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DLT is a front-end for disjunctive datalog, extending the DLV system with Template predicates, Frame Logic and higher order predicate names. Template predicates can be seen as a way to define intensional predicates by means of a subprogram, where the subprogram is generic and reusable as many times is necessary. Frame Logic extends classical logic and enables frame-based or object-oriented syntax. Higher order predicates allow to treat predicate names as variables (although this semantics has quite nothing to deal with second order logics).

More information (including an online version of this manual and an online tutorial) and the executable program for Linux platform are available at the DLT homepage (http://dlt.gibbi.com).<<BR>>

Chapter 1. Getting Started

We can invoke the DLT system directly from the command-line, after the prompt of the system, as well the DLV system. Note that DLT relies on an external solver ES, which takes an input generated by DLT. Models generated by ES are then parsed back to the DLT syntax. ES can be chosen from a variety of solvers (see the Compatibility Table below)

If you do not specify any options or files, DLT will just print some informational output:

 > ./dlt 

DLT. Disjunctive Logic Framework with Templates and more
[V1.6 - Build BEN/Jul 28 2008   gcc 4.0.1 (Apple Inc. build 5465)]

usage:  ./dlt {FRONTEND} {OPTIONS} [filename [filename [...]]]

Specify -help for more detailed usage information.

The first line shows, first of all, the name of the executable program (DLT), then it gives information about the version of the executable program, the date when the binary was built and the identifier of the compiler. The last three lines give a brief explanation on how to use dlt, the last line informing the user of the -help option for futher instructions.
The -help or -h option

 > ./dlt -help     or     > ./dlt -h

gives the following output:

DLT. Disjunctive Logic Framework with Templates and more
[V1.6 - Build BEN/Jul 28 2008   gcc 4.0.1 (Apple Inc. build 5465)]

usage:  ./dlt {FRONTEND} {OPTIONS} [filename [filename [...]]]

DLT Specific options:

--higherorder               Enables higher order predicate names
--solver=[path_name]        The path of the solver executable file 
                            For example: --solver=/dlv/dl
--preparsing                Print out re-written code and exit
--postparsing               Maps higher-order atoms into first-order atom
--framespace                Activates displaying for framespace directives
--uri                       Enables more verbose output for URI constants
--filter=name1,..,nameN     Output only instances of the specified predicates
--pfilter=name1,..,nameN    Output only positive of the  instances 
                            specified predicates
--solverparams={options}    The specific solver options 
                            For example: -solverparams={-v -nofacts}
--aritycheck                Forces to check same arity for the same
                            predicate name
--safetycheck               Forces to check safety of rule's head variables

If you do not want to see this status line, use the -silent option, which suppresses various informational output and blank lines.

{{{ usage: ./dlt {FRONTEND} {OPTIONS} [--higherorder] [--solver=<path>] [--preparsing] [--postparsing] [--framespace] [--uri] [--filter=<predicate>] [--pfilter=<predicate>] [--silent] [--aritycheck] [--safetycheck] [--solverparams={options_solver}] [filename [filename [...]]]

Specify -help for more detailed usage information.


Section 1.1. DLT Specific options

The following table shows the specific options of DLT.

note: The –filter e –pfilter options are inherited by DLV, but they must to be considered as specific options of DLT. Indeed, DLT can change the predicate names before passing them to DLV ( DLT set them up again it in the post-parsing phase ). Thus, it’s necessary that DLT performs filtering operations directly, before printing the results.




Considers the names of the predicates as variables


Allows to specify the path of the solver


Prints to standard output the program rewritten by DLT ans stops without invoking the solver


Receives the output of the solver from standard input and maps data to the original DLT format (before rewriting)


Turns on filtering. Predicates pred (or true negated -pred ) will be the only included in the output. This option may be used multiple times, and pred may also be a comma-separated list of predicate names.


Turns on filtering. Positive instances of predicate pred will be included in the output. This option may be used multiple times, and pred may also be a comma-separated list of predicate names.


In the curly brackets the user may pass a space separated list of options to be passed to the solver.


Do not output information about the version of the executable program and the compiler.


Forces to check same arity for the same predicate name


Forces to check safety of rule's head variables

-help or -h

Gives a detailed explanation on how to use DLT.

Chapter 2. DLP^T Language

DLT's native language is DLV extended with Template predicates and other constructs. We remember that DLV's native language is Disjunctive Datalog extended with constraints, true negation, aggregates and queries.

How to write a DLPT program

For a correct writing and usage of the DLPT language here are a few general conditions that must be satisfied.

Section 2.1. Writing Template Definition

A template definition D consists of a template header and an associated DLPT subprogram closed into curly braces. We consider a generic definition of template:

#template template_name { p1( a1 ) pn ( an )}(arity)

    < Body of Template >


All the following conditions must be respected:

Example 2-1. Correct Template Definitions

c1: #template max{p(1)}(1)

   max(X) :- p(X), not exceeded(X).
   exceeded(X) :- p(X), p(Y), Y > X.


c2: #templatemax{p(1)}(1)GLOBAL node

    max(X) :- p(X), not exceeded(X).
    exceeded(X) :- p(X), p(Y), Y > X.


Example 2-2 Wrong Template Definitions

t1: #template wrong_definition1{p(1)}(1)

    exceeded(X) :- p(X), p(Y), Y > X.


t2: #template wrong_definition2{-p(1), not c(1)}(1)

    < Body of Template Definition >


t1 is a wrong template definition because the formal parameter pcannot appear in the head of a rule.

t2 is a wrong template definition because the formal parameter p is preceded from the true negation symbol, while the formal parameter c is preceded from the negation as failure symbol.

Example 2-3. The following is a template definition:

#template max{p(1)}(1)

   max(X) :- p(X), not exceeded(X).
   exceeded(X) :- p(X), p(Y), Y > X.


This template introduces a generic template program, defining the predicate max, intended to compute the maximum value over the domain of a generic unary predicate p. In this example we have that

#template max{p(1)}(1)

is the template header, and


   max(X) :- p(X), not exceeded(X).
   exceeded(X) :- p(X), p(Y), Y > X.


is a DLPT subprogram. max is the template name; p is a predicate name, called formal predicate. The arity of formal predicate p is 1 and the output arity of template definition is 1.

Section 2.2. Writing Template Atoms

We consider a generic definition of template:

template_name{ p1(X1),..., pn(Xn)}(Args)

All the following conditions must be respected:

Example 2-4 Correct Template Atoms

c1: template_name{-person($,*,*)}(X).

c2: template_name{person($,*,*)}(X).

c3: template_name{person(Name,*,*)}(X).

c4: template_name{-person(Name,*,*)}(X).

Example 2-5 Wrong Template Atoms

We consider the template definition of the Example 2-3(definition of template max). The following are wrong template atoms:

a1: maxperson(Name,*,*)}(X,Y,Z).

a2: max{not person(Name,*,*)}(X).

a3: max{person(Name,*,*), person(*,*,$)}(X).

a1 is a wrong template atom because the arity of the output terms is not equal to the output arity of the correspondent definition.

a2 is a wrong template atom because the actual parameter person is preceded by negation as failure.

a3 is a wrong template atom because the number of actual predicates is not equal to the number of formal predicates of the correspondent definition.

Note: The DLPT subprogram may contain disjunctive rules, constraints, weak constraints, aggregates, built-in predicates, negative literals, true negation, template atoms (or invocations), and it do not contains template definitions.

Section 2.3. Negative Rules with Template Atoms and True Negation

Template atoms can be used also as negated and negative atoms in the generic rules of the program. Then we can use a template atom after the negation as failure symbol (not) as it happens for the usual atoms.

Example 2-6. The following is an example of negated and negative template atom, respectively.

not max{student(Sex,$,*)}(Age)

Negation for template atoms is treated as well negation for DLV atom. For more information about negation as failure and true negation we send back at the DLV user manual available at the DLV homepage (http://www.dbai.tuwien.ac.at/proj/dlv/).

Section 2.4. Template Definition with Global Predicates

Template programming can become a very useful feature in applications where it is necessary to compact repetitive pieces of code. We consider a planning program for the Space Shuttle Reaction Control System. Here is a sketch from the program:

#template ready_to_fire_conditions{propulsor_type(2)}(2)
GLOBAL time_slot, tank, is_pressurized_by, is_damaged

   ready_to_fire_conditions(J,T) :-propulsor_type(J,R),
   tank(TK2,R), TK1 !=TK2,
   not is_damaged(J).


the above template is employed to remove a set of repeated rules, which are changed this way

fire_jet(J,T) :- ready_to_fire_conditions{jet(*,*)}(J,T).

fire_vernier(J,T) :- ready_to_fire_conditions{vernier(*,*)}(J,T).

Section 2.5. Group-by

A template definition may be instantiated as many times as necessary, trough template atoms. The template atom or invocation consists of a template name, a list of atom and a list of variables and/or constants (literal constants and numerical constants).

Example 2-7. The followings are template atoms:



max is the template name; weight and student are predicate names, called actual predicates; variables and constants are called standard terms (Sex is a standard term); the dollar $ symbol is called projection term and the symbol * is called parameter term. M and Age are a list of usual terms (i.e. either variables or constants), and are called output lists.

We note that the template definitions may be unified with a template atom in many ways. The above example contains a plain invocation ( max{weight(*)}(M) ), and a compound invocation ( max{ student($,Sex,*) }(Age) ). Intuitively, projection terms ($ symbols) are intended in order to indicate attributes of an actual predicate which have to be ignored. A standard term (a constant or a variable) within an actual atom indicates a group-by attribute, whereas parameter terms (* symbols) indicate attributes to be considered as parameter.

For example, the template atom max{student($,Sex,*)}(Age) allows to employ the definition of the template predicate max on a ternary predicate student, discarding the first attribute of student, and grouping by values of the second attribute. The intuitive meaning of this invocation is to define a predicate computing the student with maximum value of the Age attribute (the third attribute of the student predicate), grouped by the Sex attribute (the second one), ignoring the first attribute. The computed values of Age are returned through the output list.

Example 2-8. We consider the following EDB:


it represents a set of persons of which it is known name, sex and age. We consider the template definition of max given in Example 2-3. The following:

older_sex(Name,Sex,Age):- max{person($,Sex,*)}(Age),person(Name,Sex,Age).

is a template atom that compute the oldest person with grouping on the sex attribute, that is the person of age greater than female sex and that one of male sex. Launching dlt from command line, as it follows:

we obtain the model

{{{ {older_sex(paddy,f,27),older_sex(gibbi,m,29)} }}}

Therefore paddy is the greatest person of female sex, while gibbi is the greateest person of male sex.

Example 2-9. We consider another example and the following template:

#template sum{emp(2)}(1)

   precedes(X,Y) :- emp(X,_), emp(Y,_), X<Y.
   succ(X,Y) :- precedes(X,Y), not elementInMiddle(X,Y).
   elementInMiddle(X,Y) :- precedes(X,Z), precedes(Z,Y).
   first(X) :- emp(X,_), not hasPredecessor(X).
   last(X) :- emp(X,_), not hasSuccessor(X).
   hasPredecessor(X) :- succ(Y,X).
   hasSuccessor(Y) :- succ(Y,X).
   partialSum(X,Sx) :- first(X), emp(X,Sx).
   partialSum(Y,S) :- succ(X,Y), partialSum(X,PSx), emp(Y,Sy),S=PSx+Sy.
   sum(S) :- last(L), partialSum(L,S).


It allows to sum all the values of the second attribute of the argument emp in an iterative manner. The first attribute must be an ID which identifies unambiguously the instance. The template first orders the instances of emp by their ID (first rule), then using the next rules it defines the successor of each instance. It introduces a new atom, succ(I,II), thus finding the first and the last of the list (first(X),first(X)). The predicate partialSum scans the entire ordered list and addes the sum of the preceding values to the following value. The sum predicate receives the value associated with the last instance of emp. This is possible due to the predicate last.

Now we consider the following EDB:


it represents a set of persons of which it is known ID, name and salary. We use the template sum and we consider the following:

result(DIP,S):- sum{impiegato(*,$,DIP,*)}(S).

this is a template atom which calculates the sum of the values of the 4th attribute of employee (salary) also taking into consideration the first (ID), ommiting the second(name) and grouping the result by the third attribute(department).

Launching dlt from command line, as it follows:

> ./ dlt name_file -silent -filter=result 

we obtain the model

{{{{result(direction,153), result(production,114), result(administration,125), result(distribution,45)} }}}

The result is obtained in a simple manner. First, the template is executed as if the DIP parameter, which is later used to group the results, is only used for the selection. Therefore the instances of employee are ordered by their ID. Then the DIP and salary of each are selected. A table with a row for each employee and two columns, the first for departiment and the second for salary is obtained. The table is then analysed dividing the rows in groups defined by their DIP values. After this, the sum is separately calculated for each group. The output (filtering only the result predicate) contains the sum of the salaries for each departiment.

Chapter 3. Safety

Safety of variables in template atom follows the usual criterion. We suppose to have in the body of a generic rule a negated invocation; then must be valid the following condition must be valid:

Safety for the Negated Template Atom

Each variable occurring in the output list of the given invocation also occurs in at least one non-comparative positive literal in the body of the same rule (resp. the same constraint).

Example 3-1. Unsafe Rules and Constraints

u1: head(Y) :- not template_name{p(M,$,*)}(const).

u2: :- not template_name{p($,M,*)}(X).

u1 is unsafe because the variable Y that appear in the head of the rule does not appear in at least a positive atom of the body of the same rule.

u2 is unsafe because the variable X in the output list of the given invocation does not appear in at least a positive atom of the body of the same rule.

Example 3-2. Safe Rules and Constraints

s1: head(Y) :- not template_name{p(M,$,*)(const), predicate(_,_,Y).

s2: :- not template_name{p($,M,*)}(X), predicate(_,_,X).

s3: head(X) :- not template_name{p(M,$,*)}(Y),predicate(_,X,Y).

s4: :- template_name{p(M,$,*)}(X).

s5: :- not template_name{p(M,$,*)}(const).

Chapter 4. Knowledge Representation by DLP^T

In this section we show by example the main advantages of template programming. Example put in evidence the easiness of providing a succinct and elegant way for quickly introducing new constructs using the DLPT language.

Section 4.1. Aggregates

Aggregate predicates allow to represent properties over sets of elements: the next example shows how to fast prototype aggregate semantics without taking into account of the efficiency of a built-in implementation.Here we take advantage of the template predicate max, defined in Example 2.1. The next template predicate defines a general program to count distinct values of a predicate p, given an order relation succ defined on the domain of p.

#template count{p(1),succ(2)}(1)

   partialCount(I,V) :- not p(Y), I=Y+1,partialCount(Y,V).
   partialCount(I,V2) :- p(Y), I=Y+1,partialCount(Y,V),
   partialCount(I,V2) :- p(Y),I=Y+1,partialCount(Y,V),
   count(M) :- max{partialCount($,*)}(M).


The above template definition is conceived in order to count, in a iterative-like way, values of the p predicate through the partialCount predicate. A ground atom partialCount(i,a) means that at the stage i, the constant a has been counted up. The predicate count takes the value which has been counted at the highest (i.e. the last) stage value. It is worth noting how max is employed over the binary predicate partialCount, instead of an unary one. Indeed, the $ and * symbols are employed to project out the first argument of partialCount. The last rule is equivalent to the piece of code:

partialCount'(X) :- partialCount(_,X).

count(M) :- max{partialCount'(*)}(M).

Template definitions can be employed to introduce and reuse constructs defining the most common search spaces. This improves declarativity of ASP programs to a larger extent. The next two examples show how to define a predicate subset and a predicate permutation, ranging, respectively, over subsets and permutations of the domain of a given predicate p.

#template subset{p(1)}(1)

   subset(X) v -subset(X) :- p(X).


#template permutation{p(1)}(2).

   permutation(X,N)v npermutation(X,N) :- p(X),#int(N), N <= N1,
   :- permutation(X,A),permutation(Z,A), Z <> X.
   :-permutation(X,A),permutation(X,B), A <> B.
   covered(X) :- permutation(X,A).
   :- p(X), not covered(X).


The explanation of the subset template predicate is quite straightforward. As for the permutation definition, a ground atom permutation(x,i) tells that the element x (taken from the domain of p), is in position i within the currently guessed permutation. The rest of the template subprogram forces permutations properties to be met.

Next we show how count and subset can be exploited to succinctly encode the k-clique problem, i.e., given a graph G (represented by predicates node and edge), find if there exists a complete subgraph containing at least k nodes (we consider here the 5-clique problem):

in_clique(X) :- subset{node(*)}(X).
:- count{in_clique(*),>(*,*)}(K),K < 5.
:- in_clique(X),in_clique(Y), X <> Y, not edge(X,Y).

The first rule of this example guesses a clique from a subset of nodes. The first constraint forces a candidate clique to be at least of nodes, while the last forces a candidate clique to be strongly connected. The permutation template can be employed, for instance, to encode the Hamiltonian Path problem: given a graph G, find a path visiting each node of G exactly once:

path(X,N) :- permutation{node(*)}(X,N).
:- path(X,M), path(Y,N), not edge(X,Y), M = N+1.

Sets: Extending Datalog with Set programming is another matter of interest for the ASP field. It is fairly quick to introduce set primitives using DLPT; a set S is modeled through the domain of a given unary predicate s. Intuitive constructs like intersection, union, or symmetricdifference, may be modeled as follows.

#template intersection{a(1),b(1)}(1).

   intersection (X) :- a(X),b(X).


#template union{a(1),b(1)}(1).

   union(X) :- a(X).
   union(X) :- b(X).


#template symmetricdifference{a(1),b(1)}(1)

   symmetricdifference(X) :- union{a(*),b(*)}(X),
   not intersection{a(*),b(*)}(X).


Dates: managing time and date data types is another important issue in engineering applications of DLP. The following template shows how to compare dates represented through a ternary relation day, month, year.

#template before{date1(3),date2(3)}(6)

   before(D,M,Y,D1,M1,Y1) :- date1(D,M,Y),
   date2(D1,M1,Y1), Y < Y1.
   before(D,M,Y,D1,M1,Y1) :- date1(D,M,Y),
   date2(D1,M1,Y1), Y == Y1, M < M1.
   before(D,M,Y,D1,M1,Y1) :- date1(D,M,Y), date2(D,M1,Y1),
   Y == Y1, M = M1, D < D1.


Chapter 5. Higher order predicates

With the use of higher order predicates it is possibile to consider the names of the predicates as variables. The user inserts atoms as if they where of first order, but if the option -higherorder is used it is possible to execute higher order queries. Moreover they are fundamental for the Frame Logic and for the use of predicates that may appear with different arities.

Example 5-1. We consider the following EDB:


it represents a set of persons; the names of the predicates define the relations between the persons. The use higher order allows the user to use the names of the predicates as though they where attributes of other predicates. The following rule simply rewrites the facts, but also allows the user to understand how to perform higher order queries.

relationship(X,R,Y):- R(X,Y).

Launching dlt from command line with option -higherorder, as it follows:

 >  ./dlt name_file -higherorder -solverparams={-nofact}

we obtain the model


Please note that now the predicate names appear also as attributes of the relations (see for instance relationship(gisella,sister,maria)).

Chapter 6. Frame Logic

F-logic is really useful, since it allows to model reality in a very simple way, exploiting its object-oriented programming features. Moreover, using frames helps to keep the code compact and enhances readability with respect to having a big set of single rules. Indeed, in principle, everything which can be expressed with frames can be also done using predicates and standard atoms; however frames make programs simpler and more intuitive. Like in OO programming languages, classes and objects are both supported. When declaring an object, it can in fact belong to a class. A frame object has usually a list of attributes, which can eventually be empty. It is possible to create a Frame Molecule and place it either in the head or in the body of a rule. A simple frame molecule is:


The above contains a single attribute expression nameattribute->valueattribute.

Example 6-1.

brown:employee[ surname -> "Mr. Brown",skill ->> {java, asp}, salary -> 800 , married].

Here we see interesting features. The object "brown" has been defined as belonging to the class "employee" by means of the operator ":". Then a list of attributes is associated to the object. Here we see two types of attributes, single-valued (characterized by the "->" arrow), and multi-valued (characterized by the "->>" arrow). Multi-valued attributes are very useful, as they permit the use of sets of values. In this case they are well suited to represent the set of skills of an employee. Please note that in case of multi-valued attributes, the curly brackets are used. They are usually omitted when there is only one attribute in the set. Attributes can also be boolean-valued.

With frames, as we have seen so far, it is possible to describe objects with ease, but they are very useful also to describe classes and taxonomies of classes, with many expressive constructs. To describe a taxonomy, we basically can make use of the binary operator "::", which means that the first operand is subclass of the second operand.

Example 6-2.

webDesigner::javaProgrammer. javaProgrammer::Programmer.

In this example we can see that webDesigner is defined as a subclass of javaProgrammer, which in turn is defined as a subclass of Programmer. The subclassOf operator is transitive, and so webDesigner is a subclass of Programmer too.

When describing classes it is possible, like in java or c++, to list a certain number of attributes, specifying either their value or their type. To do this, a wide number of "arrow" operators is available to the user, such as ->, ->>, *->, *->>, =>, =>>. These operator on their own do not have a strong semantic meaning. Actually, they take the classical F-logic meaning when coupled with specific axioms, which we will discuss later.

Example 6-3.

programmer[salary => integer].

The "=>" and "=>>" operators are used to describe the type of an attribute. Both support set of types for each attribute. If you want to use this features, keep in mind that the different values must be comma-separated and enclosed in curly brackets.

Example 6-4.

employee[skill =>>{string, int}].

The main difference between the two operators is that they "map" on different operator applied to instances. In particular, the operator "=>" maps on the instance operator "->", while "=>>" maps on "->>". Except this, they are pretty similar.

In a similar way it is possible to define the values associated to an attribute. Being in the context of classes, these values can be thought about as "default values" for the class. To associate values to a class attribute, different operators are available. As for the type operators, they have a particular meaning only when coupled to a F-logic semantics. The available operators are: ->,->>,*->,*->>. By using the operators with "*", usually the will is to specify that the value assigned to the attribute is inherited automatically by the subclasses. This is not forced by default, actually. In general, "->" and "->>" operators work on single values, while the others work on sets.

Example 6-4.

employee[skill ->> {java, asp}, salary *->1000].

While some attributes may be described as facts, other may be defined using inference rules. For example, suppose that john is married to mary and mary has three children. It can be assumed that john is the father of alice, nancy, and jack (the children of mary). Therefore the following fact:

john[ children->>{ alice,nancy,jack } ].

may be automatically derived if we add the following rule:

X[ children -> C ] :- Y[ spouse->X,children->C ].

In addition it is possible to specify the class an object belongs to using the following rules:

john: man.
mary: woman.

Once the class of the object is identified, a specific list of attributes can be assigned at the same time:

mary: woman[ children->>{ alice,nancy,jack } ].

Example 6-5. We consider an other definition frame:

carlo:man[ married->camilla: woman[ children->>{ tom: man[ engaged->sara ],laura: woman[ engaged->harryT ] },
                                 father->bruce ],
          children->>{ william: man[ mother->diana ],harry: man[ mother->diana ] },
          mother->elisabeth: woman,
          father->philip: man ].

The frame describes the relationships existing between some member of the Britannic royal family. carlo is a man and he’s married with camilla. He has two children: william and harry, and their mother was diana. carlo ‘s mother is elisabeth and his father is philip. camilla is a woman and she has a daughter, laura, and a son, tom. laura is betroth with harryT , and tom ’s girlfriend is sara.

Launching dlt from command line:

 >  ./dlt name_file

we obtain the model

{carlo:man, camilla:woman, tom:man, laura:woman, william:man, harry:man, elisabeth:woman, philip:man, man::man, woman::woman, camilla[children->>{tom,laura},father->bruce],carlo[married->camilla,children->>{william,harry},father->philip,mother->elisabeth],harry[mother->diana],laura[engaged->harryT],tom[engaged->sara],william[mother->diana]}

The higher order atoms are fundamental for the frames. Indeed, the output consists of predicates whose names were attributes in the frames. For example look at the first part of the definition of the frame:

carlo: man[ married->camilla ].

carlo is a man, and he is married to camilla; 'married' is an attribute of the object carlo, but in the output it's presented as the name of a predicate:

married(carlo, camilla).

Example 6-6. Let's show another example:


The frame says that Gianni is a student and has a pc named mistery. The pc is a notebook which uses debian as operating system. In addition information on the OS is given: kind and kernel. Launching this program without the option higherorder results with an error:

 >  ./dlt nome_file

os, first used with arity 2, now seen with arity 1.

The cause of the error is clear once we look at the opuput with the option -higherorder

 >  ./dlt -higherorder nome_file

{pc(gianni,mistero), os(mistero,debian), kind(debian,gnu), kernel(debian,linux), os(debian), notebook(mistero), student(gianni)}

The atoms os(mistero,debian) e os(debian) appears in the output. The same predicate is present with different arity. In the case of first order atoms this is not permitted. It is only possible using higher order atoms.

Section 6.1. Semantic Modeling

One very interesting feature is that it is possible to specify, in addiction to the file containing the frame program, other files containing the specification of a semantic behavior. Various pre-written modules are available to the user. The modules have been designed in order to give a semantic meaning to the various operators described previously. We esemplify the meaning of these modules with the following table:






A::B :- A::C, C::B

transitivity of the subclass operator


::, :


reflexivity of subclass operator



:- A::C, C::A, A!=C.

constraint to prohibit cycles in the taxnonomy


:, ::

X:C :- X:D, D::C.

class inheritance for individuals



:- A[M -> V], A[M -> W], V!=W.

constraint for single valued operator



:- A[M *-> V], A[M *-> W], V!=W.

constraint for single valued operator


=>, ::

D[A => T] :- D::C, C[A => T].

structural inheritance implementation


=>>, ::

D[A =>> T] :- D::C, C[A =>> T].

structural inheritance implementation


->, ->>, *->, *->>

behavioral inheritance implementation


=>, ->, :

V:T :- C[A => T],I:C,I[A->V].

mapping between the type and value operators


Please note the difference between structural inheritance, which is linked to the "=>" operator, and behavioral inheritance, which is forced if the "F" axiomatic module is loaded.

 >  ./dlt -higherorder [axiomatic constraints list] filename

Where the axiomatic constraints list can be a subset of the various constraints delivered with the executable, that is to say B,C,CO,F,S. Each constraint is contained in a file with extension ".axi".

Example 6-7. Let us consider the following example

white : colour.
black : colour.
yellow : colour.,
carnivore[ canines *-> "sharp", color => colour ].
feline :: carnivore.
feline[ genre *-> mammal ].
lion :: feline.
tiger:: feline.
cat :: feline.
tiger [color *-> yellow].
whiteTiger :: tiger.
whiteTiger [ color *-> white ].
sherekhan : whiteTiger.

It describes the family of carnivores, and the relationship with felines. Here we can see that a carnivore is characterized by its sharp canines and its colour, while a feline is a carnivore whose genre is mammal. Then it gives some races of felines, like lion and tiger, each one characterized by something different. Let us concentrate on the tiger. The default color of a tiger is yellow, whith white stripes. A particular case of a tiger is a white tiger, which is white coloured. Sherekhan is an instance of a white tiger, and so we must suppose it is white coloured. Here a problem arises, in that sherekhan, being a white tiger, is also a tiger, and so it should be yellow, because color is inherited. Here non-monotonic inheritance is used,and so sherekhan takes its color from the most specific class it belongs to, in this case whiteTiger. This is realized by means of the axiomatic modules, that impose this behavior.

if we launch the evaluation of the program with the command:

 >  ./dlt -higherorder C.axi B.axi F.axi CO.axi S.axi whiteTiger.dlf

the result will be the following:

{white:colour, black:colour, yellow:colour,sherekhan:carnivore,sherekhan:feline,sherekhan:tiger,sherekhan:whiteTiger,colour::colour, carnivore::carnivore, feline::carnivore, feline::feline, lion::carnivore, lion::feline, tiger::carnivore,tiger::feline, tiger::tiger, cat::carnivore,cat::feline, whiteTiger::carnivore, whiteTiger::feline, whiteTiger::tiger,whiteTiger::whiteTiger,carnivore[canines*->"sharp",color=>colour],cat[color=>colour,genre*->mammal,canines*>"sharp"],feline[color=>colour,genre*->mammal,canines*->"sharp"],lion[color=>colour,genre*>mammal,canines*>"sharp"],sherekhan[color->white,genre->mammal,canines->"sharp"],tiger[color=>colour,color*->yellow,genre*->mammal,canines*->"sharp"],whiteTiger[color*->white,color=>colour,genre*->mammal,canines*>"sharp"]}

It is possible to see that dlt entails that sherekhan is carnivore, feline and a tiger, and so it has all the attributes inherited from these classes, but as it belongs to the class whiteTiger, its color is white,as we wanted to be.

Section 6.2. Frame Space directive

The Frame Space is a new directive introduced in DLT:



Chapter 7. System Architecture

A DLPT program P, is sent to a DLPT pre-parser, which performs syntactic checks (included nonrecursivity checks), and builds an internal representation of the DLPT program. The DLPT Inflater produces an equivalent SOLVER program P'; P' is piped towards the SOLVER system. The models M(P') of P', computed by SOLVER, are then converted in a readable format through the Post-parser module; the Post-parser filters out from M(P') informations about internally generated predicates and rules.

Figure 1.


Section 7.1 DLT Work-Flow

We show here the work-flow of DLT, exploiting example 6-3. We launch the program with -preparsing option and divert the output in the file (tmp):
 >  ./dlt -silent -higherorder -preparser nome_file >tmp

The file tmp will appear as following:


Passing the content of tmp to the solver (i.e., DLV in this case) and diverting the output in another file (tmp2):

 >  ./dl -silent tmp > tmp2

the file tmp2 will look as following:

{a_2(married,carlo,camilla), a_2(children,carlo,william), a_2(children,carlo,harry), a_2(children,camilla,tom), a_2(children,camilla,laura), a_2(engaged,tom,sara), a_2(engaged,laura,harryT), a_2(father,carlo,philip), a_2(father,camilla,bruce), a_2(mother,carlo,elisabeth), a_2(mother,william,diana), a_2(mother,harry,diana), a_1(man,carlo), a_1(man,tom), a_1(man,william), a_1(man,harry), a_1(man,philip), a_1(woman,camilla), a_1(woman,laura), a_1(woman,elisabeth)}

Finally, we send the content of tmp2 to DLT postparser module as following:

 >  ./dlt -postparsing <tmp2 

and we obtain the following:

{married(carlo,camilla), children(carlo,william), children(carlo,harry), children(camilla,tom), children(camilla,laura), engaged(tom,sara), engaged(laura,harryT), father(carlo,philip), father(camilla,bruce), mother(carlo,elisabeth), mother(william,diana), mother(harry,diana), man(carlo), man(tom), man(william), man(harry), man(philip), woman(camilla), woman(laura), woman(elisabeth)}

the same output that we would have obtained launching the command:

 >  ./dlt family

Indeed, performing the whole process launching directly DLT as above, the only model is:

{married(carlo,camilla), children(carlo,william), children(carlo,harry), children(camilla,tom), children(camilla,laura), engaged(tom,sara), engaged(laura,harryT), man(carlo), man(tom), man(william), man(harry), man(philip), woman(camilla),woman(laura), woman(elisabeth), father(carlo,philip), father(camilla,bruce), mother(carlo,elisabeth), mother(william,diana), mother(harry,diana)}

exactly the same of what obtained performing explicitly all the operations constituting the flow.

Section 7.2. Compatibility with other solvers

DLT can virtually support any solver. At the moment, we can state the compatibility with the syntax of some of them. The following table shows the instructions of the solvers which dlt natively recognizes.




any solver


ASITIS%* Body *%
ASITIS's Body is sent verbatim to solver


Aggregates and weak constraints

see DLV - User Manual


Not tested yet

see DLV - User Manual


external built-ins

see dlvex - User Manual


external atoms, namespace directive

see dlvhex - User Manual


Supports normal programs with function symbols

see smodels - User Manual

Note: All the SOLVER options must be passed using the option -solverparams={}.

Section 7.3. Notes on using DLV-HEX with DLT

DLV-HEX has higher order reasoning built-in, so it is not necessary to invoke DLT with the option -higherorder switched on. Also, DLV-HEX supports natively DLT. DLT can be used with DLV-HEX with different command line combinations such as

   dlt -silent -preparsing program.dlt | dlvhex --silent -- | dlt -postparsing


   dlvhex --dlt program.dlt

makes dlvhex to invoke dlt as a preparser

mwiki: dlt/dltManual (last edited 2010-10-26 10:19:44 by localhost)