches	any	character	while	the	+	means	one	or	more	uses
Now	our	
response	
grammar	can	
recommend	wine	
for	a	pairing	of	
ingredients.

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