NEM 2010-07-25: Following on from a little database with unification, Playing Prolog and Playing Predicate Logic, here is a more fully fleshed-out (but still lacking many features) Prolog interpreter, loosely based on that given in Chapter 11 of Paradigms of Artificial Intelligence Programming, but optimised and adapted considerably for Tcl.

The interpreter here supports basic backtracking (depth-first search) resolution of Horn clauses in a simple interpreter. However, it currently doesn't support negation-as-failure, disjunction, or any of the built-in predicates that make Prolog a full-featured language. It also is a toy interpreter rather than a compiler, so performance will not be anywhere near competitive with modern Prolog implementations. However, it suffices to demonstrate the principles.

# prolog.tcl -- # # Prolog interpreter in Tcl. Loosely based on Chapter 11, "Paradigms of # Artificial Intelligence Programming", Peter Norvig. # # Copyright (c) 2010 by Neil Madden # License: Same as Tcl (BSD-style). # package require Tcl 8.5 package provide prolog 0.1 namespace eval ::prolog { namespace export function relation assert query namespace ensemble create }

The syntax of Prolog is based on a restricted form of first-order predicate logic using Horn clauses. A Horn clause is a disjunction ("OR") of literals with at most one positive atom. A literal here is either an atomic formula (such as *parent(x,y)*) or its negation. The arguments to an atomic formula are *terms*, which are either function symbols or variables. Real Prolog implementations usually also support numbers, strings and other concrete datatypes.

Note: function *symbols* are distinct from *functions*. Function symbols are uninterpreted structures consisting of an initial symbol (the *functor*) such as *father* followed by a tuple of arguments, e.g., *father(mary)* is a function symbol, which may denote the father of Mary. (Whether it does or not depends on the interpretation you give these symbols). Function symbols generally denote individuals or objects, whereas formulas denote statements which are either true or false.

Translating this to Tcl, we will represent variables by strings of the form *?xyz* and function symbols will be represented by a list where the first element is the functor and the remaining elements are the argument terms. Constants (0-arity function symbols) are therefore just lists with a single element. This representation allows us to represent arbitrary string constants and keep them distinct from other function symbols. The only clash we have is that strings that begin with a question-mark will always be interpreted as variables. This is a minor drawback, which seems unlikely to be a problem in practice.

# Terms: # # A term is either a variable or a function symbol. # proc prolog::var? s { string match {\?*} $s } proc prolog::fun? s { expr {![var? $s]} }

In addition to normal variables, we will also allow a lone question-mark as a "wild-card" match-anything variable, which can be useful when you do not care what the value of that variable is.

proc prolog::wildcard? s { string equal $s "?" }

In order to make working with function symbols easier, we can provide some getter functions to access some basic properties. This also allows us to easily change the representation at a later date if we wish. The properties we are interested in are the *functor*, the *arguments* and the *arity* (number of arguments) of the symbol.

# functor symbol # # Returns the functor of a function symbol. # proc prolog::functor symbol { lindex $symbol 0 } # arguments symbol # # Returns the argument terms of an atomic formula or function symbol. # proc prolog::arguments symbol { lrange $symbol 1 end } # arity symbol # # Returns the arity (number of arguments) of a symbol. # proc prolog::arity symbol { expr {[llength $symbol] - 1} }

Additionally, to make function symbols easier to construct, we can introduce a means to declare constructor commands for function symbols.

# function name ?args ...? # # Creates a constructor command for function symbols. # proc prolog::function {name args} { set name [qualify 1 $name] interp alias {} $name {} ::prolog::symbol $name $args return $name } # symbol name params ?args ...? # # Constructs a symbol with the given name and arguments. # proc prolog::symbol {name params args} { if {[llength $params] != [llength $args]} { return -code error "wrong # args: should be \"$name $params\"" } return [linsert $args 0 $name] } # qualify level name # # Returns a fully-qualified version of $name, relative to the # current namespace at level $level (where $level is a level # acceptable to [uplevel]). This command differs from [namespace # which] in that it is intended for constructing fully-qualified names # for commands which have yet to be created. # proc prolog::qualify {level name} { if {[string match ::* $name]} { return $name } if {[string is integer -strict $level] && $level >= 0} { incr level } set ns [uplevel $level { namespace current }] if {$ns eq "::"} { return ::$name } return $ns\::$name }

This allows us to write code such as the following, which makes constructing complex terms simpler in many cases.

prolog function father x set y [father mary] ;# => {::father mary} set z [father $y] ;# => {::father {::father mary}}

Given terms, we can now build up formulas. An atomic formula (or atom) is similar to a function symbol in that it consists of a predicate symbol and a list of arguments. For example, parent(john,mary) might state that John is a parent of Mary. We can reuse the representation of function symbols for atoms:

interp alias {} prolog::atom {} prolog::symbol interp alias {} prolog::predicate {} prolog::functor

Real Prolog would then extend this syntax to literals, allowing negation, but for now we stick with simple positive atoms. The final piece of syntax is a clause, which is a disjunction of literals (i.e., a OR b OR c...). Prolog restricts these to Horn clauses, with at most one positive atom (i.e., NOT a or NOT b OR c). Such clauses can always be written in the form: a AND b => c ("a and b implies c"), which Prolog writes as c :- a, b. The following procedure will construct a clause, ensuring correct syntax.

# clause head ?:- body...? # # Constructs a (Horn) clause # proc prolog::clause {head args} { if {[llength $args] == 1} { return -code error \ "wrong # args: should be \"clause head ?:- body...?\"" } if {[llength $args] > 0 && [lindex $args 0] ne ":-"} { return -code error \ "invalid syntax: expecting ':-' but got '[lindex $args 0]'" } return [list $head {*}[lrange $args 1 end]] } proc prolog::head clause { lindex $clause 0 } proc prolog::body clause { lrange $clause 1 end }

The core of any logic programming implementation is the unification algorithm. This algorithm has been discussed elsewhere on this wiki before, so we just present the core algorithm here with little commentary. The code is fairly well commented, so shouldn't be too hard to decipher.

# Variable binding environments are implemented as dictionaries proc prolog::lookup {env var} { dict get $env $var } proc prolog::extend {env var val} { dict set env $var $val } proc prolog::bound? {env var} { dict exists $env $var } # fail # # Signals a unification failure. Failure is indicated with the # exception return code 3245 ("fail" on a telephone keypad). # proc prolog::fail {} { return -code 3245 "unification failure" } # unify x y ?env? # # Unifies two symbols by finding substitutions for free variables in # each formula/term which would make the terms equal. If successful, # returns the successful substitution, otherwise throws a unification # failure exception. # proc prolog::unify {x y {env ""}} { if {$x eq $y} { return $env } ;# cheap check if {[var? $x]} { return [unify-var $x $y $env] } if {[var? $y]} { return [unify-var $y $x $env] } # Check arity and functors are identical first if {[arity $x] != [arity $y] || [functor $x] != [functor $y]} { fail } # Attempt to unify each pair of arguments in turn foreach xarg [arguments $x] yarg [arguments $y] { set env [unify $xarg $yarg $env] } # Success! return $env } # unify-var x y env # # Unify a variable with another value. # proc prolog::unify-var {x y env} { # If either term is an already-bound variable, then retry unification # after performing this substitution. if {[bound? $env $x]} { return [unify [lookup $env $x] $y $env] } if {[bound? $env $y]} { return [unify $x [lookup $env $y] $env] } # Otherwise, create a new binding for this variable. # First, perform the occurs-check to ensure that we are not creating an # infinite structure. if {[occurs-in $x $y]} { fail } return [extend $env $x $y] } # occurs-in var exp # # Returns 1 if the given var occurs anywhere within the term exp, # otherwise returns 0. This is the expensive "occurs check", which is # usually omitted in Prolog for efficiency. If we leave out this # check, then there is a possibility of creating infinite structures, # e.g., from unifying ?x and [f ?x], which will create the structure # [f [f [f ...]]]. This would then result in an infinite loop when # resolving the structure. # proc prolog::occurs-in {var exp} { if {$var eq $exp} { return 1 } foreach arg [arguments $exp] { if {[occurs-in $var $arg]} { return 1 } } return 0 }

A predicate can be seen as denoting a relation (as in relational databases), which contains all tuples of individuals that satisfy the predicate. For example, the predicate *parent* is true if the first argument individual is a parent of the second argument individual. The corresponding relation consists of all pairs of individuals that satisfy this relationship, e.g., our parent predicate might be represented by the relation *parent* = { (John, Mary), (Susan, Jill), ... } (where John is a parent of Mary, Susan a parent of Jill, and so on).

In contrast to the earlier a little database with unification, we here adopt an approach of storing data in individual relations rather than in a database. This reduces the boilerplate a little as you now no longer need to create a database before creating relations, but as the relations are namespaced there is no loss of modularity. Each relation consists of a command (similar to the commands for constructing function symbols), and a variable of the same name that contains the data. The data itself is stored as a list of clauses, with new clauses added to the end of the relation.

# relation name ?args ...? # # Creates a relation and a command for constructing elements of that # relation. # proc prolog::relation {name args} { set name [qualify 1 $name] interp alias {} $name {} ::prolog::atom $name $args set $name [list] trace add command $name delete ::prolog::DeleteRelation return } proc prolog::DeleteRelation {name args} { upvar #0 $name relation unset relation } # assert head ?:- body ...? # # Asserts a clause is true (adds it to the relation of the head). # proc prolog::assert args { set clause [clause {*}$args] set pred [predicate [head $clause]] upvar #0 $pred relation lappend relation $clause return } # clauses predicate # # Returns a list of all clauses in the relation denoted by the predicate. # proc prolog::clauses predicate { upvar #0 $predicate relation return $relation }

The interpretation of Prolog consists of answering queries by finding facts which are implied by the clauses we have asserted which satisfy the query. We can see this as being a bit like querying a database in SQL, but in Prolog we can do much more sophisticated logical inference to answer the questions. The basic approach is based on a variation of the resolution rule of inference developed for classical predicate logic. Essentially, we employ a depth-first search technique, which tries to find a clause whose head unifies with the goal in the query. We then adopt any sub-goals in the body of that clause as new goals and repeat the process. If we manage to solve all of these goals in a consistent manner, then we have found a solution to our original query. If we fail, then we *back-track* to a previous solution and try a different clause. This approach broadly corresponds to proof by contradiction, and is reasonably efficient in time and memory usage. The current implementation, of course, misses many possible optimisations, and so is by no means production-ready. However, it has the advantage of simplicity.

The first step then in our search is to find all clauses that unify with our goal. The following procedure expands a goal into a list of sub-goals which are candidates for a solution. We rename all variables in each candidate clause before attempting unification, to ensure that variables in distinct clause instances are distinct, even if they have the same name. Otherwise, some solutions will be missed due to variable name clashes.

# expand goal solution rest # # Returns a list of new possible solutions to $goal. Each element of the # list returned consists of a partial solution and a list of further goals # yet to be solved for this solution. # proc prolog::expand {goal partial rest} { set subgoals [list] foreach clause [clauses [predicate $goal]] { set clause [rename-vars $clause] try { unify $goal [head $clause] $partial } on [success] solution { lappend subgoals [list $solution [list {*}[body $clause] {*}$rest]] } on [failure] {} { continue } } return $subgoals } # sugar to make [try] look nicer :-) proc prolog::success {} { return 0 } proc prolog::failure {} { return 3245 } # rename-vars clause # # Renames all variables in the given clause to be globally-unique. # All variables with the same name in the expression (e.g., ?a) will # be renamed to the same unique variable name (e.g., ?a123). All # wildcard variables (?) are renamed to completely distinct variables. # This process ensures that variables in distinct clauses are kept # distinct. # proc prolog::rename-vars clause { set scope [dict create] set result [list] foreach literal $clause { lappend result [rename-vars-in $literal scope] } return $result } proc prolog::rename-vars-in {exp envVar} { upvar 1 $envVar env if {[wildcard? $exp]} { return [gensym "?"] } if {[var? $exp]} { if {![dict exists $env $exp]} { dict set env $exp [gensym $exp] } return [dict get $env $exp] } if {[llength $exp] > 1} { set res [list [predicate $exp]] foreach arg [arguments $exp] { lappend res [rename-vars-in $arg env] } return $res } return $exp } # gensym prefix # # Generates a unique string starting with the given prefix. # proc prolog::gensym prefix { return [format "%s%d" $prefix [incr ::prolog::gensym($prefix)]] }

The remainder of the algorithm is then a simple state-space search, similar to state space searching in Tcl. The basic approach is to start with a set of goals (a query) to be solved. Given a query, which consists of a set of goals (atoms with free variables), we try to find a solution that satisfies all of the goals together. We do this by maintaining a queue of search nodes, which initially consists of just our original goals. We then pop a node at a time from the queue and expand it, creating a new list of nodes to explore. We use a LIFO queue (stack) here to get depth-first search behaviour as in Prolog. You could try a FIFO queue to get breadth-first searching, or perhaps try one of the more exotic heuristic searches. Each node in our search space consists of the partial solution found so far (a set of variable bindings) and a list of goals yet to solve. If we find a node that has no more goals to solve, then we have solved our original query. If not then we try the next partial solution. Once we find a solution, we clean-up the solution, resolving all temporary variables used in solving sub-goals, and then execute a user-supplied script, providing a dictionary of bindings.

# query goal ?goal ...? rowVar body # # Queries the database for solutions to the given goals (taken as a # conjunction). For each solution found, rowVar is set to a dictionary # of the bound variables and then body is executed in the caller's # scope. As in other loop constructs, if the body returns a [break] # then further results are skipped, and a [continue] can be used to # retrieve the next result. # proc prolog::query args { if {[llength $args] < 3} { set usage "prolog query goal ?goal ...? rowVar body" return -code error "wrong # args: should be \"$usage\"" } set body [lindex $args end] set rowVar [lindex $args end-1] set goals [lrange $args 0 end-2] upvar 1 $rowVar row set vars [vars $goals] # Perform a depth-first search to find solutions to the goals # Nodes in the queue consist of a environment (partial solution) and a # list of goals yet to be solved. set queue [list [list {} $goals]] while {[llength $queue]} { set node [pop queue] lassign $node solution goals if {[llength $goals] == 0} { # Found a complete solution: execute the loop body set row [cleanup $vars $solution] try { uplevel 1 $body } on break {} { break } on continue {} { continue } } else { # Still more goals to solve: solve the first set goal [pop goals] push queue {*}[expand $goal $solution $goals] } } } # pop queueVar # # Pops an element off the end of a LIFO queue (stack). # Note: change this if you want something other than depth-first # search (e.g., change to FIFO for breadth-first). # proc prolog::pop queueVar { upvar 1 $queueVar queue set item [lindex $queue end] # Inline [K] trick to ensure unshared queue set queue [lrange $queue [set queue 0] end-1] return $item } # push queueVar element ?...? # # Pushes one or more elements on to the end of a LIFO queue. # proc prolog::push {queueVar args} { upvar 1 $queueVar queue lappend queue {*}$args } # cleanup vars env # # Cleans up the variables in a solution, ensuring all variables that # were present in the query are resolved as much as possible in the # result. # proc prolog::cleanup {vars env} { set ret [dict create] foreach var $vars { set val $var while {[dict exists $env $val]} { set val [dict get $env $val] } set var [string trimleft $var ?] dict set ret $var $val } return $ret } # vars goals # # Returns a list of the free variables present in a list of goals. # proc prolog::vars goals { set vars [list] foreach goal $goals { if {[var? $goal] && ($goal ni $vars)} { lappend vars $goal } else { foreach arg [arguments $goal] { lappend vars {*}[vars $arg] } } } return $vars }

So, that's our core Prolog interpreter implementation. The next steps would be to explore optimisations, or enhancements. Simple optimisations can come from use of memoizing (e.g., on the "expand" procedure). Enhancements would be support for things like negation-as-failure, disjunction, datatypes, and built-in predicates.

Some examples, showing the use of the software:

proc ? args { prolog query {*}$args row { set result [list] dict for {var val} $row { lappend result "$var = $val" } puts [join $result ", "] } } prolog relation father x y prolog relation mother x y prolog relation parent x y prolog assert [father john mary] prolog assert [mother mary bob] prolog assert [father peter bob] prolog assert [father bob susan] puts "Fathers:" ? [father ?x ?y] prolog assert [parent ?x ?y] :- [mother ?x ?y] prolog assert [parent ?x ?y] :- [father ?x ?y] puts "Parents:" ? [parent ?x ?y] prolog relation ancestor x y prolog assert [ancestor ?x ?y] :- [parent ?x ?y] prolog assert [ancestor ?x ?y] :- [parent ?x ?z] [ancestor ?z ?y] puts "Ancestors:" ? [ancestor ?x ?y] puts "Susan's ancestors:" ? [ancestor ?a susan] puts "Mary's descendents:" ? [ancestor mary ?d]

And the output:

Fathers: x = bob, y = susan x = peter, y = bob x = john, y = mary Parents: x = bob, y = susan x = peter, y = bob x = john, y = mary x = mary, y = bob Ancestors: x = john, y = susan x = peter, y = susan x = john, y = bob x = mary, y = susan x = bob, y = susan x = peter, y = bob x = john, y = mary x = mary, y = bob Susan's ancestors: a = john a = peter a = mary a = bob Mary's descendents: d = susan d = bob

NEM 2010-07-26: Fixed a bug where variables in clauses were renamed incorrectly. We need to rename variables in clauses in *expand* rather than *assert* as the same clause can be used more than once in a single execution, and the variables in each instance should also be distinct from each other to avoid clashes.