Keywords

Statistics; Logic Programming; Artificial Intelligence; R; Prolog

R interface

Most of the work is done using the three R functions query, submit, and clear. The R program in Listing 1 illustrates a query to Prolog’s member/2 using rolog’s syntax rules.

# member(1, [1, 2.0, a, "b", X, true])
query(call("member", 1L, list(1L, 2.0, quote(a), "b", expression(X), TRUE)))
#> [1] TRUE
#> attr(,"query")
#> [1] "member(1, [1, 2.0, a, b, X, true])"

# returns an empty list, stating that member(1, [1 | _]) is satisfied
submit()
#> list()

# returns a list with constraints, stating that the query is also satisfied 
# if the fifth element of the list, X, is 1
submit()
#> $X
#> [1] 1

# close the query
clear()

Listing 1.

A query to Prolog’s member/2 predicate.

query. The function query(call, options) is used to create a Prolog query (without invoking it yet). The first argument is a regular R call that is created using R’s function call(name, ...). This call represents the Prolog query that will be submitted in the later course. The creation of such predicates and Prolog terms is described below and can become quite contrived (see the examples in Section 4). The second argument, options, may be used for ad hoc modifications of the translation between R and Prolog, see the section below. The function returns TRUE on success. Note that query does not check if a Prolog predicate corresponding to call actually exists (see submit() below). Only a single query can be opened at a given time. If a new query Q is created while another query R is still open, a warning is shown and R is closed.

submit. Once a query has been created, it can be submitted using submit(). If the query fails, the return value is FALSE. If the query succeeds, a list of constraints is returned, with bindings for the variables that satisfy the query. Repeated calls to submit are possible, returning the different solutions of a query (until it eventually fails). The distinction between the different types of return values for success and failure (list vs. FALSE) is facilitated by the R function isFALSE(x).

clear. Closes the query. The name of the function was chosen to avoid name clashes with R’s own built-in function close. The function returns an invisible TRUE, even if there is no open query.

Three more functions consult, once, and findall are provided for convenience.

consult. In most applications, a number of Prolog facts and rules will be loaded into the system. To facilitate this recurrent task, the Prolog directive consult/1 has been mirrored into R, consult(filename), with filename being a string or a vector of strings if multiple files are to be consulted. The function returns TRUE on success; in case of problems, it returns FALSE and an error message is shown.

once and findall. The function once(call, options) is a convenience function that acts as a shortcut for query(call, options), submit(), and clear(). Similarly, findall(call, options) abbreviates the commands query(call, options), repetition of submit() until failure, and clear(), returning a list collecting the return values of the individual submissions.

Creating Prolog terms in R

Table 1 summarizes the rules for the translation from R objects to Prolog. Most rules work in both directions, but a few exceptions exist.

Table 1

Creating Prolog terms from R

In R, the basic elements such as integers, floating point numbers, character strings, and logicals are vectorized, and scalar entities are treated like vectors with one element. Conversely, Prolog does not natively support vectors or matrices. The problem is solved in the following way:

• R vectors of length 0 are translated to Prolog’s empty list.
• R vectors of length 1 are translated to Prolog scalars.
• R vectors of length N > 1 are translated to Prolog terms ##/N, %%/N, $$/N, and !!/N for floating point numbers, integers, strings and logicals, respectively.
• R matrices are translated to Prolog terms ###/R, %%%/R, $$$/R, and !!!/R with the respective row vectors as arguments.

In the reverse direction, Prolog terms like ##/N are translated back to R vectors of length N, including the terms ##/0 and ##/1 that map to R vectors of length 0 and 1, respectively. Translation of a polymorphic Prolog term such as ##(a, 1.5) to R will fail, since rolog expects the arguments to be numeric.

If a Prolog object cannot be translated to R (e.g., a cyclic term), an error is raised. If an R object that lacks a suitable representation in Prolog (e.g., S4 class), a warning is printed and the result is unified with na.

To summarize, the rules for translation are not fully symmetrical. A quick check for symmetry of the representation is obtained by a query to =/2 or even r_eval/2 (see also below, subsection Prolog interface):

Q <- call("=", expression(X), c(1, 2, NA, NaN, Inf))
once(Q, options=list(portray=TRUE))
#> $X
#> [1]   1   2  NA NaN Inf
#> 
#> attr(,"query")
#> [1] "X= ##(1.0, 2.0, na, 1.5NaN, 1.0Inf)"

Q <- call("r_eval", c(1, 2, NA, NaN, Inf), expression(X))
once(Q)
#> $X
#> [1]   1   2  NA NaN Inf

The optional argument env to query, once and findall allows to raise the query (and, as a consequence, r_eval/1,2 in a specific environment.

Package options

A few package-specific options have been defined to allow some fine-tuning of the rules for translation between R and Prolog.

• realvec (string): Name of the Prolog term for vectors of floats (default is ##)
• realmat: Name of the Prolog term for matrices of floats (default is ###)
• intvec/intmat: same for vectors/matrices of integers (defaults are %%/%%%)
• boolvec/boolmat: same for vectors/matrices of logicals (defaults are !!/!!!)
• charvec/charmat (string): same for vectors/matrices of character strings (defaults are $$/$$$). The single dollar cannot be used because it is the list operator in R.
• scalar (logical): if TRUE (default), R vectors of length 1 are translated to scalars in Prolog. If FALSE (rarely used), R vectors are always translated to ##/N, or %%/N, !!/N, $$/N, even if they have only one element.
• portray (logical): if TRUE (default in query), the result of query, once and findall includes an attribute with a text representation of the query in Prolog.
• preproc (function with one argument): R hook that can be used to preprocess R terms before translation. The default is rolog’s own preproc function that maps R’s x <= y to Prolog’s x =< y and != to \=. Preprocessing can be turned off by assigning the R function dontCheck to the preproc option.
• postproc (function with one argument): R hook that can be used to postprocess R terms after a query. The default is rolog’s own postproc function that reverses the mapping from preproc.

The command rolog_options() returns a list with all the options. The options can be globally modified with options() or in the optional argument of query, once, and findall.

options(rolog.intvec="iv")
Q <- call("member", expression(X), list(c(1L, 2L), c(3.5, 4.5)))
query(Q, options=list(realvec="rv"))
#> [1] TRUE
#> attr(,"query")
#> [1] "member(X, [iv(1, 2), rv(3.5, 4.5)])"
submit()
#> $X
#> [1] 1 2
clear()

Prolog interface to R

rolog offers some basic support to call R from Prolog, that is, connecting the two systems in the reverse direction. Two predicates can be used for this purpose, r_eval(Call) and r_eval(Function, Result). The former just invokes R with the command Call (ignoring the result); the latter evaluates Function and unifies the result with Result. Note that proper quoting of R functions is needed at the Prolog end, especially with R functions that start with uppercase letters and/or contain a dot in their name (see Section 4).

Exceptions

Package rolog has limited support for exception handling. If Prolog raises an exception, the error string is forwarded to R using the stop function. The examples below illustrate this by querying an undefined Prolog predicate.

Q <- call("membr", expression(X), list(1, 2, 3))
query(Q)
#> [1] TRUE
#> attr(,"query")
#> [1] "membr(X, [1.0, 2.0, 3.0])"
try(submit())
#> Warning in .submit():
#> error(existence_error(procedure,membr/2),context(system:call/1,_))
#> [1] FALSE
clear()

See Section 4 for another example with an error resulting from a malformed query to r_eval/2.

Preprocessing in R

rolog uses a default preprocessing function preproc(query) to map the R operators <= and != to their Prolog counterparts =</2 and \=/2, respectively.

However, we have seen above that raising even simple everyday Prolog queries such as member(X, [1, 2, 3, a, b]) require complicated R expressions like call("member", expression(X), list(1, 2, 3, quote(a), quote(b))). The R function as.rolog(query) is meant to simplify this a bit by translating symbols starting with a dot to Prolog variables, and calls like ""[1, 2, 3, a, b] to lists. In the example below, as.rolog is added to the queue of preprocessing functions.

a <- 5
Q <- quote(member(.X, ""[1, 2, 3, a, (a), 1 <= 2]))
once(Q, options=list(preproc=list(as.rolog, preproc), portray=TRUE))
#> $X
#> [1] 1
#> 
#> attr(,"query")
#> [1] "member(X, [1.0, 2.0, 3.0, a, 5.0, 1.0=<2.0])"

Note that the name of the variable will still be X in the later course, not “dot-X”. As illustrated by the example above, as.rolog treats the argument a as a symbol; to evaluate the respective variable (i.e., “unquote”), it can be put in parentheses.

Preprocessing can be turned off by setting the option preproc to the identity function dontCheck.

Section 3 includes an example for mathematical rendering of R expressions. In that example, a preprocessing function is used to bring function calls with named arguments to a canonical form which is then handled in Prolog. More sophisticated work with quasi-quotations and unquoting expressions is described in “Advanced R” (Wickham 2019).

Postprocessing in R

In most cases, postprocessing will revert the manipulations during preprocessing, and the default function postproc(query) actually translates the Prolog operators =< and \= back to their respective counterparts in R.

Many Prolog programmers are used to operate with atoms, whereas character strings are the preferred representation of symbolic information in R. In the example below, a second hook is put in the queue that converts the result of a query like member(X, [a, b, c]) to strings.

stringify <- function(x)
{
  if(is.symbol(x))
    return(as.character(x))

  if(is.call(x))
    x[-1] <- lapply(x[-1], FUN=stringify)

  if(is.list(x))
    x <- lapply(x, FUN=stringify)

  if(is.function(x))
    body(x) <- stringify(body(x))

  return(x)
}

Q <- quote(member(.X, ""[a, b, c]))
R <- findall(Q, options=list(preproc=list(as.rolog, preproc), 
       postproc=list(stringify, postproc)))
unlist(R)
#>   X   X   X 
#> "a" "b" "c"

In other words, the query is satisfied if X is either “a”, or “b”, or “c”.

Pre- and postprocessing in Prolog

Recent versions of SWI-Prolog support so-called dictionaries of the form Tag{Key1:Value1, Key2:Value2, ...}. The tag is typically an atom (but can be a variable, as well), the keys are unique atom or integers; the values can be anything. Suppose we have a Prolog predicate that does something with dicts, and we would like to query it from R. The simplest solution is a wrapper in Prolog that translates key-value pairs [Key1-Value1, Key2-Value2, ...] back and forth to dicts:

do_something_with_pairs(Pairs0, Pairs1) :-
    dict_pairs(Dict0, my_dict, Pairs0),
    do_something_with_dicts(Dict0, Dict1),
    dict_pairs(Dict1, my_dict, Pairs1).

do_something_with_pairs/2 can then be queried from R using, for example, lists with named elements (see Table 1).

once(call("do_something_with_pairs", list(a=1, b=2), expression(X)))

In the code above, dict_pairs/2 takes the role of both preproc/2 and postproc/2 in Figure 1. It illustrates that complicated syntax on the R side can be much simplified when doing the conversion at the Prolog end. Ways to extend Prolog by add-ons (“packs”) are shown in the next section.

Hello, world

Prolog’s typical hello world example is a search through a directed acyclic graph (DAG), for example, a family tree like the one given in Listing 2.

parent(pam, bob). parent(bob, ann). parent(bob, pat). parent(pat, jim).

ancestor(X, Z) :-
    parent(X, Z).

ancestor(X, Z) :-
    parent(X, Y),
    ancestor(Y, Z).

Listing 2

A family tree in Prolog (see also family.pl)

Listing 2 is included in the package and is accessed using the function system.file. Within Prolog, the normal workflow is to consult the code with [family] and then to raise queries such as ancestor(X, jim), which returns, one by one, four solutions for the variable X. In R, we obtain the following results:

library(rolog)
consult(system.file(file.path("pl", "family.pl"), package="rolog"))
query(call("ancestor", expression(X), quote(jim)))
#> [1] TRUE
#> attr(,"query")
#> [1] "ancestor(X, jim)"
submit()        # solutions for X
#> $X
#> pat
submit()        # etc.
#> $X
#> pam
clear()         # close the query

As stated above, consult loads the facts and rules of Listing 2 into the Prolog database. query initializes a query, and the subsequent calls to submit return the conditions under which the query succeeds. In this example, the query succeeds if X is either pat, pam, or bob. A query is closed with clear(), or automatically if the query fails. If we are interested in just the first solution, we can use once(Call) as a shortcut to query(Call), then submit(), then clear(). If we want to collect all solutions of a query with a finite set of solutions, we can use findall(Call).

As mentioned in Section 2, a simplified syntax is provided by as.rolog that accepts quoted expressions with dots indicating Prolog variables:

Q <- quote(ancestor(.X, jim))
findall(Q, options=list(preproc=as.rolog))

Backdoor test

A useful application of DAGs is confounder adjustment in causal analysis (Greenland, Pearl, and Robins 1999; Barrett 2021). The Prolog file backdoor.pl is an implementation of Greenland et al.’s criteria for the backdoor test for d-separation in DAGs, with a predicate minimal/3 that searches for minimally sufficient sets of variables for confounder adjustment on the causal path between exposure and outcome. The nodes and arrows refer to Figure 12 in Greenland et al.

consult(system.file(file.path("pl", "backdoor.pl"), package="rolog"))

node <- function(N) invisible(once(call("assert", call("node", N))))
node("a"); node("b"); node("c"); node("f"); node("u")
node("e") # exposure
node("d") # outcome

arrow <- function(X, Y) invisible(once(call("assert", call("arrow", X, Y))))
arrow("a", "d"); arrow("a", "f"); arrow("b", "d"); arrow("b", "f")
arrow("c", "d"); arrow("c", "f"); arrow("e", "d"); arrow("f", "e")
arrow("u", "a"); arrow("u", "b"); arrow("u", "c")

R <- findall(call("minimal", "e", "d", expression(S)))
unlist(R)
#>  S1  S2  S3   S 
#> "a" "b" "c" "f"

The query to minimal/3 returns two minimally sufficient sets of covariates for confounder adjustment (namely, {a, b, c} and {f}).

Definite clause grammars

One of the main driving forces of Prolog development was natural language processing (Dahl 1981). Therefore, the next example is an illustration of sentence parsing using so-called definite clause grammars. As Listing 3 shows, rolog can access modules from SWI’s standard library (e.g., “dcg/basics.pl”).

:- use_module(library(dcg/basics)).

s(s(NP, VP)) --> np(NP, C), blank, vp(VP, C).
np(NP, C) --> pn(NP, C).
np(np(Det, N), C) --> det(Det, C), blank, n(N, C).
np(np(Det, N, PP), C) --> det(Det, C), blank, n(N, C), blank, pp(PP).
vp(vp(V, NP), C) --> v(V, C), blank, np(NP, _).
vp(vp(V, NP, PP), C) --> v(V, C), blank, np(NP, _), blank, pp(PP).
pp(pp(P, NP)) --> p(P), blank, np(NP, _).

det(det(a), sg) --> `a`.
det(det(the), _) --> `the`.
pn(pn(john), sg) --> `john`.
n(n(man), sg) --> `man`.
n(n(men), pl) --> `men`.
n(n(telescope), sg) --> `telescope`.
v(v(sees), sg) --> `sees`.
v(v(see), pl) --> `see`.
p(p(with)) --> `with`.

% Translate R string to code points and invoke phrase/2
sentence(Tree, Sentence) :-
    string_codes(Sentence, Codes),
    phrase(s(Tree), Codes).

Listing 3

Simple grammar and lexicon. sentence/2 preprocesses the R call.

As in the first example, we first consult a little Prolog program with a minimalistic grammar and lexicon (Listing 3, see also pl/telescope.pl), and then raise a query asking for the syntactic structure of “john sees a man with a telescope”. Closer inspection of the two results reveals the two possible meanings, “john sees a man who carries a telescope” versus “john sees a man through a telescope”. Further Prolog examples of natural language processing are found in , including the resolution of anaphoric references and the extraction of semantic meaning.

consult(system.file(file.path("pl", "telescope.pl"), package="rolog"))
Q <- quote(sentence(.Tree, "john sees a man with a telescope"))
unlist(findall(Q, options=list(preproc=as.rolog)))
#> $Tree
#> s(pn(john), vp(v(sees), np(det(a), n(man), pp(p(with), np(det(a), 
#>     n(telescope))))))
#> 
#> $Tree
#> s(pn(john), vp(v(sees), np(det(a), n(man)), pp(p(with), np(det(a), 
#>     n(telescope)))))

Installation of add-ons for Prolog

In description of the previous example, we noted in passing that rolog can access the built-in libraries of SWI-Prolog (e.g., by calls to use_module/1). It is also possible to extend the installation by add-ons, including add-ons that require compilation, if the build tools (essentially, RTools under Windows, and xcode under macOS) are properly configured. This is illustrated below by the demo add-on environ (Wielemaker 2012) that collects the current environment variables.

once(call("pack_install", quote(environ), list(quote(interactive(FALSE)))))
once(quote(use_module(library(environ))))
once(call("environ", expression(X)))

The query then unifies X with a list with Key=Value terms. The purpose of this example is obviously not to mimic the built-in function Sys.getenv() from R, but to illustrate the installation and usage of Prolog extensions from within R. In most situations, the user would install the pack from within Prolog with pack_install(environ)..

Term manipulation

Prolog is homoiconic, that is, code is data. In this example, we make use of Prolog’s ability to match expressions against given patterns and modify these expressions according to a few predefined “buggy rules” (Brown and Burton 1978), inspired by recurrent mistakes in the statistics exams of our students. Consider the t-statistic for comparing an observed group average to a population mean:

$$ T = \frac{\overline{X} - \mu}{s / \sqrt{N}} $$

Some mistakes may occur in this calculation, for example, omission of the implicit parentheses around the numerator and the denominator when typing the numbers into a calculator, resulting in $\overline{X} - \frac{\mu}{s} \div \sqrt{N}$, or forgetting the square root around N, or both. Prolog code for the two buggy rules is given in Listing 4.

% Correct steps and mistakes
expert(tratio(X, Mu, S, N), frac(X - Mu, S / sqrt(N))).
buggy(frac(X - Mu, S / SQRTN), X - frac(Mu, S) / SQRTN).
buggy(sqrt(N), N).

% Apply expert and buggy rules, or enter expressions
step(X, Y) :-
    expert(X, Y) ; buggy(X, Y).
step(X, Y) :-
    compound(X),
    mapargs(search, X, Y),
    dif(X, Y).

% Search through problem space
search(X, X).
search(X, Z) :-
    step(X, Y),
    search(Y, Z).

Listing 4

Manipulating terms in Prolog

The little e-learning system shown in Listing 4 produces six response alternatives. The fourth and the sixth result are combinations of the same two buggy rules (parenthesis, then square root, and the other way round). Some additional filters would be needed to eliminate trivial and redundant solutions .

consult(system.file(file.path("pl", "buggy.pl"), package="rolog"))
Q <- quote(search(tratio(x, mu, s, n), .S))
unlist(findall(Q, options=list(preproc=as.rolog)))
#> $S
#> tratio(x, mu, s, n)
#> 
#> $S
#> frac(x - mu, s/sqrt(n))
#> 
#> $S
#> x - frac(mu, s)/sqrt(n)
#> 
#> $S
#> x - frac(mu, s)/n
#> 
#> $S
#> frac(x - mu, s/n)
#> 
#> $S
#> x - frac(mu, s)/n

An important feature of such a term manipulation is that the evaluation of the term can be postponed; for example, there is no need to instantiate the variables x, mu, s, and n with given values before raising a query. This is especially helpful for variables that may represent larger sets of data in later steps.

It should be mentioned that R is homoiconic, too, and the Prolog code above can, in principle, be rewritten in R using non-standard evaluation techniques (Wickham 2019). Prolog’s inbuilt pattern matching algorithm simplifies things a lot, though.

Rendering mathematical expressions

The R extension of the markdown language (Xie, Dervieux, and Riederer 2020) enables reproducible statistical reports with nice typesetting in HTML, Microsoft Word, and Latex. However, so far, R expressions such as pbinom(k, N, p) are typeset as-is; prettier mathematical expressions such as P_Bi(X ≤ k; N, p) require Latex commands like P_\mathrm{Bi}\left(X \le k; N, p\right), which are cumbersome to type in and hard to read even if the expressions are simple. Since recently, manual pages include support for mathematical expressions (Sarkar and Hornik 2022), which already is a big improvement.

Below Prolog’s grammar rules are used for an automatic translation of R calls to MathML. The result can then be used for calculations or it can be rendered on a web page. A limited set of rules for translation from R to MathML is found in pl/mathml.pl of package rolog. A more comprehensive translator is provided by the R package mathml (Gondan 2022). The relevant code snippets are shown in the listings below, along with their output.

library(rolog)
consult(system.file(file.path("pl", "mathml.pl"), package="rolog"))

# R interface to Prolog predicate r2mathml/2
mathml <- function(term)
{
  t <- once(call("r2mathml", term, expression(X)))
  cat(paste(t$X, collapse=""))
}

Listing 4

Generate MathML from R expressions

The first example is easy. At the Prolog end, there is a handler for pbinom/3 that translates the term into a pretty MathML syntax like P_bi(X <= k; N, pi).

term <- quote(pbinom(k, N, p))

# Pretty print
mathml(term)

$P_{\text{Bi}}\left(X\le k;N,p\right)$

# Do some calculations with the same term
k <- 10
N <- 22
p <- 0.4
eval(term)

[1] 0.77195

The next example is interesting because Prolog needs to find out the name of the integration variable for sin. For that purpose, rolog provides a predicate r_eval/2 that calls R from Prolog (i.e., the reverse direction, see also next example). Here, the predicate is used for the R function formalArgs(args(sin)), which returns the name of the function argument of sin, that is, x.

term <- quote(integrate(sin, 0L, 2L*pi))
mathml(term)

$\underset{0}{\overset{2\pi }{\int }}\sin \left(x\right)dx$

eval(term)

2.221501e-16 with absolute error < 4.4e-14

Note that the Prolog end, the handler for integrate/3 is rather rigid; it accepts only these three arguments in that particular order, and without names, that is, integrate(sin, lower=0L, upper=2L * pi) would not print the desired result.

The extra R function canonical() applies match.call() to non-primitive R calls, basically cleaning up the arguments and bringing them into the correct order. Moreover, an extra handler maps the extractor function $(Fn, "value") to Fn.

canonical <- function(term)
{
  if(is.call(term))
  {
    f <- match.fun(term[[1]])
    if(!is.primitive(f))
      term <- match.call(f, term)
    
    # Recurse into arguments
    term[-1] <- lapply(term[-1], canonical)
  }

  return(term)
}

g <- function(u)
  sin(u)

# Mixture of (partially) named and positional arguments in unusual order
term <- quote(2L * integrate(low=-Inf, up=Inf, g)$value)
mathml(canonical(term))

$2\cdot \underset{-\infty }{\overset{\infty }{\int }}g\left(u\right)du$

# It is a bit of a mystery that R knows the result of this integral.
eval(term)

[1] 0

Note that both sin nor g in the above terms are R symbols, not R functions. In order to render something like call("integrate", low=-Inf, up=Inf, g), or call("integrate", low=-Inf, up=Inf, sin), with g and sin referring to the respective functions, one would need to determine its name, which is not possible in general.

print(g)
#> function (u) 
#> sin(u)
#> <bytecode: 0x5567628c12f0>

Calling R from Prolog

The basic workflow of the bridge from R to Prolog is to (A) translate an R expression into a Prolog term (i.e., a predicate), (B) query the predicate, and then, (C) translate the result (i.e., the bindings of the variables) back to R (see also Figure 1). The reverse direction is straightforward, we start by translating a Prolog term to an R expression (i.e. Step C), evaluate the R expression, and then translate the result back to a Prolog term (Step A). Package rolog provides two predicates for that purpose, r_eval(Expr) and r_eval(Expr, Res). The former is used to invoke an R expression Expr for its side effects (e.g., initializing a random number generator); it does not return a result. The latter is used to evaluate the R expression and return the result Res. The code snippet in Listing 6 (r_eval.pl) illustrates this behavior.

r_seed(Seed) :-
    r_eval('set.seed'(Seed)).

r_norm(N, L) :-
    r_eval(rnorm(N), L).

Listing 6

Calling R from Prolog using r_eval/1 and r_eval/2. The R call set.seed is quoted because the dot is an operator in Prolog.

consult(system.file(file.path("pl", "r_eval.pl"), package="rolog"))
invisible(once(call("r_seed", 123L)))
once(call("r_norm", 3L, expression(X)))
#> $X
#> [1] -0.5604756 -0.2301775  1.5587083

The example in Listing 6 is a bit trivial, basically illustrating the syntax and the workflow. More serious applications of are shown in the next two sections where r_eval/2 is used to evaluate monotonically behaving R functions and to obtain the names of function arguments in R.

As show below, the default environment of rolog’s r_eval/2 is .GlobalEnv, this can be changed in an optional argument to once(), findall(), and query().

# Set variable in R, read in Prolog
env <- new.env()
with(env, a <- 1)
once(call("r_eval", quote(a), expression(X)), env=env)
#> $X
#> [1] 5

# Set R variable in Prolog, read in R
invisible(once(call("r_eval", call("<-", quote(b), 2))))
cat("b =", b)
#> b = 2

If the R call raises an exception, an error is propagated to Prolog and finally to the rolog package:

#try(once(quote(r_eval(rnorm(-1))))) # return "-1" random normals

Interval arithmetic

Let ⟨ℓ, u⟩ denote a number between ℓ and u, ℓ ≤ u. It is easily verified that the result of the difference ⟨ℓ₁, u₁⟩ − ⟨ℓ₂, u₂⟩ is somewhere in the interval ⟨ℓ₁ − u₂, u₁ − ℓ₂⟩, and a number of rules exist for basic arithmetic operations and (piecewise) monotonically behaving functions (Hickey, Ju, and Emden 2001). For ratios, denominators with mixed sign yield two possible intervals, for example, ⟨1, 2⟩/⟨−3, 3⟩ = ⟨−∞, 3⟩ ∪ ⟨3, ∞⟩, as shown in Figure 4 in Hickey et al.’s article. The number of possible candidates increases if more complicated functions are involved, as unions of intervals themselves appear as arguments (e.g., if I₁ ∪ I₂ is added to I₃ ∪ I₄, the result is I₁ + I₃ ∪ I₁ + I₄ ∪ I₂ + I₃ ∪ I₂ + I₄). As a consequence, calculations in interval arithmetic are non-deterministic in nature, and the number of possible results is not foreseeable and cannot, in general, be vectorized as is often done in R. Use cases for interval arithmetic are the limitations of floating-point representations in computer hardware, but intervals can also be used to represent the result of measurements with limited precision, or truncated intermediate results of students doing hand calculations. A few rules for basic interval arithmetic are found in pl/interval.pl; a few examples are shown below. Again, Prolog rings back to R via r_eval/2 to determine the result of dbinom(X, Size, Prob, Log).

#consult(system.file(file.path("pl", "interval.pl"), package="rolog"))

#Q <- quote(int(`...`(1, 2) / `...`(-3, 3), .Res))
#unlist(findall(Q, options=list(preproc=as.rolog)))

#D  <- quote(`...`(5.7, 5.8))
#mu <- 4
#s  <- quote(`...`(3.8, 3.9))
#N  <- 24L
#tratio <- call("/", call("-", D, mu), call("/", s, call("sqrt", N)))
#once(call("int", tratio, expression(Res)))

# Binomial density
#prob = quote(`...`(0.2, 0.3))
#once(call("int", call("dbinom", 4L, 10L, prob, FALSE), expression(Res)))

The slightly cumbersome syntax for entering an interval ⟨ℓ, u⟩ is due to the fact that the ellipsis is a reserved symbol in R and cannot be used as an infix operator. A powerful and comprehensive system for constraint logic programming over intervals is available as a Prolog pack (Workman 2021) and can easily be connected to R using, for example, the present package.