[Dev] Chatbots in Prolog
Tanner Lovelace
lovelace at wayfarer.org
Fri Sep 12 23:59:43 EDT 2003
I don't normally forward entire newsletters, but this one was
interesting enough given the fact that we basically have
a chatbot on the irc channel right now. It might
be interesting to experiment with a prolog based
chatbot sometime. O:-) Might be an interesting
way to learn prolog for those that don't know it
already. :-)
Cheers,
Tanner
--
Tanner Lovelace | lovelace at wayfarer.org | http://wtl.wayfarer.org/
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This would be a very good time to hang out with the Open Source
people, before they get formally reclassified as a national security
threat. -- Bruce Sterling
-------- Original Message --------
Subject: Dr. Dobb's AI Expert Newsletter - 9/12/03
Date: Fri, 12 Sep 2003 16:00:00 -0700
From: Dr. Dobb's AI Expert Newsletter <aiexpert at drdobbs.email-publisher.com>
=============================================
Dr. Dobb's AI Expert Newsletter
Issue #11 - September 2003
=============================================
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By Dennis Merritt
Dr. Dobb's AI Expert Newsletter
'AI - The art and science of making computers do interesting things that are not
in their nature.'
-------------------------------------------------------------------------
--Writing a Chat Bot
--Building an Ontology
--Chatting with an Ontology
--Conferences
--Links
I've been meaning to add code corners to the last few newsletters but haven't
found the time. So this newsletter will be all about building the types of AI
systems discussed in the last few issues. In other words, this is an issue for
those who want to roll up their sleeves and create their own AI systems.
The two types of AI application studied are chat bots and ontologies.
July 2003 newsletter - chat bots:
http://www.ainewsletter.com/newsletters/aix_0307.htm
June 2003 newsletter - ontologies:
http://www.ainewsletter.com/newsletters/aix_0306.htm
Both are perfect examples of the ideas expressed in the first newsletter
about AI applications in general. Both require a knowledge representation
language and a reasoning engine that knows how to apply that knowledge.
November 2002: http://www.ainewsletter.com/newsletters/aix_0211.htm
In the case of chat bots, the knowledge is patterns of inputs and responses
that drive a dialog. In the case of ontologies, the knowledge is relationships
and attributes of words and concepts.
A chat bot tool provides a way to represent the knowledge and a way to deploy
it. An ontology tool does the same.
There are chat bot and ontology tools available, and good ones at that, but
they suffer the problems of all AI tools. They make assumptions about how and
what types of knowledge can be represented, and further, on how that knowledge
is deployed.
If the assumptions fit an application area, that's great. But if not... Well
that's a very practical line of thought. The simple truth is, it's a lot more
fun to play with your own knowledge representation and reasoning engine, and
you can get it to do exactly what you want.
The programs can be written in any programming language, but we'll use Prolog
in the newsletter because it is particularly versatile for a rapid prototyping
and experimentation approach to AI concepts, as well as being very compact and
fitting better in a newsletter.
Logic programs have the added benefit that they read much like program
specifications
and can be used for the design of applications intended for implementation in
other languages. So if you don't like Prolog, read along and code in Lisp, Mozart,
Java or the language of your choice. For those who don't know Prolog, there
are boxes with explanatory notes.
One final note. Design decisions for the programs were always made in favor
of clarity, sometimes at the expense of performance, although this will not
be an issue until the knowledge base for either program grows to a large size.
As always I like to read your feedback, and for any who request it I can e-mail
back the source code developed in this article.
dennis at ddj.com
------------------------------------------
Chat Bot
Chat bot is the generic name for programs derived from Weizenbaum's original
Eliza program, which mimicked a therapist. Eliza was written almost as a joke,
to show how mindless pattern-matching rules can be used to generate realistic
conversations, thus proving the software was NOT intelligent. Yet chat bots
have come closer to passing the Turing test than other types of programs, and
Richard Wallace hypothesizes that that is because much human dialog is, in fact,
mindless pattern-matching.
------------------------------------------
Pronoun Reversal
One of the main insights in Eliza was how much can be done simply by changing
the grammatical person of a sentence, like 'I' to 'you' and 'mine' to 'yours'.
This is called pronoun reversal. We'll start with a very simple, and extremely
annoying chat bot that does only that, and adds a question mark at the end.
To represent the knowledge of which words to reverse, we'll use Prolog facts,
which are logically the same as relations in a relational database. Here are the
few we'll use for testing, in a relation call 'me_you/2', the /2 indicating
there are two arguments.
me_you(me,you).
me_you(i,you).
me_you(my,your).
me_you(am,are).
The choice of the me_you/2 predicate was the first design decision about knowledge
representation.
------------------------------------------
Input/Output
The reasoning engine needs an interface to the user or caller. For the chat
bot all that is required is a predicate (like a function) that takes an input
string of user text and puts out a response string. A predicate 'respond'
with two arguments, the input and the response, provides that service. It is
referred to in Prolog as 'respond/2.'
Any application that can call Prolog could be used to provide the dialog interface
with the user, and Prolog is certainly a language that can call Prolog. Here's
a simple loop for a console version of the program that can also be run in a
Prolog listener (interpreter).
main :-
write('DDJ AI Expert ChatBot Sample'), nl,
repeat,
write('> '), read_string(InputString),
respond(InputString, ResponseString),
write(ResponseString), nl,
InputString == `quit`.
A Prolog 'logic base', or program, is made up of logical 'clauses'
that define 'predicates.' This one is simple. The predicate is called
'main/0' (the /0 indicating no arguments) and has only one clause.
After the neck' :-' symbol is a comma-separated list of 'goals',
which typically call other predicates. The most important one here is 'respond/2',
which will do the real work. Logical variables are indicated by an initial
upper case letter, such as 'InputString.'
------------------------------------------
Core Pattern Matcher
How should a sentence be represented internally by the program? Thinking of
the sentence as a list of words seems like a good starting point. Proceeding
in a top-down manner, here is the implementation of 'respond/2.'
respond(InputString, ResponseString) :-
string_to_list(InputString, InputWordList),
swap_person(InputWordList, SwappedWordList),
form_response(SwappedWordList, ResponseWordList),
list_to_string(ResponseWordList, ResponseString),
!.
The '!', called 'cut', is used to turn off Prolog's automatic
backtracking search for multiple solutions for a given rule. In this case,
we only want one response for each user input.
The first and last goal of respond/2 perform the service of converting strings
to and from lists of words. For experimentation, those steps could be simply
eliminated, using lists directly for input and output. For final use, those
steps should be quite nice, dealing with punctuation and capitalization. For
now, we'll make use of simple built-in predicates that do the conversion for
us but require everything in lower case. Later nicer versions of these can be
implemented.
string_to_list(String, List) :-
string_tokens(String, List).
list_to_string(List, String) :-
stringlist_concat(List, ` `, String).
The utility predicates for dealing with strings and lists are not part
of the ISO standard for Prolog, so different implementations support different
ones. They are all relatively easy to write in Prolog. The two used here,
'string_tokens/2' and 'stringlist_concat/3' are part of Amzi!
Prolog. You might have to find equivalents for other Prologs, or simply
skip this step and use lists directly for input and output.
Next to implement is the real guts of the program where the 'me_you/2'
knowledge is applied in the first step of creating a response. Recursion is
typically used to process lists in Prolog. The recursive rules to replace the
pronouns in a list of words are:
'boundary condition' - The list is empty, we're done.
'recursive condition' - Take the first word in the list and call 'swap_word/2'
to see what the replacement word is. Put that word in the output list and
recursively
call the rest of input list to generate the rest of the output list.
swap_person([], []).
swap_person([X|Xs], [Y|Ys]) :-
swap_word(X,Y),
!, swap_person(Xs,Ys).
'[H|T]' is Prolog notation for separating a list into its head (first
element), and tail (remainder of the list). This allows for the easy writing
of recursive rules where some operation is performed on the head, and
the tail is then used as the argument to the next recursion. The empty
list is represented by [] and usually signifies the boundary condition
for a recursion.
So, for example, when 'swap_person/2' is called with the input list
[i,want,pizza], the pattern [X|Xs] is mapped to it in a process called
'unification.' X becomes i, Xs becomes [want,pizza]. swap_word/2
will unify Y with you and Ys will become what ever is returned by calling
'swap_person/2' with [want,pizza].
The 'swap_word/2' rules look in the knowledge base portion of the program
to see if the word is one mentioned in the 'me_you/2' relation. If its
not, the word is replaced with itself.
swap_word(X,Y) :- me_you(X,Y).
swap_word(X,Y) :- me_you(Y,X).
swap_word(W,W).
One last bit and we're ready to run. We form the response, for now, by simply
adding a question mark on the end.
form_response(In,Out) :-
append(In, [?], Out).
'append/3' is a common list utility in Prolog that appends one list to
another. In this case we're adding the list with the single element, ?, to the
end of our output list of words. To use a library of common list utilities we
need these two lines at the front of the program. We'll use other list utilities
later as well. (If you don't have a library, the implementations of various
list utility predicates can be readily found and coded directly in the program.)
:- load(list).
:- import(list).
We're now ready to load the program into a Prolog listener and see how it runs.
?- consult(chatbot).
yes
?- main.
DDJ AI Expert ChatBot Sample
> i have problems with my computer
you have problems with your computer ?
> yes
yes ?
> will you help me
will i help you ?
> quit
quit ?
yes
It's already sounding like some people I know.
------------------------------------------
Adding Response Patterns
The 'intelligence' of a chat bot lies in the patterns it is told
to recognize. Lists of words and variables can represent a pattern to look for,
and lists that have those same variables in them can be used for various possible
responses.
There are two types of logical variables in Prolog. Named ones, beginning
with an upper case letter, and anonymous ones, represented by an underscore.
The named variables can be used to echo parts of the input back, and anonymous
ones for indicating we don't care about parts of the input.
Here are two responses for any input that has the adjacent words 'blue screen'
in it.
response( [_,blue,screen,_],
[ [why,do,you,use,windows,?],
[wipe,your,computer,clean,and,install,linux,'.'] ]).
Here's a response pattern for any input with the word 'linux' in
it.
response( [_,linux,_],
[ [nobody,supports,linux,'.'] ]).
That should drive them nuts.
Here are a few more response patterns that might come in handy to keep the
conversation
going. The last one is a catch-all that is used if none of the other patterns
match. It does the job of echoing the input with a question mark. Remember that
the input word lists have already had the pronouns swapped before they are used
in these patterns.
response( [yes,_],
[ [why,?] ]).
response( [no,_],
[ [why,not,?] ]).
response( [_,you,like,X],
[ [how,can,you,like,X,?],
[is,it,strange,to,like,X,?] ]).
response( [_,want,to,X], [ [why,would,you,want,to,X,?], [you,can,not,X,'.'],
[is,it,dangerous,to,X,?] ]).response( [X],
[ [X,?] ]).
When a named variable appears multiple times in a rule, like X in the
above patterns gets a value, all occurrences of X automatically have the
same value. So when the pattern matcher we will write determines a value
of X from the input pattern, then that value will be echoed in the output patterns.
Before looking at the code, here's how these response rules will drive a dialog
with the chat bot.
?- main.
DDJ AI Expert ChatBot Sample
> i get a blue screen on my computer
wipe your computer clean and install linux .
> ok, i did that but now i cannot get linux to work.
nobody supports linux .
When there are multiple response patterns to pick from, the engine will pick
one at random. Here's another run with the same rules, but a different result.
> i get a blue screen on my computer
why do you use windows ?
> i like the windows operating system
is it strange to like the windows operating system ?
> no
why not ?
> lots of people like windows
lots of people like windows ?
> yes
why ?
> it is easy to use
it is easy to use ?
'The Response Pattern Matcher'
The tricky part of the pattern matcher is setting a variable to possibly multiple
words in the input. For example, in the response pattern [_,want,to,X] X might
match a phrase. That phrase is best represented as a list of words as well,
so we will have an output list with embedded lists in it. A list utility,
'flatten/2',
will fix that problem.
For example if the input is [i,want,to,learn,to,fly] X will be bound to
[learn,to,fly]
and the outputs will look like [why,would,you,want,to,[learn,to,fly]]. 'flatten/2'
converts that to [why,would,you,want,to,learn,to,fly].
Here is a new version of 'form_response/2' that uses the 'response/2'
rules in the knowledge base portion of the application.
form_response(Input,Response) :-
response(InputPattern, ResponsePatterns),
match(InputPattern, Input),
random_elem(ResponsePatterns, ResponsePattern),
flatten(ResponsePattern, Response).
This rule makes use of Prolog automatic backtracking search to find the
right response rule. It first picks a 'response/2' and then uses 'match/2'
to see if its pattern matches the input. If it doesn't, then Prolog backtracks
and tries the next 'response/2' rule. When it finds one that matches,
it picks one of the possible response outputs, flattens the list and returns
the response.
Here's the recursive rules that try to match the input word list with a pattern
in one of the response rules. It will be called with the input pattern from
a rule and its job will be to see if that pattern can be matched with the user's
input. Further, if there are variables in the input pattern, then the binding
for those variables will be determined as well.
There are two boundary conditions.
--One is when the the word list for the input pattern is empty, meaning the
pattern matcher succeeded.
--The other is when the only element left in the input pattern is a variable,
in which case the pattern also succeeds and the variable is bound to whatever
is left in the user input.
There are two recursive cases.
--When the next element in the input pattern list is a variable, a separate
predicate is called to gather words from the user input up until the next
specific word in the input pattern is encountered.
--When the next element is a specific word, then if it matches the user input,
recursion continues.
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If the input doesn't match, none of the four 'match/2' rules will succeed
and the call to 'match/2' fails, triggering backtracking search for another
'response/2.'
match([], []).
match([Xs], IWs) :-
var(Xs),
!,
Xs = IWs.
match([Xs,W|PWs], IWs) :-
var(Xs),
!,
fill_var(Xs, W, IWs, IWsLeft),
match([W|PWs], IWsLeft).
match([W|PWs], [W|IWs]) :-
!,
match(PWs, IWs).
'fill_var/4' is another recursive predicate that walks the input list
looking for the first occurrence of a word that matches the word after the
variable.
It has four arguments:
--the output list of words, returned to caller;
--the next word in the input pattern that causes recursion to stop when it's
found;
--the user input list being walked, looking for the stop word;
--the remainder of the input list, returned for the caller for further
processing.
fill_var([], W, [W|IWs], [W|IWs]) :-
!.
fill_var([X|Xs], W, [X|IWs], IWsLeft) :-
fill_var(Xs, W, IWs, IWsLeft).
It is easy in Prolog to test individual predicates such as this one. When the
program is loaded in a Prolog listener, any of the predicates can be queried
directly.
?- match([a,b,c], [a,b,c]).
yes
?- match([a,X,d], [a,b,c,d]).
X = [b, c]
yes
?- match([_,like,X], [you,like,to,fly,planes]).
X = [to, fly, planes]
yes
?- match([_,like,X], [you,want,to,fly,planes]).
no
?- match([a,b,c], [a,r,c]).
no
If you are new to Prolog, the best way to learn to understand code like this
is to run it in a debugger, which will provide a trace of Prolog execution and
variable bindings. Here is a trace of 'form_response/2' from one type of
Prolog debugger.
DDJ AI Expert ChatBot Sample
> i like pizza
'CALL':user:form_response([you, like, pizza], H132)
'CALL':user:response(H321, H322)
'EXIT':user:response([H388, blue, screen, H394], [[why, do, you, use, windows,
?], [wipe, your, computer, clean, and, install, linux, .]])
'CALL':user:match([H388, blue, screen, H394], [you, like, pizza])
'FAIL':user:match([H388, blue, screen, H394], [you, like, pizza])
'REDO':user:response([H388, blue, screen, H394], [[why, do, you, use, windows,
?], [wipe, your, computer, clean, and, install, linux, .]])
'EXIT':user:response([yes, H390], [[why, ?]])
'CALL':user:match([yes, H390], [you, like, pizza])
'FAIL':user:match([yes, H390], [you, like, pizza])
'REDO':user:response([yes, H390], [[why, ?]])
'EXIT':user:response([no, H390], [[why, not, ?]])
'CALL':user:match([no, H390], [you, like, pizza])
'FAIL':user:match([no, H390], [you, like, pizza])
'REDO':user:response([no, H390], [[why, not, ?]])
'EXIT':user:response([H388, you, like, H394], [[how, can, you, like, H394, ?],
[is, it, strange, to, like, H394, ?]])
'CALL':user:match([H388, you, like, H394], [you, like, pizza])
'EXIT':user:match(['[]', you, like, [pizza]], [you, like, pizza])
'CALL':user:random_elem([[how, can, you, like, [pizza], ?], [is, it, strange,
to, like, [pizza], ?]], H348)
'EXIT':user:random_elem([[how, can, you, like, [pizza], ?], [is, it, strange,
to, like, [pizza], ?]], [how, can, you, like, [pizza], ?])
'CALL':user:flatten([how, can, you, like, [pizza], ?], H132)
'EXIT':user:flatten([how, can, you, like, [pizza], ?], [how, can, you, like,
pizza, ?])
'EXIT':user:form_response([you, like, pizza], [how, can, you, like, pizza, ?])
how can you like pizza ?
>
This trace illustrates that the rules are tried in the order they appear. So
if a user input might match multiple response patterns, the first one will have
priority.
At this point, all the code is here that is needed to run the program and get
the results displayed a few paragraphs back.
------------------------------------------
Enhancements
There are a number of enhancements that can be made to the sample.
--Implement better formatting of input and output strings.
--Put the knowledge in a separate file, so different sets of pattern matching
rules can be used. Let the user pick from a menu of rule files.
--Indexing of response rules by key words in the patterns can be added. So for
example, rules that match the word 'can' might be indexed on 'can'. This can
be used for greater efficiency if a very large number of patterns are coded,
and it can be used to easily let one pattern refer to another for completion.
--Memory of past variable bindings can be kept and popped back in from time to
time. So we might remember the user liked pizza and if nothing else matches,
have a response that uses the memory to ask a question about pizza.
--Add facts similar to the 'me_you/2' facts that can be used to map a variety
of input words into a single word used for pattern matching. For example, 'need'
might map to 'want' so the rules matching 'want' will also work if the user
types 'need'. Also it would be nice if the these facts used lists of words,
rather than being restricted to single words.
--And of course creating more and more response patterns to make the chat bot
more interesting.
------------------------------------------
Ontology
Tools for ontologies are easy to create and play with in Prolog. As always,
first a knowledge representation for the ontology is determined, and then tools
to use it are developed.
------------------------------------------
Knowledge Representation
The entries for the ontology can be stored as property lists, where each word
has a property list associated with it. A property list is just a list whose
elements are Property = Value. Using this idea for representing ontological
knowledge, here are some entries we might want in order to support an
application about
food.
word(pizza, [
kind_of = food,
contains = cheese,
contains = tomato ]).
word(broccoli, [
kind_of = food,
color = green ]).
word(donut, [
kind_of = food,
contains = sugar ]).
word(spaghetti_sauce, [
kind_of = food,
contains = tomato ]).
Here are some more entries about people and allergies.
word(joe, [
instance_of = person,
allergy = tomato ]).
word(jill, [
instance_of = person,
allergy = cheese ]).
------------------------------------------
Reasoning Engine
Now, armed with typical list utilities, we can write a predicate that can answer
questions about the properties of words in our ontology.
property(Word, Property, Value) :-
word(Word, PropertyList),
member(Property = Value, PropertyList).
Because Prolog is just matching patterns itself, the 'property/3'
predicate works no matter which variables are bound. So it can find properties
of a particular word, or words with a particular property.
Here are some things we can do with 'property/3' in the listener. (The semicolon
tells Prolog to look for another solution, so in these queries we find all
solutions.)
?- property(pizza, contains, X).
X = cheese ;
X = tomato ;
no
?- property(X, kind_of, food).
X = pizza ;
X = broccoli ;
X = donut ;
X = spaghetti_sauce ;
no
?- property(X, kind_of, food), property(X, color, green).
X = broccoli ;
no
?- findall(X, property(pizza, contains, X), L).
X = H19
L = [cheese, tomato]
yes
Applications of the Ontology
The ontology we developed can be used to support other applications. The types
of information we stored in the small sample can be used, for example, in an
expert system that warns people about what foods they can and cannot eat based
on allergies.
The rules of that expert system might be written directly in Prolog. Each of
these rules finds a person word and a food word and compares the allergy of
the person with what the food contains.
can_eat(Person, Food) :-
property(Person, instance_of, person),
property(Person, allergy, Allergy),
property(Food, kind_of, food),
not property(Food, contains, Allergy).
cannot_eat(Person, Food) :-
property(Person, instance_of, person),
property(Person, allergy, Allergy),
property(Food, kind_of, food),
property(Food, contains, Allergy).
Note that the ontology has changed the knowledge engineering task for this
allergy expert system. Without the ontology, the knowledge would all be stored
in the rules. With the ontology, the rules become simple general purpose rules
and the knowledge is stored in the ontology.
Here's how these two rules can be used.
?- can_eat(joe, pizza).
no
?- can_eat(joe, donut).
yes
?- can_eat(joe, X).
X = broccoli ;
X = donut ;
no
?- cannot_eat(joe, X).
X = pizza ;
X = spaghetti_sauce ;
no
?- can_eat(X, broccoli).
X = joe ;
X = jill ;
no
------------------------------------------
Transitivity
Transitivity is an easy enhancement to add to the ontology engine. Transitivity
applies to certain properties, such as 'kind_of.' It means that if X 'kind_of'
Y and Y 'kind_of' Z then X 'kind_of' Z. We can define certain properties
as transitive by specifying it in the knowledge base.
transitive(kind_of).
transitive(contains).
Now if we're looking for a value for a property that is transitive, our engine
can recursively call itself.
property(Word, Property, Value) :-
word(Word, PropertyList),
member(Property = Value, PropertyList).
property(Word, Property, Value) :-
transitive(Property),
word(Word, PropertyList),
member(Property = Word2, PropertyList),
property(Word2, Property, Value).
To test it, make one small change to the ontology: have pizza contain
spaghetti_sauce
instead of tomato.
word(pizza, [
kind_of = food,
contains = cheese,
contains = spaghetti_sauce ]).
The test will be to see if joe is still not allowed to eat pizza, as the pizza
does not directly contain tomato.
?- property(pizza, contains, X).
X = cheese ;
X = spaghetti_sauce ;
X = tomato ;
no
?- can_eat(joe, pizza).
no
?- cannot_eat(joe, pizza).
yes
------------------------------------------
Chat Bot with Ontology
We can use the ontology we developed to add more intelligence to the patterns
in the chat bot rules. In particular we can specify properties to include in
the pattern match. Here's a pattern that recognizes when the user is talking
about food.
response( [_,property(W,kind_of,food),_],
[ [are,you,talking,about,W,because,you,are,hungry,'?'],
[when,did,you,last,eat,W,'?'] ]).
And here's some to add to the annoying behavior of our technical support.
response( [_,property(W,kind_of,windows),_],
[ [you,should,dump,W,and,switch,to,unix,'.'] ]).
response( [_,property(W,kind_of,unix),_],
[ [you,should,dump,W,and,switch,to,windows,'.'] ]).
These last two require the addition of some more information in the ontology.
word(windows, [
kind_of = operating_system ]).
word(unix, [
kind_of = operating_system ]).
word(xp, [
kind_of = windows ]).
word(w2000, [
kind_of = windows ]).
word(linux, [
kind_of = unix ]).
And we have to make some small changes to the basic pattern matcher to recognize
and handle these property patterns in the rules. The new clauses are indicated
in red.
match([], []).
match([Xs], IWs) :-
var(Xs),
!,
Xs = IWs.
match([Xs,W|PWs], IWs) :-
var(Xs),
!,
fill_var(Xs, W, IWs, IWsLeft),
match([W|PWs], IWsLeft).
match([property(W,P,V)|PWs], [W|IWs]) :-
property(W,P,V),
!,
match(PWs,IWs).
match([W|PWs], [W|IWs]) :-
!,
match(PWs, IWs).
fill_var([], W, [W|IWs], [W|IWs]) :-
!.
fill_var([], property(W,P,V), [W|IWs], [W|IWs]) :-
property(W,P,V),
!.
fill_var([X|Xs], W, [X|IWs], IWsLeft) :-
fill_var(Xs, W, IWs, IWsLeft).
Trying the new pattern rules we get a dialog like this.
?- main.
DDJ AI Expert ChatBot Sample
> i have problmes with xp
you should dump xp and switch to unix .
> ok i just installed linux
you should dump linux and switch to windows .
> you are going in circles
i am going in circles ?
> do you wnat to get a pizza
when did you last eat pizza ?
> yesterday
yesterday ?
> yes i ate pizza yesterday
are you talking about pizza because you are hungry ?
------------------------------------------
Until next month,
Dennis Merritt
dennis at ddj.com
**Get DDJ delivered right to your door every month! Subscribe now!**
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