% Schema
%-------
% * Each predicate (relation) is named.
% * Each column is (optionally) named.
% * Each column is assigned a data type.
% * Datatypes are: integer, real, string, any.
% * Columns of type any can contain complex terms (functors).
database({city(Name:string, State:string, Population:integer),
	  distance(City1:string, City2:string, Distance:real)
	 }).

%Derivated predicates and rules
%------------------------------
%
%  * New (or derived) predicates are defined by rules.
%
%  * Example: derive the city in Texas with more than 400,000 people
%
%    lt_city(C, Pop) <-  city(C, 'Texas', Pop), Pop > 400000.
%
%  * Rules have a purely declarative role, similar to virtual views in
%    relational databases
%
%  * Queries have an imperative role: when the query is executed then the
%    results tha satisfy the query are returned
%
%  * Example: List all cities in Texas with where the population exceeds
%    400,000. 
%
%    query lt_city(C, Pop)
%
%    This query will return:
%
%       lt_city('Huston',   3000000)
%       lt_city('Dallas',   2000000)
%       lt_city('Austin',    750000)
%       lt_city('San Antonio', 1500000)
%
%  * But queries without variables (aka closed queries) return a yes/no answer
%    (no in this case since Austin does not have 2,000,000 inhabitants)
%
%The command
%
% query lt_city('Austin', 2000000)
%
%will actually be used in the regular interface, while in the Java interface
%the user will select an item from the menu.
lt_city(C, Pop) <-  city(C, 'Texas', Pop), Pop > 400000.
export lt_city(X, Y).

% X is given and Y is expected. 
export lt_city($X, Y).
% X is expected and Y is given. 
export lt_city(X, $Y).
% Both X and Y are given and the response is True/False. 
export lt_city($X, $Y).