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Abduction with Penalization in Logic Programming

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Abduction with Penalization in Logic Programming
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    a  r   X   i  v  :  c  s   /   0   1   1   1   0   1   0  v   1   [  c  s .   L   O   ]   6   N  o  v   2   0   0   1 ABDUCTION WITH PENALIZATIONIN LOGIC PROGRAMMING Giovambattista Ianni, Nicola Leone, Simona Perri Francesco Scarcello Department of Mathematics D.E.I.S.University of Calabria University of CalabriaI-87030 Rende (CS), I-87030 Rende (CS), Italy ianni@deis.unical.it, scarcello@deis.unical.itleone@unical.it,sperri@si.deis.unical.it Abstract Abduction, first proposed in the setting of classical logics, has beenstudied with growing interest in the logic programming area during thelast years.In this paper we study  abduction with penalization   in logic program-ming. This form of abductive reasoning, which has not been previouslyanalyzed in logic programming, turns out to represent several relevantproblems, including optimization problems, very naturally. We define aformal model for abduction with penalization from logic programs, whichextends the abductive framework proposed by Kakas and Mancarella. Weshow the high expressiveness of this formalism, by encoding a couple of relevant problems, including the well-know Traveling Salesman Problemfrom optimization theory, in this abductive framework. The resulting en-codings are very simple and elegant. We analyze the complexity of themain decisional problems arising in this framework. An interesting resultin this course is that “negation comes for free.” Indeed, the addition of (even unstratified) negation does not cause any further increase to thecomplexity of the abductive reasoning tasks (which remains the same asfor not-free programs). 1 Introduction Abduction is an important kind of reasoning which has been first studied indepth by Peirce [18]. Given the observation of some facts, abduction aims atconcluding the presence of other facts, from which, together with an underlyingtheory, the observed facts can be explained, i.e., deductively derived. Thus,roughly speaking, abduction amounts to an inverse of modus ponens.For example, medical diagnosis is a typical abductive reasoning process:From the symptoms and the medical knowledge, a diagnosis about a possibledisease is abduced. Notice that this form of reasoning is not sound (a diagnosis1  may turn out to be wrong), and that in general several abductive explanations(i.e., diagnoses) for observations may be possible.During the last years, there has been increasing interest in abduction indifferent areas of computer science. It has been recognized that abduction is animportant principle of common-sense reasoning, and that abduction has fruitfulapplications in a number of areas such diverse as model-based diagnosis [19],speech recognition [11], maintenance of database views [14], and vision [3].In the past, most research on abduction concerned abduction from classicallogic theories. However, we argue that the use of logic programming to performabductive reasoning can be more appropriate in several applicative domains.For instance, consider the following scenario. Assume that it is Saturdayand is known that Joe goes fishing on Saturdays if it’s not raining. This maybe represented by the following theory  T  : go fishing  ←  is saturday  &  ¬ rains  ;  is saturday  ← Now you observe that Joe is not out for fishing. Intuitively, from this observationwe conclude that it rains (i.e, we abduce  rains ), for otherwise Joe would be outfor fishing. Nevertheless, under classical inference, the fact  rains  is not anexplanation of   ¬ go fishing , as  T   ∪ { rains } | =  ¬ go fishing  (neither can onefind any explanation). On the contrary, if we adopt the semantics of logicprogramming, then, according with the intuition, we obtain that  rains  is anexplanation of   ¬ go fishing , as it is entailed by  T   ∪{ rains } .In the context of logic programming, abduction has been first proposed byKakas and Mancarella [13] and, during the recent years, common interest in thesubject has been growing rapidly [4, 15, 13, 12, 6, 5, 20], also for the observationthat, compared to deduction, this kind of reasoning has some advantages fordealing with incomplete information [5, 1].In this paper we study  abduction with penalization   in logic programming.This form of abductive reasoning, well studied in the setting of classical logics[7], has not been previously analyzed in logic programming.We define a formal model for abduction with penalization from logic pro-grams, which extends the abductive framework proposed by Kakas and Man-carella [13]. Roughly, a penalty is assigned to each hypothesis. An abductivesolution  S   is weighted by the sum of the penalties of the hypotheses in  S  . 1 Mimimum-weight solutions are preferred over the other solutions since they areconsidered more likely to occur.We show that some relevant problems, including, e.g., the classical  Traveling Salesman Problem   from optimization theory, can be encoded very simply andelegantly by abduction with penalization; while they cannot be encoded atall in (function-free) logic programming even under the powerful stable modelsemantics.We analyze the computational complexity of the main decisional problemsarising in this framework. An interesting result in this course is that “nega- 1 Actually, in this paper, we consider also forms of weighting functions more general thanSum. 2  tion comes for free.” Indeed, the addition of (even unstratified) negation doesnot cause any further increase to the complexity of the abductive reasoningtasks (which remains the same as for not-free programs). Thus, abduction withpenalization over general logic programs has precisely the same complexity asabduction with penalization over definite Horn theories of classical logics. Con-sequently, the user can enjoy the knowledge representation power of nonmono-tonic negation without paying any additional cost in terms of computationaloverhead. 2 Preliminaries on Logic Programming 2.1 Syntax A  term  is either a constant or a variable 2 . An  atom  is  a ( t 1 ,...,t n ), where  a  is a  predicate  of arity  n  and  t 1 ,...,t n  are terms. A  literal  is either a  positive literala  or a  negative literal  ¬ A , where  a  is an atom. Two literals are  complementary if they are of the form  p  and ¬  p , for some atom  p . Given a literal  L ,  ¬ .L  denotesits complementary literal (the complement of an atom  A  is literal  ¬ A  and viceversa). Accordingly, given a set  A  of literals, ¬ .A  denotes the set  {¬ .L  | L  ∈  A } .A  program clause   (or  rule  )  r  is a  ←  b 1 , ···  ,b k , ¬ b k +1 , ···  , ¬ b m , n  ≥  1 , m  ≥  0where  a,b 1 , ···  ,b m  are atoms. Atom  a  is the  head   of   r , while the conjunction b 1 ,...,b k , ¬ b k +1 ,..., ¬ b m  is the  body   of   r .A  (logic) program   is a finite set of rules. A  ¬ -free program is called  Horn program   or  positive program  . A term, an atom, a literal, a rule or program is ground  if no variable appears in it. A ground program is also called a  proposi-tional   program. 2.2 Stable model semantics Let  LP   be a program. The  Herbrand Universe   U  LP   of   LP   is the set of allconstants appearing in  LP  . The  Herbrand Base   B LP   of   LP   is the set of allpossible ground atoms constructible from the predicates appearing in the rulesof   LP   and the constants occurring in  U  LP   (clearly, both  U  LP   and  B LP   arefinite). Given a rule  r  occurring in a program  LP  , a  ground instance   of   r  is arule obtained from  r  by replacing every variable  X   in  r  by  σ ( X  ), where  σ  is amapping from the variables occurring in  r  to the constants in  U  LP  . We denoteby  ground ( LP  ) the (finite) set of all the ground instances of the rules occurringin  LP  . An  interpretation   for  LP   is a subset  I   of   B LP   (i.e., it is a set of groundatoms). A positive literal  a  (resp. a negative literal  ¬ a ) is true with respect toan interpretation  I   if   a  ∈  I   (resp.  a / ∈  I  ); otherwise it is false. A ground rule r  is  satisfied   (or  true  ) w.r.t.  I   if its head is true w.r.t.  I   or its body is false 2 Note that function symbols are not considered in this paper. 3  w.r.t.  I  . A  model   for  LP   is an interpretation  M   for  LP   such that every rule r  ∈  ground ( P  ) is true w.r.t.  M  .Given a logic program  LP   and an interpretation  I  , the  Gelfond-Lifschitz transformation   of   LP   with respect to  I   is the logic program  LP  I  consistingof all rules  a  ←  b 1 ,... ,b k  such that (1)  a  ←  b 1 ,... ,b k , ¬ b k +1 ,... , ¬ b m  ∈ LP   and (2)  b i  / ∈  I,  for all  k < i  ≤  m. Notice that ¬  does not occur in  LP  I  , i.e., it is a positive program. Each positiveprogram  LP   has a least model (i.e., a model included in every model), denotedby  lm ( LP  ).An interpretation  I   is called a  stable model   of   LP   iff   I   =  lm ( LP  I  ) [10]. Thecollection of all stable models of   LP   is denoted by STM( LP  ) (i.e., STM( LP  ) = { I   |  I   =  lm ( LP  I  ) } ). Example 2.1  Consider the following (ground) program  LP  : a  ← ¬ b b  ← ¬ a c  ←  a c  ←  b The stable models of   LP   are  M  1  =  { a,c } and  M  2  =  { b,c } . Indeed, by definitionof Gelfond-Lifschitz transformation,  LP  M  1 =  {  a  ← , c  ←  a, c  ←  b  }  and LP  M  2 =  {  b  ← , c  ←  a, c  ←  b  } ; thus, it is immediately recognized that lm ( LP  M  1 ) =  M  1  and  lm ( LP  M  2 ) =  M  2 .In general, a logic program may have more than one stable model or even nostable model at all. In the logic programming framework (under stable modelsemantics) there are two main notions of reasoning: Brave reasoning  (or  credulous reasoning  ) infers that a literal  Q  is true in LP   (denoted  LP   | = b Q ) iff   Q  is true with respect to  M   for some  M   ∈ STM( LP  ). Cautious reasoning  (or  skeptical reasoning  ) infers that a literal  Q  is true in LP   (denoted  LP   | = c Q ) iff   Q  is true with respect to  M   for all  M   ∈ STM( LP  ).The inference relations  | = b and  | = c extend to sets of literals as usual. Example 2.2  For the program  LP   of Example 2.1,  a,b  and  c  are brave infer-ences ( LP   | = b { a,b,c } ); the only cautious inference is  c  ( LP   | = c c ).In this paper, we are mainly interested in brave reasoning, even if our defi-nitions can be easily extended to cautious reasoning. 3 A Model of Abduction with Penalization In this section, we describe our formal model for abduction with penalizationsover logic programs.4  abc def  Figure 1: Computer network Definition 3.1  A problem of abduction P   consists of a triple  H,LP,O  , where H   is a finite set of ground atoms  (hypotheses) ,  LP   is a logic program, and  O  isa finite set of ground literals  (observations, or manifestations) .A set of hypotheses  S   ⊆  H   is an  admissible solution   (or  explanation  ) to  P   if there exists a stable model  M   of   LP   ∪ S   such that,  ∀ o  ∈  O ,  o  is true w.r.t.  M  (i.e.,  LP   ∪ S   | = b O ).The set of all admissible solutions to  P   is denoted by  Adm  ( P  ).The following example shows a classical application of abduction for diag-nosis purposes. Example 3.2  (Network Diagnosis)  Suppose that we are working on ma-chine  a   (and we therefore know that machine  a   is online) of the computer net-work in Figure 1, but we observe machine  e   is not reachable from  a  , even if weare aware that  e   is online. We would like to know which machines are offline.This can be easily modeled in our abduction framework defining a problem of abduction   H,LP,O  , where the set of hypotheses is  H   = { offline(a), offline(b),offline(c), offline(d), offline(e), offline(f) } , the set of observations is  O  =  {¬ offline(a),  ¬  offline(e),  ¬  reaches(a,e) } , and  LP   is the logic program reaches  ( X  , X  ) :  −  node  ( X  ) , ¬  offline  ( X  ) . reaches  ( X  , Z  ) :  −  reaches  ( X  , Y  ) ,  connected  ( Y  , Z  ) ,  ¬  offline  ( Z  ) . Note that the admissible solutions for  P   corresponds to the network config-urations that may explain the observations in  O . In this example,  Adm  ( P  ) con-tains five solutions S  1  =  { offline  (  f   ) ,  offline  ( b ) } ,  S  2  =  { offline  (  f   ) ,  offline  ( c  ) , offline  ( d  ) } , S  3  =  { offline  (  f   ) ,  offline  ( b ) ,  offline  ( c  ) } ,  S  4  =  { offline  (  f   ) ,  offline  ( b ) ,  offline  ( d  ) } , S  5  =  { offline  (  f   ) ,  offline  ( b ) ,  offline  ( c  ) ,  offline  ( d  ) } .Note that Definition 3.1 concerns only the logical properties of the hypothe-ses, and it does not take into account any kind of minimality criterion. Wenext define the problem of abduction with penalizations, which allows us tomake finer abductive reasonings, by expressing preferences on different sets of hypotheses and single out the most plausible abductive explainations. Definition 3.3  A problem of abduction with penalization ( PAP  )  P   is a tuple  H,LP,O,γ,cost  , where   H,LP,O  , is a problem of abduction,  γ   is a function5
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