Well-Founded Semantics for Extended Logic Programs with Dynamic Preferences

G. Brewka

Volume 4, 1996

Links to Full Text:

Abstract:
The paper describes an extension of well-founded semantics for logic programs with two types of negation. In this extension information about preferences between rules can be expressed in the logical language and derived dynamically. This is achieved by using a reserved predicate symbol and a naming technique. Conflicts among rules are resolved whenever possible on the basis of derived preference information. The well-founded conclusions of prioritized logic programs can be computed in polynomial time. A legal reasoning example illustrates the usefulness of the approach.

Extracted Text


Journal of Artificial Intelligence Research 4 (1996) 19-36 Submitted 7/95;
published 2/96

Well-Founded Semantics for Extended Logic Programs

with Dynamic Preferences

Gerhard Brewka brewka@kr.tuwien.ac.at TU Wien, Abteilung f"ur Wissensbasierte
Systeme Treitlstr. 3, 1040 Wien, Austria

Abstract The paper describes an extension of well-founded semantics for logic
programs with two types of negation. In this extension information about
preferences between rules can be expressed in the logical language and derived
dynamically. This is achieved by using a reserved predicate symbol and a
naming technique. Conflicts among rules are resolved whenever possible on
the basis of derived preference information. The well-founded conclusions
of prioritized logic programs can be computed in polynomial time. A legal
reasoning example illustrates the usefulness of the approach.

1. Introduction: Why Dynamic Preferences are Needed Preferences among defaults
play a crucial role in nonmonotonic reasoning. One source of preferences
that has been studied intensively is specificity (Poole, 1985; Touretzky,
1986; Touretzky, Thomason, & Horty, 1991). In case of a conflict between
defaults we tend to prefer the more specific one since this default provides
more reliable information. E.g., if we know that students are adults, adults
are normally employed, students are normally not employed, we want to conclude
"Peter is not employed" from the information that Peter is a student, thus
preferring the student default over the conflicting adult default.

Specificity is an important source of preferences, but not the only one,
and at least in some applications not necessarily the most important one.
In the legal domain it may, for instance, be the case that a more general
rule is preferred since it represents federal law as opposed to state law
(Prakken, 1993). In these cases preferences may be based on some basic principles
regulating how conflicts among rules are to be resolved.

Also in other application domains, like model based diagnosis or configuration,
preferences play a fundamental role. Model based diagnosis uses logical descriptions
of the normal behaviour of components of a device together with a logical
description of the actually observed behaviour. One tries to assume normal
behaviour for as many components as possible. A diagnosis corresponds to
a set of components for which these normalcy assumptions lead to inconsistency.
Very often a large number of possible diagnoses is obtained. In real life
some components are less reliable than others. To eliminate less plausible
diagnoses one can give the normalcy assumptions for reliable components higher
priority.

In configuration tasks it is often impossible to achieve all of the design
goals. Often one can distinguish more important goals from less important
ones. To construct the best possible configurations goals then have to be
represented as defaults with different preferences according to their desirability.

cfl1996 AI Access Foundation and Morgan Kaufmann Publishers. All rights reserved.

Brewka The relevance of preferences is well-recognized in nonmonotonic reasoning,
and prioritized versions for most of the nonmonotonic logics have been proposed,
e.g., prioritized circumscription (Lifschitz, 1985), hierarchic autoepistemic
logic (Konolige, 1988), prioritized default logic (Brewka, 1994a). In these
approaches preferences are handled in an "external" manner in the following
sense: some ordering among defaults is used to control the generation of
the nonmonotonic conclusions. For instance, in the case of prioritized default
logic this information is used to control the generation of extensions. However,
the preference information itself is not expressed in the logical language.
This means that this kind of information has to be fully pre-specified, there
is no way of reasoning about (as opposed to reasoning with) preferences.
This is in stark contrast to the way people reason and argue with each other.
In legal argumentation, for instance, preferences are context-dependent,
and the assessment of the preferences among involved conflicting laws is
a crucial (if not the most crucial) part of the reasoning.

What we would like to have, therefore, is an approach that allows us to represent
preference information in the language and derive such information dynamically.
In a recent paper (Brewka, 1994b) the author has described a variant of normal
default logic in which reasoning about preferences is possible. Although
the version of default logic presented in this earlier paper produces reasonable
results in most cases, this approach has several drawbacks:

1. The approach is computationally extremely demanding as it involves the
construction

of the Reiter extensions and an additional compatibility check for each extension
guaranteeing that the preference information was taken into account adequately.

2. It may happen that consistent default theories, i.e., theories whose strict
part is satisfiable, possess no extensions at all. This is astonishing since
in that paper we only dealt with normal defaults. The non-existence of extensions
is due to defeasible preference information. It is highly questionable whether
such information should be able to destroy all extensions.

3. The earlier paper did not take non-normal defaults into account, it is
thus not general

enough to cover normal logic programs with negation as failure.

The approach presented in this paper will be based on extended logic programs
with two types of negation. This means that in comparison with our earlier
proposal we are more restrictive in one respect and more general in another:
we are more restrictive since we do not allow arbitrary first order formulas
as in normal default logic; we are more general since we admit negation as
failure and hence rules which correspond to non-normal defaults in Reiter's
logic. We also switch from the extension based semantics of default logic
to well-founded semantics (van Gelder, Ross, & Schlipf, 1991; Przymusinski,
1991; Lifschitz, 1996), i.e., to an inherently skeptical approach where the
nonmonotonic conclusions are defined directly, not through the notion of
extensions. It is well-known that well-founded semantics sometimes looses
intuitively expected conclusions. This is also the case in our proposal.
However, this is outweighed by a tremendous gain in efficiency: the well-founded
conclusions can be computed in polynomial time.

The outline of the rest of the paper is as follows: in Section 2 we first
review a definition of well-founded semantics for logic programs with two
types of negation which is based on the

20

Extended Logic Programs with Dynamic Preferences double application of a
certain anti-monotone operator. The definition extends Baral and Subrahmanian's
formulation of well-founded semantics for normal logic programs (Baral &
Subrahmanian, 1991) and was used by several authors (Baral & Gelfond, 1994;
Lifschitz, 1996). We show that this definition suffers from an unnecessary
weakness and present a reformulation that leads to better results. Section
3, the main section of the paper, introduces our dynamic treatment of preferences
together with several small motivating examples. We show that our conclusions
are, in general, a superset of the well-founded conclusions. Section 4 illustrates
the expressive power of our approach using a more realistic example from
legal reasoning. Section 5 shows that the worst case time complexity for
generating well-founded conclusions for prioritized programs is polynomial.
Section 6 investigates the relationship to Gelfond and Lifschitz's answer
set semantics (Gelfond & Lifschitz, 1990). Section 7 discusses related work
and concludes.

2. Well-Founded Semantics for Extended Logic Programs A (propositional) extended
logic program consists of rules of the form

c  a1; : : : ; an; not b1; : : : ; not bm where the ai; bj and c are propositional
literals, i.e., either propositional atoms or such atoms preceded by the
classical negation sign. The symbol not denotes negation by failure (weak
negation), : denotes classical (strong) negation. For convenience we will
sometimes use a rule schema to represent a set of propositional rules, namely
the set of all ground instances of the schema.

Extended logic programs are very useful for knowledge representation purposes,
see for instance (Baral & Gelfond, 1994) for a number of illustrative examples.
Two major semantics for extended logic programs have been defined: (1) answer
set semantics (Gelfond & Lifschitz, 1990), an extension of stable model semantics,
and (2) a version of well-founded semantics (Przymusinski, 1991). The second
approach can be viewed as an efficient approximation of the first.

Let us first introduce answer sets. We say a rule r 2 P of the form above
is defeated by a literal l if l = bi for some i 2 f1; : : : ; mg. We say
r is defeated by a set of literals X if X contains a literal that defeats
r. Furthermore, we call the rule obtained by deleting weakly negated preconditions
from r the monotonic counterpart of r and denote it with M on(r). We also
apply M on to sets of rules with the obvious meaning.

Definition 1 Let P be a logic program, X a set of literals. The X-reduct
of P , denoted P X , is the program obtained from P by

ffl deleting each rule defeated by X, and ffl replacing each remaining rule
r with its monotonic counterpart M on(r).

Definition 2 Let R be a set of rules without negation as failure. Cn(R) denotes
the smallest set of literals that is

1. closed under R, and

21

Brewka 2. logically closed, i.e., either consistent or equal to the set of
all literals. Definition 3 Let P be a logic program, X a set of literals.
Define an operator flP as follows:

flP (X) = Cn(P X) X is an answer set of P iff X = flP (X). A literal l is
a consequence of a program P under answer set semantics, denoted l 2 Ans(P
), iff l is contained in all answer sets of P .

The second major semantics for extended logic programs, well-founded semantics,
is an inherently skeptical semantics that refrains from drawing conclusions
whenever there is a potential conflict. The original formulation of well-founded
semantics for general logic programs by Gelder, Ross and Schlipf (1991) is
based on a certain partial model. Przymusinski reconstructed this definition
in 3-valued logic (Przymusinski, 1990). The formulation using an anti-monotone
operator was first given by Baral and Subrahmanian (1991) for general logic
programs together with a corresponding definition for default logic. The
straightforward extension of this formulation (respectively, the restriction
of the default logic definition) to extended logic programs that will be
introduced now was used by several authors, e.g. (Baral & Gelfond, 1994;
Lifschitz, 1996).1 Note that in this paper we will only consider the literals
that are true in the corresponding 3-valued semantics.

Like answer set semantics the well-founded semantics for extended logic programs
is based on the operator flP However, the operator is used in a totally different
way. Since flP is anti-monotone the function Gamma P = (flP )2 is monotone.
According to the famous KnasterTarski theorem (Tarski, 1955) every monotone
operator has a least fixpoint. The set of well-founded conclusions of a program
P , denoted W F S(P ), is defined to be this least fixpoint of Gamma P .
The fixpoint can be approached from below by iterating Gamma P on the empty
set. In case P is finite this iteration is guaranteed to actually reach the
fixpoint.

The intuition behind this use of the operator is as follows: whenever flP
is applied to a set of literals X known to be true it produces the set of
all literals that are still potentially derivable. Applying it to such a
set of potentially derivable literals it produces a set of literals known
to be true, often larger than the original set X. Starting with the empty
set and iterating until the fixpoint is reached thus produces a set of true
literals. It can be shown that every well-founded conclusion is a conclusion
under the answer set semantics. Well-founded semantics can thus be viewed
as an approximation of answer set semantics.

Unfortunately it turns out that for many programs the set of well-founded
conclusions is extremely small and provides a very poor approximation of
answer set semantics. Consider the following program P0 which has also been
discussed by Baral and Gelfond (1994):

1) b  not :b 2) a  not :a 3) :a  not a

1. Pereira and Alferes (1992) introduced a version of well-founded semantics
that adheres to the so-called

coherence principle which requires that strong negation implies weak negation.
We will show later in Sect. 3 how the coherence principle can be introduced
in our approach.

22

Extended Logic Programs with Dynamic Preferences The set of well-founded
conclusions is empty since flP0 (;) equals Lit, the set of all literals,
and the Lit-reduct of P0 contains no rule at all. This is surprising since,
intuitively, the conflict between 2) and 3) has nothing to do with :b and
b.

This problem arises whenever the following conditions hold:

1. a complementary pair of literals is provable from the monotonic counterparts
of the

rules of a program P , and

2. there is at least one proof for each of the complementary literals whose
rules are not

defeated by Cn(P 0), where P 0 consists of the "strict" rules in P , i.e.,
those without negation as failure.

In this case well-founded semantics concludes l iff l 2 Cn(P 0). It should
be obvious that such a situation is not just a rare limiting case. To the
contrary, it can be expected that many commonsense knowledge bases will give
rise to such undesired behaviour. For instance, assume a knowledge base contains
information that birds normally fly and penguins normally don't, expressed
as the set of ground instances of the following rule schemata:

1) f ly(x)  not :f ly(x); bird(x) 2) :f ly(x)  not f ly(x); penguin(x)

Assume further that the knowledge base contains the information that Tweety
is a penguin bird. Now if neither f ly(T weety) nor :f ly(T weety) follows
from strict rules in the knowledge base we are in the same situation as with
P0: well-founded semantics does not draw any "defeasible" conclusion, i.e.
a conclusion derived from a rule with weak negation in the body, at all.

We want to show that a minor reformulation of the fixpoint operator can overcome
this intolerable weakness and leads to much better results. Consider the
following operator

fl?P (X) = Cl(P X ) where Cl(R) denotes the minimal set of literals closed
under the (classical) rules R. Cl(R) is thus like Cn(R) without the requirement
of logical closedness. Now define

Gamma ?P (X) = flP (fl?P (X)) Again we iterate on the empty set to obtain
the well-founded conclusions of a program P which we will denote W F S?(P
).

Consider the effects of this modification on our example P0. fl?P

0(;) = fa; :a; bg. Rule 1)is contained in the fa; :a; bg-reduct of P

0 and thus Gamma ?P0(;) = fbg. Since b is also the only literal contained
in all answer sets of P0 our approximation actually coincides with answer

set semantics in this case.

In the Tweety example both f ly(T weety) and :f ly(T weety) are provable
from the ;-reduct of the knowledge base. However, this has no influence on
whether a rule not containing the weak negation of one of these two literals
in the body is used to produce fl?P (;) or not. The effect of the conflicting
information about Tweety's flying ability is thus kept local and does not
have the disastrous consequences it has in the original formulation of well-founded
semantics.

23

Brewka It is not difficult to see that the new monotone operator is equivalent
to the original one whenever P does not contain negation as failure. In this
case the X-reduct of P , for arbitrary X, is equivalent to P and for this
reason it does not make any difference whether to use flP or fl?P as the
operator to be applied first in the definition of Gamma P . The same is
obviously true for programs without classical negation: for such programs
Cn can never produce complementary pairs of literals and for this reason
the logical closedness condition is obsolete.

In the general case the new operator produces more conclusions than the original
one:

Proposition 1 Let P be an extended logic program. For an arbitrary set of
literals X we have

Gamma P (X) ` Gamma ?P (X):

Proof: We have fl?P (X) ` flP (X), thus P flP (X) ` P fl

? P (X). From this the result follows

immediately. 2

It remains to be shown that the new operator produces no unwanted results,
i.e., that our new semantics can still be viewed as an approximation of answer
set semantics.

Proposition 2 Let P be an extended logic program. Let Ans(P ) be the set
of literals contained in all answer sets of P . W F S? is correct wrt. answer
set semantics, i.e., W F S?(P ) ` Ans(P ).

Proof: The proposition is trivially satisfied whenever P has no answer set
at all, or when Lit is the single answer set of P . So assume P possesses
a non-empty set of consistent answer sets, the only remaining possibility
according to results in (Gelfond & Lifschitz, 1990).

To show that iterating Gamma ?P on the empty set cannot produce a literal
s 62 Ans(P ) it suffices to show that X ` Ans(P ) implies Gamma ?P (X) `
Ans(P ).

Let A be an arbitrary answer set and assume X ` Ans(P ). Since X ` A we have
P A ` P X . Since by assumption A is consistent we have A = Cn(P A) ` Cl(P
X). Therefore Gamma ?P (X) = Cn(P Cl(P

X)) ` Cn(P A) = A.

2

For the rest of the paper a minor reformulation turns out to be convenient.
Instead of using the monotonic counterparts of undefeated rules we will work
with the original rules and extend the definitions of the two operators Cn
and Cl accordingly, requiring that weakly negated preconditions be neglected,
i.e., for an arbitrary set of rules P with weak negation we define Cn(P )
= Cn(M on(P )) and Cl(P ) = Cl(M on(P )). We can now equivalently characterize
flP and fl?P by the equations

flP (X) = Cn(PX)

fl?P (X) = Cl(PX ) where PX denotes the set of rules not defeated by X.

Before we turn to the treatment of preferences we give an alternative characterization
of Gamma ?P based on the following notion:

Definition 4 Let P be a logic program, X a set of literals. A rule r is X-safe
wrt. P (r 2 SAF EX (P )) if r is not defeated by fl?P (X) or, equivalently,
if r 2 Pfl?P (X).

24

Extended Logic Programs with Dynamic Preferences With this new notion we
can obviously characterize Gamma ?P as follows:

Gamma ?P (X) = Cn(P fl

? P (X)) = Cn(Pfl?

P (X)) = Cn(SAF EX(P ))

It is this last formulation that we will modify later. More precisely, the
notion of X-safeness will be weakened to handle preferences adequately.

3. Adding Preferences In order to handle preferences we need to be able to
express preference information explicitly. Since we want to do this in the
logical language we have to extend the language. We do this in two respects:

1. we use a set of rule names N together with a naming function name to be
able to

refer to particular rules,

2. we use a special (infix) symbol OE that can take rule names as arguments
to represent

preferences among rules.

Intuitively, n1 OE n2 where n1 and n2 are rule names means the rule with
name n1 is preferred over the rule with name n2.2

A prioritized logic program is a pair (R; name) where R is a set of rules
and name a naming function. To make sure that the symbol OE has its intended
meaning, i.e., represents a transitive and anti-symmetric relation, we assume
that R contains all ground instances of the schemata

N1 OE N3  N1 OE N2; N2 OE N3

and

:(N2 OE N1)  N1 OE N2

where Ni are parameters for names. Note that in our examples we won't mention
these rules explicitly.

The function name is a partial injective naming function that assigns a name
n 2 N to some of the rules in R. Note that not all rules do necessarily have
a name. The reason is that names will only play a role in conflict resolution
among defeasible rules, i.e., rules with weakly negated preconditions. For
this reason names for strict rules, i.e., rules in which the symbol not does
not appear, won't be needed. A technical advantage of leaving some rules
unnamed is that the use of rule schemata with parameters for rule names does
not necessarily make programs infinite. If we would require names for all
rules we would have to use a parameterized name for each schema and thus
end up with an infinite set N of names.

In our examples we assume that N is given implicitly. We also define the
function name implicitly. We write:

ni : c  a1; : : : ; an; not b1; : : : ; not bm

to express that name(c  a1; : : : ; an; not b1; : : : ; not bm) = ni.

2. Note that for historical reasons we follow the convention that the minimal
rules are the preferred ones.

25

Brewka For convenience we will simply speak of programs instead of prioritized
logic programs whenever this does not lead to misunderstandings.

Before introducing new definitions we would like to point out how we want
the new explicit preference information to be used. Our approach follows
two principles:

1. we want to extend well-founded semantics, i.e. we want that every W F
S?-conclusion

remains a conclusion in the prioritized approach,

2. we want to use preferences to solve conflicts whenever this is possible
without violating

principle 1.

Let us first explain what we mean by conflict here. Rules may be conflicting
in several ways. In the simplest case two rules may have complementary literals
in their heads. We call this a type-I conflict. Conflicts of this type may
render the set of well-founded conclusions inconsistent, but do not necessarily
do so. If, for instance, a precondition of one of the rules is not derivable
or a rule is defeated the conflict is implicitly resolved. In that case the
preference information will simply be neglected. Consider the following program
P1:

n1 : b  not c n2 : :b  not b n3 : n2 OE n1

There is a type-I conflict between n1 and n2. Although the explicit preference
information gives precedence to n2 we want to apply n1 here to comply with
the first of our two principles. Technically, this means that we can apply
a preferred rule r only if we are sure that r's application actually leads
to a situation where literals defeating r can no longer be derived.

The following two rules exhibit a different type of conflict:

a  not b b  not a

The heads of these rules are not complementary. However, the application
of one rule defeats the other and vice versa. We call this a direct type-II
conflict. Of course, in the general case the defeat of the conflicting rule
may be indirect, i.e. based on the existence of additional rules. We say
r1 and r2 are type-II conflicting wrt. a set of rules R iff

1. Cl(R) neither defeats r1 nor r2, 2. Cl(R + r1) defeats r2, and 3. Cl(R
+ r2) defeats r1 Here R + r abbreviates R [ frg. A direct type-II conflict
is thus a type-II conflict wrt. the empty set of rules. The rule sets R that
have to be taken into account in our well-founded semantics based approach
are subsets of the rules which are undefeated by the set of literals known
to be true. Note that the two types of conflict are not disjoint, i.e. two
rules may be in conflict of both type-I and type-II. Consider the following
program P2, a slight modification of P1:

26

Extended Logic Programs with Dynamic Preferences n1 : b  not c; not :b n2
: :b  not b n3 : n2 OE n1

Now we have a type-II conflict between n1 and n2 (more precisely, a direct
type-II and a type-I conflict) that is not solvable by the implicit mechanisms
of well-founded semantics alone. It is this kind of conflict that we try
to solve by the explicit preference information. In our example n2 will be
used to derive :b. Note that now the application of n2 defeats n1 and there
is no danger that a literal defeating n2 might become derivable later. Generally,
a type-II conflict between r1 and r2 (wrt. some undefeated rules of the program)
will be solved in favour of the preferred rule, say r1, only if applying
r1 excludes any further possibility of deriving an r1-defeating literal.

Note that every type-I conflict can be turned into a direct type-II conflict
by a (nonequivalent!) rerepresentation of the rules: if each conflicting
rule r is replaced by its seminormal form3 then all conflicts become type-II
conflicts and are thus amenable to conflict resolution through preference
information.

After this motivating discussion let us present the new definitions. Our
treatment of priorities is based on a weakening of the notion of X-safeness.
In Sect. 2 we considered a rule r as X-safe whenever there is no proof for
a literal defeating r from the monotonic counterparts of X-undefeated rules.
Now in the context of a prioritized logic program we will consider a rule
r as X-safe if there is no such proof from monotonic counterparts of a certain
subset of the X-undefeated rules. The subset to be used depends on the rule
r and consists of those rules that are not "dominated" by r. Intuitively,
r0 is dominated by r iff r0 is (1) known to be less preferred than r and
(2) defeated when r is applied together with rules that already have been
established to be X-safe. (2) is necessary to make sure that explicit preference
information is used the right way, according to our discussion of P1.

It is obvious that whenever there is no proof for a defeating literal from
all X-undefeated rules there can be no such proof from a subset of these
rules. Rules that were X-safe according to our earlier definition thus remain
to be X-safe. Here are the precise definitions:

Definition 5 Let P = (R; name) be a prioritized logic program, X a set of
literals, Y a set of rules, and r 2 R. The set of rules dominated by r wrt.
X and Y , denoted DomX;Y (r), is the set

fr0 2 R j name(r) OE name(r0) 2 X and Cl(Y + r) defeats r0g

Note that DomX;Y (r) is monotonic in both X and Y . We can now define the
X-safe rules inductively:

Definition 6 Let P = (R; name) be a prioritized logic program, X a set of
literals. The set of X-safe rules of P , denoted SAF EprX (P ), is defined
as follows: SAF EprX (P ) = S1i=0 Ri, where

R0 = ;, and for i ? 0, Ri = fr 2 R j r not defeated by Cl(RX n DomX;RiGamma
1(r))g

3. The seminormal form of c  a1; : : : ; an; not b1; : : : ; not bm is

c  a1; : : : ; an; not b1; : : : ; not bm; not c

0

where c

0 is the complement of c. The term seminormal is taken from Reiter (1980).

27

Brewka Note that X-safeness is obviously monotonic in X. Based on this notion
we introduce a new monotonic operator Gamma prP :

Definition 7 Let P = (R; name) be a prioritized logic program, X a set of
literals. The operator Gamma prP is defined as follows:

Gamma prP (X) = Cn(SAF EprX (P )) As before we define the (prioritized)
well-founded conclusions of P , denoted W F Spr(P ), as the least fixpoint
of Gamma prP . If a program does not contain preference information at all,
i.e., if the symbol OE does not appear in R, the new semantics coincides
with W F S? since in that case no rule can dominate another rule. In the
general case, since the new definition of X-safeness is weaker than the one
used earlier in Sect. 2 we may have more X-safe rules and for this reason
obtain more conclusions than via Gamma ?P . The following result is thus
obvious:

Proposition 3 Let P = (R; name) be a prioritized logic program. For every
set of literals X we have Gamma ?R(X) ` Gamma prP (X).

From this and the monotonicity of both operators it follows immediately that
W F S?(R) ` W F Spr(P ).4

Well-founded semantics has sometimes been criticized for being too weak and
missing intended conclusions. The proposition shows that we can strengthen
the obtained results by adding adequate preference information.

As a first simple example let us consider the following program P3:

n1 : b  not c n2 : c  not b n3 : n2 OE n1

We first apply Gamma prP

3 to the empty set. Besides the instances of the transitivity and anti-symmetry
schema that we implicitly assume only n

3 is in SAF Epr; (P3). We thus obtain

S1 = fn2 OE n1; :(n1 OE n2)g We next apply Gamma prP

3 to S1. Since n2 OE n1 2 S1 we have n1 2 DomS1;;(n2). n2 2 SAF E

pr S1 (P3)since

Cl(P3S

1 n fn1g) does not defeat n2 and we obtain

S2 = fn2 OE n1; :(n1 OE n2); cg

Further iteration of Gamma prP

3 yields no new literals, i.e. S2 is the least fixpoint. Note that c is nota
conclusion under the original well-founded semantics.

We next show that the programs P1 and P2 discussed earlier are handled as
intended. Here is P1:

4. Pereira and Alferes (1992) argue that each extension of well-founded semantics
to two types of negation

should satisfy what they call coherence principle: a weakly negated precondition
should be considered satisfied whenever the corresponding strongly negated
literal is derived. To model this principle in our approach one would have
to weaken the notion of X-safeness even further. In the inductive definition,
a rule r would have to be considered a member of Ri whenever for each weak
precondition not b of r

ffl b 62 Cl(RX n DomX;Ri

Gamma 1(r)), or

ffl b

0 2 X, where b0 = :b if b is an atom and a if b = :a.

28

Extended Logic Programs with Dynamic Preferences n1 : b  not c n2 : :b  not
b n3 : n2 OE n1

Since fl?P1(;) does not defeat n1 this rule is safe from the beginning, i.e.,
n1 2 SAF Epr; (P1). Gamma prP (;) yields

fn2 OE n1; :(n1 OE n2); bg

which is also the least fixpoint. The explicit preference does not interfere
with the implicit one, as intended.

The situation changes in P2 where the first rule in P1 is replaced by

n1 : b  not c; not :b The new rule n1 is not in SAF Epr; (P2) since it is
defeated by the consequence of n2 and n2 is not dominated by n1. Gamma prP

2 (;) yields

S1 = fn2 OE n1; :(n1 OE n2)g

Now n2 2 SAF EprS

1 (P2) since n2 dominates n1 wrt. S1 and the empty set of rules. We thusconclude
:b as intended. The least fixpoint is

S2 = fn2 OE n1; :(n1 OE n2); bg In (Brewka, 1994b) we used an example to
illustrate the possible non-existence of extensions in our earlier approach.
This example involved two normal defaults each of which had the conclusion
that the other one is to be preferred. The prioritized logic programming
representation of this example is the following:

n1 : n2 OE n1  not :(n2 OE n1) n2 : n1 OE n2  not :(n1 OE n2)

It is straightforward to verify that the set of well-founded conclusions
for this example is empty.

4. A Legal Reasoning Example In this section we want to show that the additional
expressiveness provided by our approach actually helps representing real
world problems. We will use an example first discussed by Gordon (1993, p.7).
We somewhat simplified it for our purposes. The same example was also used
in (Brewka, 1994b) to illustrate the approach presented there.

Assume a person wants to find out if her security interest in a certain ship
is perfected. She currently has possession of the ship. According to the
Uniform Commercial Code (UCC, x9-305) a security interest in goods may be
perfected by taking possession of the collateral. However, there is a federal
law called the Ship Mortgage Act (SMA) according to which a security interest
in a ship may only be perfected by filing a financing statement. Such a statement
has not been filed. Now the question is whether the UCC or the SMA takes
precedence in this case. There are two known legal principles for resolving
conflicts of this kind. The principle of Lex Posterior gives precedence to
newer laws. In our case the UCC

29

Brewka is newer than the SMA. On the other hand, the principle of Lex Superior
gives precedence to laws supported by the higher authority. In our case the
SMA has higher authority since it is federal law.

The available information can nicely be represented in our approach. To make
the example somewhat shorter we use the notation

c ( a1; : : : ; an; not b1; : : : ; not bm as an abbreviation for the rule

c  a1; : : : ; an; not b1; : : : ; not bm; not c0 where c0 is the complement
of c, i.e. :c if c is an atom and a if c = :a. Such rules thus correspond
to semi-normal or, if m = 0, normal defaults in Reiter's default logic (Reiter,
1980).

We use the ground instances of the following named rules to represent the
relevant article of the UCC, the SMA, Lex Posterior (LP), and Lex Superior
(LS). The symbols d1 and d2 are parameters for rule names:

U CC : perf ected ( possession SM A : :perf ected ( ship; :f in-statement
LP (d1; d2) : d1 OE d2 ( more-recent(d1; d2) LS(d1; d2) : d1 OE d2 ( f ed-law(d1);
state-law(d2)

The following facts are known about the case and are represented as rules
without body (and without name):

possession ship :f in-statement more-recent(U CC; SM A) f ed-law(SM A) state-law(U
CC)

Let's call the above set of literals H. Iterated application of Gamma prP
yields the following sequence of literal sets (in each case Si = (Gamma
prP )i(;)):

S1 = H S2 = S1

The iteration produces no new results besides the facts already contained
in the program. The reason is that UCC and SMA block each other, and that
no preference information is produced since also the relevant instances of
Lex Posterior and Lex Superior block each other. The situation changes if
we add information telling us how conflicts between the latter two are to
be resolved. Assume we add the following information:5

LS(SM A; U CC) OE LP (U CC; SM A) 5. In realistic settings one would again
use a schema here. In order to keep the example simple we use the

relevant instance of the schema directly.

30

Extended Logic Programs with Dynamic Preferences Now we obtain the following
sequence:

S1 = H [ fLS(SM A; U CC) OE LP (U CC; SM A);

:LP (U CC; SM A) OE LS(SM A; U CC)g S2 = S1 [ fSM A OE U CC; :U CC OE SM
Ag S3 = S2 [ f:perf ectedg S4 = S3

This example nicely illustrates how in our approach conflict resolution strategies
can be specified declaratively, by simply asserting relevant preferences
among the involved conflicting rules.

5. Complexity The time complexity of well-founded semantics for a general
logic program P is known to be quadratic in the size of P , a result attributed
to folklore in (Baral & Gelfond, 1994). A proof was given by Witteveen (1991).
His analysis is based on Dowling and Gallier's result whereby satisfiability
of Horn clauses can be tested in linear time (Dowling & Gallier, 1984). In
Dowling and Gallier's approach it is actually a minimal model of a Horn theory
that is computed in linear time. Since minimal models of Horn theories are
equivalent to closures of rules without negation the result is directly applicable
to well-founded semantics for general logic programs. It also applies to
well-founded semantics for extended logic programs since for the computation
of the least fixed point of Gamma P respectively Gamma ?P the complementary
literals l and :l can be viewed as two distinct atoms.

For the complexity analysis of our prioritized approach let n be the number
of rules in a prioritized program P = (R; name). A straightforward implementation
would model the application of Gamma prP in an outer loop and the computation
of SAF EprX in an inner loop. Fortunately, we can combine the two loops into
a single loop whose body is executed at most n times. The reason is that
SAF EprX grows monotonically with X and Gamma prP grows monotonically with
SAF EprX . Here is a nondeterministic algorithm for computing the least fixed
point of Gamma prP :

Procedure WFS+ Input: A prioritized logic program P = (R; name) with jRj
= n Output: the least fixed point of Gamma prP S0 := ;; R0 := ;; for i =
1 to n do

if there is a rule r 2 RSiGamma 1 n RiGamma 1 such that Cl(RSiGamma 1
n DomSiGamma 1;RiGamma 1 (r)) does not defeat r then Ri := RiGamma 1 +
r; Si := Cn(Ri) else return SiGamma 1

endfor end WFS+

31

Brewka In each step Si and Ri denote the well-founded conclusions, respectively
safe rules established so far. The body of the for-loop is executed at most
n times and there are at most n rules that have to be checked for satisfaction
of the if-condition. The if-condition itself can, according to the results
of Dowling and Gallier, be checked in linear time: we need to establish DomSiGamma
1;RiGamma 1(r) which involves the computation of a minimal model of the
monotonic counterparts of RiGamma 1 + r. We then have to eliminate the rules
dominated by r form RSiGamma 1 and compute another minimal model to see
whether r is defeated.

More precisely, Dowling and Gallier show that the needed time is linear in
the number of propositional constants. This number may be greater than n
in principle. However, since literals that do not appear in the head of a
rule must be false in the minimal model we can eliminate them accordingly
and work with a set of rules that has at most n literals. This leads to an
overall time complexity of O(n3).

It should be mentioned, however, that due to the use of rule schemata for
transitivity and anti-symmetry of OE prioritized programs can be considerably
larger than corresponding unprioritized programs. The transitivity schema,
for instance, has jN j3 instances. An implementation should, therefore, be
based on an approach where instances are only generated when actually needed,
or on other built in techniques that handle transitivity and anti-symmetry.
Such techniques are beyond the scope of this paper.

6. Relation to Answer Sets In this section we will investigate the relation
of our modification of well-founded semantics to answer set semantics (Gelfond
& Lifschitz, 1990). Since our approach handles an extended language in which
certain symbols are given a particular pre-defined meaning a thorough investigation
of this relationship is only possible after a corresponding extension of
answer set semantics to prioritized logic programs has been defined. We are
not planning to introduce and defend such an extension in this paper. Nevertheless,
we can give some preliminary results here. More precisely, we will show that
the conclusions produced in our proposal are correct wrt. a particular subclass
of answer sets, the so-called priority-preserving answer sets.

Definition 8 Let R be a logic program, A an answer set of R, and let

r = c  a1; : : : ; an; not b1; : : : ; not bm be a rule in R. We say r is
rebutted in A (r 2 reR(A)) iff fa1; : : : ; ang ` A and r is defeated in
A.

Definition 9 Let P = (R; name) be a prioritized logic program, A an answer
set of R. A is called priority preserving iff for every r 2 reR(A) the set

Cl(RA n DomA;RA(r)) defeats r. The intuition behind the definition is the
following: whenever a rule r is rebutted in an answer set A but its rebuttal
is solely based on rules dominated by r with respect to A

32

Extended Logic Programs with Dynamic Preferences and the rules not defeated
by A we consider this as a violation of the available preference information
and "reject" the answer set.

We can now show correctness of our approach wrt. priority preserving answer
sets.

Proposition 4 Let P = (R; name) be a prioritized logic program. l 2 W F Spr(P
) implies l is contained in all priority preserving answer sets of R.

Proof: The proof is similar to the correctness proof of W F S? wrt. answer
set semantics (Proposition 2). Again the proposition is trivially satisfied
whenever there is no priority preserving answer set, or Lit is the single
priority preserving answer set. We may therefore assume that every priority
preserving answer set of R is consistent.

In the inductive step we show that for an arbitrary priority preserving answer
set A a rule r is not defeated in A whenever r 2 SAF EprX (P ), given that
X is a set of literals true in A. From this it follows that Cn(SAF EprX (P
)) contains only literals true in all priority preserving answer sets.

Let Ri be defined as in Def. 6 (the inductive definition of X-safeness) and
assume it is already known that the rules in RiGamma 1 are not defeated
in A. By definition r = c  a1; : : : ; an; not b1; : : : ; not bm 2 Ri iff
r is not defeated by Cl(RX n DomX;RiGamma 1 (r)). We distinguish two cases:

Case 1: a1; : : : ; an 2 A: Since X ` A and Dom is monotonic in both indices
we have Cl(RA n DomA;RA(r)) ` Cl(RX n DomX;RiGamma 1 (r)). Therefore r cannot
be defeated in A since A is priority preserving.

Case 2: a1; : : : ; an 62 A: Since the prerequisites of r cannot be derived
from RA the set DomA;RA(r) contains only rules defeated by Cl(RA) alone.
Since A is an answer set these rules can't be contained in RA. Therefore
Cl(RA) = Cl(RA n DomA;RA(r)) and thus Cl(RA) ` Cl(RX n DomX;RiGamma 1 (r)).
Since by assumption A is consistent we also have Cl(RA) = Cn(RA) and therefore
r cannot be defeated in A. 2

We have seen that our approach is guaranteed to produce only conclusions
contained in all priority preserving answer sets. We can also ask the opposite
question: given a particular answer set A, is it always possible to obtain
A (or, more precisely, a superset of A containing additional preference information)
through prioritized well-founded semantics by adding adequate preference
information?

The answer to this question is no. The reason is that for sake of tractability
we always consider single rules when determining X-safeness in our approach.
Here is an example:

n1 : b  not a n2 : c  not b n3 : d  not c n4 : a  not d

This program has two answer sets S1 = fb; dg and S2 = fc; ag. Consider S1.
Even if we add the preference information that both n1 and n3 are preferred
to each of n2 and n4 we are unable to derive b and d. For instance, n1 is
not X-safe because its head does not defeat n4.

In order to derive S1 it would be necessary to take the possibility of sets
of rules (here n1 and n3) defeating less preferred sets of rules (here n2
and n4) into account. Although this is possible in principle it would clearly
lead to intractability since in the worst case an

33

Brewka exponential number of subsets of rules would have to be checked. Giving
up tractability seems too high a price for what is gained and we stick to
our more cautious approach for this reason.

7. Related Work and Conclusions Several approaches treating preferences in
the context of logic programming have been described in the literature. We
will now discuss how they relate to our proposal.

Kowalski and Sadri (1991) proposed to consider rules with negation in the
head as exceptions to more general rules and to give them higher priority.
Technically, this is achieved by a redefinition of answer sets. It turns
out that the original answer sets remain answer sets according to the new
definition whenever they are consistent. The main achievement is that programs
whose single answer set is inconsistent become consistent in the new semantics.
The approach can hardly be viewed as a satisfactory treatment of preferences
for several reasons:

1. preferences are implicit and highly restricted; the asymmetric treatment
of positive

and negative information seems unjustified,

2. it is difficult to see how, for instance, exceptions of exceptions can
be represented, 3. fewer conclusions are obtained than in the original answer
set semantics, contrary to

what one would expect when preferences are taken into account.

It is, therefore, more reasonable to view Kowalski and Sadri's approach as
a contribution to inconsistency handling rather than preference handling.

An approach that is closer in spirit to ours is ordered logic programming
(Buccafurri, Leone, & Rullo, 1996). An ordered logic program is a set of
components forming an inheritance hierarchy. Each component consists of a
set of rules. The inheritance hierarchy is used to settle conflicts among
rules: rules lower in the hierarchy have preference over those higher up
in the hierarchy since the former are considered more specific. A notion
of a stable model for ordered logic programs can be defined (see Buccafurri
et al., 1996, for the details).

There are two main differences between ordered logic programs and our extension
of well-founded semantics:

1. ordered logic programs use only one kind of negation, the distinction
between negation

as failure and classical negation is not expressible in the language,

2. the preferences of ordered logic programs are predefined through the inheritance
hierarchy, there is no way of deriving context-dependent preferences dynamically.

Finally we would like to mention an approach recently presented by Prakken
and Sartor (1995). They extend Dung's argument system style reconstruction
of logic programming (Dung, 1993) with a preference handling method that
is very close to ours. This is not astonishing since, as the authors point
out, their approach is based on "unpublished ideas of Gerhard Brewka". In
fact, it was a preliminary version of this paper that led to their formulation.

34

Extended Logic Programs with Dynamic Preferences We presented in this paper
an extension of logic programs with two types of negation where preference
information among rules can be expressed in the logical language. This extension
is very useful for practical applications, as was demonstrated using an example
from legal reasoning. The main advantage of our approach is that also this
type of information is context-dependent and can be reasoned upon and derived
dynamically.

From well-founded semantics we inherit some drawbacks and advantages. Sometimes
reasonable conclusions are not obtained. On the other hand, the addition
of preference information can make the set of conclusions considerably larger,
as we have shown. Moreover, - and this certainly is the greatest advantage
of well-founded semantics and our proposed extension - reasoning can be done
in polynomial time.

The simple and natural representation of the legal example discussed in Sect.
4 seems to indicate that our generalization of well-founded semantics may
provide a new attractive compromise between expressiveness and efficiency
with a number of interesting potential applications.

Acknowledgements I would like to thank Franz Baader, J"urgen Dix, Tom Gordon,
Henry Prakken, Cees Witteveen, and two anonymous referees for interesting
comments helping to improve the quality of this paper.

References Baral, C., & Gelfond, M. (1994). Logic programming and knowledge
representation. Journal

of Logic Programming, 19,20, 73-148.

Baral, C., & Subrahmanian, V. (1991). Duality between alternative semantics
of logic programs and nonmonotonic formalisms. In Proc. Intl. Workshop on
Logic Programming and Nonmonotonic Reasoning. Springer.

Brewka, G. (1994a). Adding priorities and specificity to default logic. In
Proc. JELIA-94,

York. Springer.

Brewka, G. (1994b). Reasoning about priorities in default logic. In Proc.
AAAI-94, Seattle. Buccafurri, F., Leone, N., & Rullo, P. (1996). Stable models
and their computation for

logic programming with inheritance and true negation. to appear in Journal
of Logic Programming.

Dowling, W., & Gallier, J. (1984). Linear time algorithms for testing the
satisfiability of

propositional horn formulae. Journal of Logic Programming, 3, 267-284.

Dung, P. (1993). The acceptability of arguments and its fundamental role
in nonmonotonic

reasoning and logic programming. In Proc. 13th Intl. Joint Conference on
Artificial Intelligence.

Gelfond, M., & Lifschitz, V. (1990). Logic programs with classical negation.
In Proc. 7th

Intl. Conference on Logic Programming.

35

Brewka Gordon, T. F. (1993). The Pleadings Game: An Artificial Intelligence
Model of Procedural

Justice. Ph.D. thesis, TU Darmstadt.

Konolige, K. (1988). Hierarchic autoepistemic theories for nonmonotonic reasoning.
In

Proc. AAAI.

Kowalski, R., & Sadri, F. (1991). Logic programs with exceptions. New Generation
Computing, 9, 387-400.

Lifschitz, V. (1985). Computing circumscription. In Proc. 9th Int. Joint
Conference on

Artificial Intelligence.

Lifschitz, V. (1996). Foundations of declarative logic programming. In Brewka,
G. (Ed.),

Principles of Knowledge Representation. CSLI publishers, Studies in Logic,
Language and Information.

Pereira, L., & Alferes, J. (1992). Well founded semantics for logic programs
with explicit

negation. In Proc. 10th European Conference on Artificial Intelligence, Vienna.

Poole, D. (1985). On the comparison of theories: Preferring the most specific
explanation.

In Proc. IJCAI-85, Los Angeles.

Prakken, H. (1993). Logical Tools for Modelling Legal Argument. Ph.D. thesis,
VU Amsterdam.

Prakken, H., & Sartor, G. (1995). On the relation between legal language
and legal argument: Assumptions, applicability and dynamic priorities. In
Proc. International Conference in AI and Law, Washington.

Przymusinski, T. (1990). The well-founded semantics coincides with the three-valued
stable

semantics. Fundamenta Informaticae, 13(4), 445-464.

Przymusinski, T. (1991). Stable semantics for disjunctive programs. New Generation
Computing, 9, 401-424.

Reiter, R. (1980). A logic for default reasoning. Artificial Intelligence,
13, 81-132. Tarski, A. (1955). A lattice-theoretical fixpoint theorem and
its applications. Pacific Journal

of Mathematics, 5, 285-309.

Touretzky, D. S. (1986). The Mathematics of Inheritance. Pitman Research
Notes in

Artificial Intelligence, London.

Touretzky, D. S., Thomason, R. H., & Horty, J. F. (1991). A skeptic's menagerie:
Conflictors,

preemptors, reinstaters, and zombies in nonmonotonic inheritance. In Proc.
12th IJCAI, Sydney.

van Gelder, A., Ross, K., & Schlipf, J. (1991). The well-founded semantics
for general logic

programs. Journal of the ACM, 38(3), 620-650.

Witteveen, C. (1991). Partial semantics for truth maintenance. In van Eijck,
J. (Ed.),

Logics in AI, pp. 544-561. Springer LNAI 478, Heidelberg.

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