Abstract
We propose to apply a variant of forgetting, a simple method to restore consistency, in order to get a new inconsistency measure from the following intuitive idea: How much effort is needed to restore consistency of a knowledge base is presumably indicative of how inconsistent the knowledge base is. We discuss properties of the inconsistency measure obtained, in particular in the face of well-known postulates for inconsistency measures. We also mention in what sense this new measure does not fall into the dichotomy of inconsistency measures proposed in the literature: alphabet-based approaches vs formula-based approaches.
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1 Introduction
Inconsistency measures have gained much interest recently (Ammoura et al. 2015; Grant and Hunter 2013; Hunter et al. 2014; Jabbour et al. 2015, 2016; Liu and Mu 2016; McAreavey et al. 2014; Mu et al. 2012; Mu 2015; Thimm 2013, 2016a, 2016b; Thimm and Wallner 2016; Xiao and Ma 2012). An inconsistency measure ascribes a quantity to a logical knowledge base, a quantity which is meant to tell how inconsistent the knowledge base is. Here, we apply forgetting (Lin and Reiter 1994), a well-known method to restore consistency (Lang and Marquis 2002), in order to get a new inconsistency measure from the following idea: How much effort is needed to restore consistency of a knowledge base is presumably indicative of how inconsistent the knowledge base is.
1.1 Formal Preliminaries
By a knowledge base, we mean a finite multiset of formulas of propositional logic. We use \(\lnot \), \(\wedge \), \(\vee \) to denote the usual Boolean connectives: negation, conjunction, disjunction. We use Greek letters \(\varphi , \psi , \ldots \), whether indexed or not, to denote formulas of propositional logic. We use capital Greek letters \(\varDelta , \varGamma \),... to denote knowledge bases (i.e., multisets of formulas of propositional logic, as just said). We use \(\kappa \) (which is introduced in Definition 1) to denote forgetting: Intuitively, \(\kappa \) “forgets” occurrences of a propositional variable.
2 An Inconsistency Measure
We consider formulas labelled with superscripts denoting occurrences of atoms. Every formula \(\varphi \) in which an unlabelled atom v occurs \(k > 0\) times is identified as \(\varphi (v^1,\ldots ,v^k)\) where \(v^i\) denotes the ith unlabelled occurrence of v in \(\varphi \).Footnote 1
Example. The unlabelled formula \(a\,\wedge \,b\,\wedge \,\lnot \,b\) is identified with the labelled formula \(a^1 \wedge b^1 \wedge \lnot b^2\).
Accordingly, given the list of propositional variables \(\mathcal {P} = v_1, v_2,\ldots \) define the set of atom occurrences as
Importantly, \(v^i\) is identified with \(v^j\) for all purposes (e.g., consistency issues) except for purposes of occurrences of atoms. In particular, a Boolean combination of labelled formulas may display multiple copies of the same atom occurrence e.g., \((a^1 \wedge b^1 \wedge \lnot b^2)\left| \begin{array}{c@{}}{b^{1} \rightarrow \top ,\bot }\end{array}\right. \!\) (see below) is \((a^1 \wedge \top \wedge \lnot b^2) \vee (a^1 \wedge \bot \wedge \lnot b^2)\).
Notation. The symbol \(\rightarrow \) is used for substitution as follows.
denotes the formula resulting from \(\varphi \) by replacing simultaneously each atom occurrence \(v_j^{i_j}\) by \(\psi _j\) (informally speaking, an atom occurrence refers either to an unlabelled occurrence of the atom or to a labelled version of the atom). The abbreviation
is used to denote the disjunction whose each disjunct is the formula obtained from \(\varphi \) by replacing \(v^{i}\) by one of \(\psi _1,\ldots ,\psi _m\) in turn.
Example. Taking \(\varphi \) to be \(a^1 \wedge b^1 \wedge \lnot b^2 \wedge (b^3 \vee \lnot (a^1 \wedge b^1))\), the substitution of \(b^1\) by \(\top \) in \(\varphi \) is denoted by \(\varphi \left| {(\scriptstyle b^{1} \rightarrow ~\top )}\right. \), yielding \(a^1 \wedge \top \wedge \lnot b^2 \wedge (b^3 \vee \lnot (a^1 \wedge \top ))\). That is, all occurrences of \(b^1\) in \(\varphi \) are replaced by occurrences of \(\top \). Also, \(\bigvee \varphi \left| \begin{array}{c}{\scriptstyle b^{1} \rightarrow ~\top ,\bot }\end{array}\right. \) denotes \([a^1 \wedge \top \wedge \lnot b^2 \wedge (b^3 \vee \lnot (a^1 \wedge \top ))] \vee [a^1 \wedge \bot \wedge \lnot b^2 \wedge (b^3 \vee \lnot (a^1 \wedge \bot ))]\).
Definition 1
\(\kappa _{i,v} . \varphi \) is the labelled formula obtained from the labelled formulaFootnote 2 \(\varphi \) by replacing the atom occurrences \(v^{i}\) in \(\varphi \), first by \(\top \), second by \(\bot \), taking the disjunction thereof. In symbols,
For clarity, let us stress that \(\kappa _{i,v} . \varphi \) is a labelled formula hence \(\kappa _{j,u} . (\kappa _{i,v} . \varphi )\) is such that \(\kappa _{j,u}\) introduces no superscript (but it duplicates superscripted atoms).
Lemma 1
Using the substitution notation,
In previous work (Lin and Reiter 1994; Lang and Marquis 2002; Lang et al. 2003) about forgetting, it is shown that consistency can be recovered if enough atoms are forgotten. It is of interest to characterize which occurrences of atoms are enough to consider if consistency is to be recovered.
Definition 2
Define \(\sigma (\varphi )\) as the set of multisets of atom occurrences whose forgetting is enough to turn \(\varphi \) into a consistent formula, in symbols,
Then, the inconsistency number of \(\varphi \) is intuitively the minimum number n of (iterated) applications of \(\kappa \) operators such that i.e. such that \(\varphi \) is turned into a consistent formula.
Definition 3
The inconsistency number of \(\varGamma \) is \(n(\varGamma )\), defined as
Reminder. Before turning to the examples, it is important to repeat that \(v^i\) is identified with \(v^j\) for most purposes, including consistency issues. For instance, in Example 2, \((\top \wedge a^2) \wedge (\lnot a^3 \wedge \lnot a^4)\) is inconsistent because it is identified with \((\top \wedge a^i) \wedge (\lnot a^i \wedge \lnot a^i)\) which is inconsistent, whatever i.
Example 1
Let \(\varGamma _1 = \{a \vee a, \lnot a \vee \lnot a\}\). Then, \(\bigwedge \varGamma _1 = (a^1 \vee a^2) \wedge (\lnot a^3 \vee \lnot a^4)\). \(\{a^1\} \in \sigma (\varGamma _1)\) since \(\kappa _{1,a} . \bigwedge \varGamma _1\) is \([(\top \vee a^2) \wedge (\lnot a^3 \vee \lnot a^4)] \vee [(\bot \vee a^2) \wedge (\lnot a^3 \vee \lnot a^4)]\) which is consistent. Hence \(n(\varGamma _1) = 1\).
Example 2
Let \(\varGamma _2 = \{a \wedge a, \lnot a \wedge \lnot a\}\). Hence, \(\bigwedge \varGamma _2 = (a^1 \wedge a^2) \wedge (\lnot a^3 \wedge \lnot a^4)\). It does not matter whether considering \(a^1\) instead of \(a^2\) and \(a^3\) instead of \(a^4\). \(\kappa _{1,a} . \bigwedge \varGamma _2\) is \([(\top \wedge a^2) \wedge (\lnot a^3 \wedge \lnot a^4)] \vee [(\bot \wedge a^2) \wedge (\lnot a^3 \wedge \lnot a^4)]\) which is inconsistent. \(\kappa _{3,a} . \kappa _{1,a} . \bigwedge \varGamma _2\) is \([(\top \wedge a^2) \wedge (\lnot \top \wedge \lnot a^4)] \vee [(\bot \wedge a^2) \wedge (\lnot \top \wedge \lnot a^4)] \vee [(\top \wedge a^2) \wedge (\lnot \bot \wedge \lnot a^4)] \vee [(\bot \wedge a^2) \wedge (\lnot \bot \wedge \lnot a^4)]\) i.e. \([\bot \vee \bot ] \vee [\bot \vee \bot ]\) which is inconsistent (and so is \(\kappa _{4,a} . \kappa _{1,a} . \bigwedge \varGamma _2\)). However, \(\kappa _{2,a} . \kappa _{1,a} . \bigwedge \varGamma _2\) is \([(\top \wedge \top ) \wedge (\lnot a^3 \wedge \lnot a^4)] \vee [(\bot \wedge \top ) \wedge (\lnot a^3 \wedge \lnot a^4)] \vee [(\top \wedge \bot ) \wedge (\lnot a^3 \wedge \lnot a^4)] \vee [(\bot \wedge \bot ) \wedge (\lnot a^3 \wedge \lnot a^4)]\) i.e. \([(\lnot a^3 \wedge \lnot a^4) \vee \bot ] \vee [\bot \vee \bot ]\), it is consistent. Hence, \(n(\varGamma _2) = 2\).
Keep in mind that n refers to the minimum amount of forgetting needed to restore consistency. E.g., each of the knowledge bases \(\varGamma \) below satisfies \(n(\varGamma ) = 1\).
3 How Inconsistent About v?
Besides determining how inconsistent \(\varGamma \) is, it would be interesting to determine how inconsistent \(\varGamma \) is about some v.
Definition 4
\(n\!\mid _v\!(\varGamma ) \mathop {=}\limits ^{\mathrm {def}} \min \limits _{A \in \sigma (\wedge \tiny {\varGamma })} \mid A \cap \{v\}_\omega \mid \)
Notation. \(\{v\}_\omega \) denotes the multiset consisting of countably many copies of v.
Example 3
Let \(\varGamma _3 = \{(a \wedge \lnot a) \vee (b \wedge \lnot b)\}\). Therefore, \(\{b^1\} \in \sigma (\varGamma _3)\) because \(\kappa _{1,b} . \bigwedge \varGamma _3\) is \([(a^1 \wedge \lnot a^2) \vee (\top \wedge \lnot b^2)] \vee [(a^1 \wedge \lnot a^2) \vee (\bot \wedge \lnot b^2)]\) which is consistent. Hence, \(n\!\mid _a\!(\varGamma _3) = 0\) due to \(\{b^1\} \cap \{a\}_\omega \) being empty. Similarly, \(n\!\mid _b\!(\varGamma _3) = 0\). However, \(n(\varGamma _3) = 1\).
Comment. The reader may be unhappy that \(n\!\mid _v\!(\varGamma ) = 0\) captures both the case that v is involved in no contradiction in \(\varGamma \) and the case (as in Example 3) that v is involved in a contradiction together with at least another atom. There are many ways to change Definition 4, e.g. by considering some liability function l (with the constraint \(l_{v,\sigma }(\varGamma ) > 1\)) so as to alternatively define \(n\!\mid _v\) as follows:
Anyway, knowledge bases can be compared in the following way: \(\varGamma \) is at least as v -inconsistent as \(\varGamma '\) iff \(n\!\mid _v\!(\varGamma ) \ge n\!\mid _v\!(\varGamma ')\).
Lemma 2
If \(n(\varGamma ) \ge n(\varGamma ')\) then there exists an atom v such that \(\varGamma \) is at least as v-inconsistent as \(\varGamma '\).
Lemma 3
\(n(\varGamma ) \ge n(\varGamma ')\) if for every v, \(\varGamma \) is at least as v-inconsistent as \(\varGamma '\).
It is also possible to compare the involvement of atoms in the conflicts of a knowledge base. Therefore, if atoms can be mapped to topics, such a measure \(n\!\mid _v\) would permit to judge whether a topic gives rise to more severe conflicts than some other topic, or to judge whether the overall inconsistency degree of the knowledge base amounts to the inconsistency degree ascribed to such and such topic. (Please keep in mind that “severe” only refers to intensity, there can be a severe conflict about a topic of little importance.)
4 Postulates for Inconsistency Measures
We now turn to examining what postulates are satisfied by our inconsistency measure n. In this respect, a useful lemma is the following one.
Lemma 4
For all \(j \ge h\), if then . If then for all \(j \ge 0\), (hence \(2^\mathcal {A} \subseteq \sigma (\varphi )\) for ).
We begin with considering postulates proposed in Hunter and Konieczny (2010), expressed using I to denote an arbitrary inconsistency measure.
-
\(I(\varGamma ) = 0\) iff (Consistency Null)
-
\(I(\varGamma \cup \varGamma ') \ge I(\varGamma )\) (Monotony)
-
If \(\varphi \) is freeFootnote 3 for \(\varGamma \) then \(I(\varGamma \cup \{\varphi \}) = I(\varGamma )\) (Free Formula Independence)
It happens that our inconsistency measure n satisfies the postulates above. However, n fails the following postulate, also due to (Hunter and Konieczny 2010).
Example 4
Let \(\varGamma = \{\lnot a \wedge \lnot a \wedge \lnot a\}\). Take \(\varphi = a\) and \(\psi = a \wedge a \wedge a\). Then, \(n(\varGamma \cup \{\varphi \}) = 1\) but \(n(\varGamma \cup \{\psi \}) = 3\).
Failure of (Dominance) entails failure wrt the postulate (Besnard 2014) below
Furthermore, our inconsistency measure n satisfies the following postulate, introduced in Besnard (2014).
Keeping in mind that \(\varGamma \) denotes a multiset of formulas, it is easy to check that n satisfies the next postulate also introduced in Besnard (2014).
Since n satisfies both (Monotony) and (Adjunction Invariancy), it satisfies
Similarly, an easy consequence of (Free Formula Independence) is
which our inconsistency measure n satisfies as well as the related postulate (Besnard 2014) below
5 Conclusion
The inconsistency measure introduced in this paper shows two main distinctive features. First, it deals with multisets of formulas. Second, it breaks the dichotomy suggested in Hunter and Konieczny (2010) which splits the universe of inconsistency measures into two categories: inconsistency measures based on minimal inconsistent subsets and inconsistency measures based on the alphabet (i.e., what atoms are involved in conflicts). Indeed, Example 4 is such that the inconsistency value differs in two cases with isomorphic sets of minimal inconsistent subsets and also differs in two cases where the alphabet consists of one propositional symbol.
Notes
- 1.
As to labelling, logical constants \(\top \) and \(\bot \) are not considered atoms: A formula in which either occurs is regarded as labelled if all other atoms in it are superscripted.
- 2.
That is, if \(\varphi \) is unlabelled, it is identified with \(\varphi (v^{1}_1,\ldots ,v^{i_1}_1,\ldots ,v^{1}_p,\ldots ,v^{i_p}_p)\) where \(v_1,\ldots ,v_p\) are all the propositional variables in \(\varphi \).
- 3.
A formula \(\varphi \) is free for \(\varGamma \) iff \(\varDelta \cup \{\varphi \} \vdash \bot \) for no consistent subset \(\varDelta \) of \(\varGamma \).
References
Ammoura, M., Raddaoui, B., Salhi, Y., Oukacha, B.: On measuring inconsistency using maximal consistent sets. In: Destercke, S., Denoeux, T. (eds.) ECSQARU 2015. LNCS, vol. 9161, pp. 267–276. Springer, Switzerland (2015)
Besnard, P.: Remedying inconsistent sets of premises. Approx. Reason. 45(2), 308–320 (2007)
Besnard, P.: Revisiting postulates for inconsistency measures. In: Fermé, E., Leite, J. (eds.) JELIA 2014. LNCS, vol. 8761, pp. 383–396. Springer, Heidelberg (2014)
Grant, J., Hunter, A.: Distance-based measures of inconsistency. In: van der Gaag, L.C. (ed.) ECSQARU 2013. LNCS, vol. 7958, pp. 230–241. Springer, Heidelberg (2013)
Hunter, A., Konieczny, S.: On the measure of conflicts: Shapley inconsistency values. Artif. Intell. 174(14), 1007–1026 (2010)
Hunter, A., Parsons, S., Wooldridge, M.: Measuring inconsistency in multi-agent systems. Künstliche Intelligenz 28(3), 169–178 (2014)
Jabbour, S., Ma, Y., Raddaoui, B., Sais, L., Salhi, Y.: A MIS partition based framework for measuring inconsistency. In: Proceedings of the 15th Conference on Principles of Knowledge Representation and Reasoning (KR 2016), pp. 84–93. AAAI Press (2016)
Jabbour, S., Raddaoui, B., Sais, L.: Inconsistency-based ranking of knowledge bases. In: Proceedings of the 7th International Conference on Agents and Artificial Intelligence (ICAART 2015), vol. 2, pp. 414–419. SciTePress (2015)
Lang, J., Marquis, P.: Resolving inconsistencies by variable forgetting. In: Proceedings of the 8th Conference on Principles of Knowledge Representation and Reasoning (KR 2002), pp. 239–250. Morgan Kaufmann (2002)
Lang, J., Liberatore, P., Marquis, P.: Propositional independence: formula-variable independence and forgetting. J. Artif. Intell. Res. 18, 391–443 (2003)
Lin, F.: On strongest necessary and weakest sufficient conditions. Artif. Intell. 128(1–2), 143–159 (2001)
Lin, F., Reiter, R.: Forget it! In: Proceedings of the AAAI Fall Symposium on Relevance, pp. 154–159 (1994)
Liu, W., Mu, K.: Editors of special issue on theories of inconsistency measures and their applications. Approx. Reason. (2016, to appear)
McAreavey, K., Liu, W., Miller, P.: Computational approaches to finding and measuring inconsistency in arbitrary knowledge bases. Approx. Reason. 55, 1659–1693 (2014)
Kedian, M.: Responsability for inconsistency. Approx. Reason. 61, 43–60 (2015)
Kedian, M., Liu, W., Jin, Z.: Measuring the blame of a formula for inconsistent prioritized knowledge bases. Logic Comput. 22(3), 481–516 (2012)
Su, K., Lv, G., Zhang, Y.: Reasoning about knowledge by variable forgetting. In: Proceedings of the 9th Conference on Principles of Knowledge Representation and Reasoning (KR 2004), pp. 576–586. Morgan Kaufmann (2004)
Thimm, M.: Inconsistency measures for probabilistic logics. Artif. Intell. 197, 1–24 (2013)
Thimm, M.: On the expressivity of inconsistency measures. Artif. Intell. 234, 120–151 (2016)
Thimm, M.: Stream-based inconsistency measurement. Approx. Reason. 68, 68–87 (2016)
Thimm, M., Wallner, J.P.: Some complexity results on inconsistency measurement. In Proceedings of the 15th Conference on Principles of Knowledge Representation and Reasoning (KR 2016), pp. 114–124. AAAI Press (2016)
Xiao, G., Ma, Y.: Inconsistency measurement based on variables in minimal unsatisfiable subsets. In: Proceedings of the 20th European Conference on Artificial Intelligence (ECAI 2012), pp. 864–869. IOS Press (2012)
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The author is grateful to the reviewers for both useful comments on this paper and insightful suggestions about this topic.
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Besnard, P. (2016). Forgetting-Based Inconsistency Measure. In: Schockaert, S., Senellart, P. (eds) Scalable Uncertainty Management. SUM 2016. Lecture Notes in Computer Science(), vol 9858. Springer, Cham. https://doi.org/10.1007/978-3-319-45856-4_23
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