%%% -*- Mode: Prolog; -*-

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% ProbLog program describing a viral marketing problem
% example for using tabled decision theory ProbLog
% $Id: viralmarketing_tabled.pl 4875 2010-10-05 15:28:35Z theo $
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% The viral marketing example consists of a social network of friend relations. You have to decido which persons to market. Sending marketing has a cost of 2, but might cause people to buy your product, giving you a profit of 5. When someone buys the product, it becomes more likely that his friends also buy the product.

:- use_module(library(dtproblog)).

% Decisions
? :: marketed(P) :- person(P).

% Utility attributes
buys(P) => 5 :- person(P).
marketed(P) => -2 :- person(P).

% Probabilistic facts
0.2 :: buy_from_marketing(_).
0.3 :: buy_from_trust(_,_).

% Background knowledge
person(bernd).
person(ingo).
person(theo).
person(angelika).
person(guy).
person(martijn).
person(laura).
person(kurt).

trusts(X,Y) :- trusts_directed(X,Y).
trusts(X,Y) :- trusts_directed(Y,X).

trusts_directed(bernd,ingo).
trusts_directed(ingo,theo).
trusts_directed(theo,angelika).
trusts_directed(bernd,martijn).
trusts_directed(ingo,martijn).
trusts_directed(martijn,guy).
trusts_directed(guy,theo).
trusts_directed(guy,angelika).
trusts_directed(laura,ingo).
trusts_directed(laura,theo).
trusts_directed(laura,guy).
trusts_directed(laura,martijn).
trusts_directed(kurt,bernd).

% The buys predicate is tabled to speed up exact inference. K-best inference does not support tabled predicates.

% Add this before a tabled predicate.
:- dynamic buys/1.

buys(X) :-
        marketed(X),
        buy_from_marketing(X).
buys(X) :-
        trusts(X,Y),
        buy_from_trust(X,Y),
        buys(Y).

% Add this after a tabled predicate.
:- problog_table buys/1.
:- set_problog_flag(use_db_trie, true).
:- set_problog_flag(use_old_trie, false).

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% EXAMPLE USE::
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%
% Find the globally optimal strategy.
%
% ?- dtproblog_solve(Strategy,ExpectedValue).
% ExpectedValue = 3.21097,
% Strategy = [marketed(martijn),marketed(guy),marketed(theo),marketed(ingo)]
%
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%
% Compute the expected value for a given strategy.
%
% ?- dtproblog_ev([marketed(martijn),marketed(laura)],ExpectedValue).
% ExpectedValue = 2.35771065
%
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%
% Find a locally optimal strategy.
%
% ?- set_problog_flag(optimization, local), dtproblog_solve(Strategy,ExpectedValue).
% ExpectedValue = 3.19528,
% Strategy = [marketed(martijn),marketed(laura),marketed(guy),marketed(ingo)]
%
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%
% Find all ground utility facts in the theory.
%
% ?- dtproblog_utility_facts(Facts).
% Facts = [buys(bernd)=>5, buys(ingo)=>5, buys(theo)=>5, buys(angelika)=>5, buys(guy)=>5, buys(martijn)=>5, buys(laura)=>5, buys(kurt)=>5, marketed(bernd)=> -2, marketed(ingo)=> -2, marketed(theo)=> -2, marketed(angelika)=> -2, marketed(guy)=> -2, marketed(martijn)=> -2, marketed(laura)=> -2, marketed(kurt)=> -2]
%
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%
% Find all ground decisions relevant to the utility attributes.
%
% ?- dtproblog_decisions(Decisions).
% Decisions = [marketed(angelika), marketed(theo), marketed(kurt), marketed(ingo), marketed(laura), marketed(martijn), marketed(guy), marketed(bernd)]
%
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%
% (K-best inference and optimization does not support tabled predicates. Please use the non-tabled viral marketing example.)
%
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