Metric Propositional Neighborhood Logics

Transcription

1 Metric Propositional Neighborhood Logics D. Bresolin, D. Della Monica, V. Goranko, A. Montanari, and G. Sciavicco University of Murcia Please notice: these slides have been mostly produced by Dario Della Monica (University of Udine), who, in turn, borrowed many ideas from Davide Bresolin s slides (University of Verona). ECAI Lisbon Guido Sciavicco (Univ. of Murcia) Metric Propositional Neighborhood Logics ECAI Lisbon 1 / 40

2 Outline 1 Interval Temporal Logics 2 Extending PNL with Metric Features 3 Decidability of MPNL l 4 Expressive Completeness Results 5 Classification w.r.t. Expressive Power 6 Conclusions and Future Research Directions Guido Sciavicco (Univ. of Murcia) Metric Propositional Neighborhood Logics ECAI Lisbon 2 / 40

3 Outline 1 Interval Temporal Logics 2 Extending PNL with Metric Features 3 Decidability of MPNL l 4 Expressive Completeness Results 5 Classification w.r.t. Expressive Power 6 Conclusions and Future Research Directions Guido Sciavicco (Univ. of Murcia) Metric Propositional Neighborhood Logics ECAI Lisbon 3 / 40

4 Time and logics Studying time and its structure is of great importance in computer science: Artificial Intelligence. Planning, Natural Language Recognition,... Databases. Temporal Databases. Formal methods. Specification and Verification of Systems and Protocols, Model Checking,... Guido Sciavicco (Univ. of Murcia) Metric Propositional Neighborhood Logics ECAI Lisbon 4 / 40

5 Points vs. intervals Usually, time is formalized as a (usually linearly ordered) set of points. In point-based temporal logics, formulas are interpreted directly over points. In interval-based ones, they are interpreted over intervals. In this case, intervals can also be given of a duration. It is well-known that interval-based logics are much more difficult to deal with. Guido Sciavicco (Univ. of Murcia) Metric Propositional Neighborhood Logics ECAI Lisbon 5 / 40

6 What is an interval? Definition Given a linear order D = D,< : an interval in D is a pair [d 0, d 1 ] such that d 0 < d 1 (or d 0 d 1 ); I(D) is the set of all intervals on D; D, I(D) is an interval structure. We consider intervals as pairs of time points. A point d D belongs to [d 0, d 1 ] if d 0 d d 1. Guido Sciavicco (Univ. of Murcia) Metric Propositional Neighborhood Logics ECAI Lisbon 6 / 40

7 Allen s binary relations There are 13 different binary relations between intervals: together with their inverses. Between points we have only three binary relations! Guido Sciavicco (Univ. of Murcia) Metric Propositional Neighborhood Logics ECAI Lisbon 7 / 40

8 Allen s binary relations There are 13 different binary relations between intervals: later together with their inverses. Between points we have only three binary relations! Guido Sciavicco (Univ. of Murcia) Metric Propositional Neighborhood Logics ECAI Lisbon 7 / 40

9 Allen s binary relations There are 13 different binary relations between intervals: later after/meets together with their inverses. Between points we have only three binary relations! Guido Sciavicco (Univ. of Murcia) Metric Propositional Neighborhood Logics ECAI Lisbon 7 / 40

10 Allen s binary relations There are 13 different binary relations between intervals: later after/meets overlaps together with their inverses. Between points we have only three binary relations! Guido Sciavicco (Univ. of Murcia) Metric Propositional Neighborhood Logics ECAI Lisbon 7 / 40

11 Allen s binary relations There are 13 different binary relations between intervals: later after/meets overlaps ends/finishes together with their inverses. Between points we have only three binary relations! Guido Sciavicco (Univ. of Murcia) Metric Propositional Neighborhood Logics ECAI Lisbon 7 / 40

12 Allen s binary relations There are 13 different binary relations between intervals: later after/meets overlaps ends/finishes during together with their inverses. Between points we have only three binary relations! Guido Sciavicco (Univ. of Murcia) Metric Propositional Neighborhood Logics ECAI Lisbon 7 / 40

13 Allen s binary relations There are 13 different binary relations between intervals: later after/meets overlaps ends/finishes during begins/starts together with their inverses. Between points we have only three binary relations! Guido Sciavicco (Univ. of Murcia) Metric Propositional Neighborhood Logics ECAI Lisbon 7 / 40

14 Allen s binary relations There are 13 different binary relations between intervals: later after/meets overlaps ends/finishes during begins/starts L A O E D B together with their inverses. Between points we have only three binary relations! Guido Sciavicco (Univ. of Murcia) Metric Propositional Neighborhood Logics ECAI Lisbon 7 / 40

15 Allen s binary relations There are 13 different binary relations between intervals: later after/meets overlaps ends/finishes during begins/starts L L A A O O E E D D B B together with their inverses. Between points we have only three binary relations! Guido Sciavicco (Univ. of Murcia) Metric Propositional Neighborhood Logics ECAI Lisbon 7 / 40

16 Allen s binary relations There are 13 different binary relations between intervals: later after/meets overlaps ends/finishes during begins/starts L L A A O O E E D D B B together with their inverses. Between points we have only three binary relations! Guido Sciavicco (Univ. of Murcia) Metric Propositional Neighborhood Logics ECAI Lisbon 7 / 40

17 Interval temporal logics Interval temporal logics, such as HS [Halpern and Shoham, 1991] and CDT [Venema, 1991], are very expressive (compared to point-based temporal logics) Most interval temporal logics are (highly) undecidable Problem Find expressive, yet decidable, interval temporal logics. Guido Sciavicco (Univ. of Murcia) Metric Propositional Neighborhood Logics ECAI Lisbon 8 / 40

18 A simple path to decidability Interval logics make it possible to express properties of pairs of time points rather than of single time points. How has decidability been achieved? By imposing suitable syntactic and/or semantic restrictions that allow one to reduce interval logics to point-based ones: Constraining interval modalities B B and E E fragments of HS. Constraining temporal structures Split Logics: any interval can be chopped in at most one way (Split Structures). Constraining semantic interpretations Local QPITL: a propositional variable is true over an interval if and only if it is true over its starting point (Locality Principle). Guido Sciavicco (Univ. of Murcia) Metric Propositional Neighborhood Logics ECAI Lisbon 9 / 40

19 An alternative path to decidability A major challenge Identify expressive enough, yet decidable, logics which are genuinely interval-based. What is a genuinely interval-based logic? A logic is genuinely interval-based if it cannot be directly translated into a point-based logic and does not invoke locality, or any other semantic restriction reducing the interval-based semantics to the point-based one. Guido Sciavicco (Univ. of Murcia) Metric Propositional Neighborhood Logics ECAI Lisbon 10 / 40

20 Known decidability results The picture of decidable/undecidable non-metric interval logics is almost complete Propositional Neighborhood Logic (AA) is the first discovered decidable genuine interval logic (and maximal in most cases, including N) the logic ABBA is maximal decidable over finite DDBBLL is maximal decidable over dense the vast majority of all other fragments is undecidable no previous known results for metric extension of any interval logic We will present a family of metric extensions of PNL over natural numbers: Decidability proof of the most expressive fragment (MPNL l ) Expressive completeness and undecidable extension ( FO 2 [N,=,<,s]) Classification of all metric fragments w.r.t. expressive power Guido Sciavicco (Univ. of Murcia) Metric Propositional Neighborhood Logics ECAI Lisbon 11 / 40

21 Outline 1 Interval Temporal Logics 2 Extending PNL with Metric Features 3 Decidability of MPNL l 4 Expressive Completeness Results 5 Classification w.r.t. Expressive Power 6 Conclusions and Future Research Directions Guido Sciavicco (Univ. of Murcia) Metric Propositional Neighborhood Logics ECAI Lisbon 12 / 40

22 The logic PNL Semantics PNL is based on the neighborhood operators meets and met-by: A ϕ ϕ { }} {{ }} { meets : met by : d 0 d 1 d 2 ϕ A ϕ {}}{{}}{ Metric formulas can constrain the length of the current interval or the length of reachable intervals d 2 d 0 d 1 Guido Sciavicco (Univ. of Murcia) Metric Propositional Neighborhood Logics ECAI Lisbon 13 / 40

23 The addition of a metric aspect Metric extensions of PNL over the integers 1 Extensions of the modal operators A ( r ) and A ( l ): =k r, >k r, [k,k ] l, (k,k ) l,... S: set of all possible metric extensions of PNL modalities 2 Introduction of atomic length constraints: len >k, len k, len =k,... L: set of all atomic length constraints Guido Sciavicco (Univ. of Murcia) Metric Propositional Neighborhood Logics ECAI Lisbon 14 / 40

24 The addition of a metric aspect Metric extensions of PNL over the integers 1 Extensions of the modal operators A ( r ) and A ( l ): =k r, >k r, [k,k ] l, (k,k ) l,... S: set of all possible metric extensions of PNL modalities 2 Introduction of atomic length constraints: len >k, len k, len =k,... L: set of all atomic length constraints Guido Sciavicco (Univ. of Murcia) Metric Propositional Neighborhood Logics ECAI Lisbon 14 / 40

25 The addition of a metric aspect Metric extensions of PNL over the integers 1 Extensions of the modal operators A ( r ) and A ( l ): =k r, >k r, [k,k ] l, (k,k ) l,... S: set of all possible metric extensions of PNL modalities 2 Introduction of atomic length constraints: len >k, len k, len =k,... L: set of all atomic length constraints Guido Sciavicco (Univ. of Murcia) Metric Propositional Neighborhood Logics ECAI Lisbon 14 / 40

26 The addition of a metric aspect Metric extensions of PNL over the integers 1 Extensions of the modal operators A ( r ) and A ( l ): =k r, >k r, [k,k ] l, (k,k ) l,... S: set of all possible metric extensions of PNL modalities 2 Introduction of atomic length constraints: len >k, len k, len =k,... L: set of all atomic length constraints Guido Sciavicco (Univ. of Murcia) Metric Propositional Neighborhood Logics ECAI Lisbon 14 / 40

27 The addition of a metric aspect Metric extensions of PNL over the integers 1 Extensions of the modal operators A ( r ) and A ( l ): =k r, >k r, [k,k ] l, (k,k ) l,... S: set of all possible metric extensions of PNL modalities 2 Introduction of atomic length constraints: len >k, len k, len =k,... L: set of all atomic length constraints MPNL = {MPNL S L S, S S, L L} set of all metric extenstions of PNL Guido Sciavicco (Univ. of Murcia) Metric Propositional Neighborhood Logics ECAI Lisbon 14 / 40

28 MPNL l : a simple metric interval logic Propositional Neighborhood Logic with atomic length constraints Syntax of MPNL l ϕ ::= p ϕ ϕ ϕ A ϕ A ϕ len =k Proposition MPNL l is the most powerful logic in MPNL Guido Sciavicco (Univ. of Murcia) Metric Propositional Neighborhood Logics ECAI Lisbon 15 / 40

29 The leaking gas burner Every time the flame is ignited, a small amount of gas can leak from the burner. The propositional letter Gas is used to indicate the gas is flowing. The propositional letter Flame is true when the gas is burning. Safety of the gas burner: 1 It is never the case that the gas is leaking for more than 2 seconds. 2 The gas burner will not leak for 30 seconds after the last leakage. Guido Sciavicco (Univ. of Murcia) Metric Propositional Neighborhood Logics ECAI Lisbon 16 / 40

30 Safety of the gas burner in MPNL l Universal modality: ϕ holds everywhere in the future [G]ϕ ::= ϕ [A]ϕ [A][A]ϕ Leaking = gas flowing but not burning [G](Leak Gas Flame) Safety properties: 1 [G](Leak len 2 ) 2 [G](Leak A (len <30 A Leak)) Guido Sciavicco (Univ. of Murcia) Metric Propositional Neighborhood Logics ECAI Lisbon 17 / 40

31 MPNL l is simple but powerful Metric Until MPNL l is expressive enough to encode a metric form of Until: p is true at a point in the future at distance k from the current interval and, until that point, q is true (pointwise) A (len =k A (len =0 p)) [A](len <k A (len =0 q)) Unbounded until is not expressible in MPNL l. Guido Sciavicco (Univ. of Murcia) Metric Propositional Neighborhood Logics ECAI Lisbon 18 / 40

32 MPNL l is simple but powerful Metric version of Allen s relations MPNL l is expressive enough to encode some metric form of all (but one) Allen s relation: p holds over intervals of length l, with k l k [G](p len k len k ) Any p-interval begins a q-interval [G] k i=k (p len =i l r (len >i q)) Any p-interval contains a q-interval [G] k i=k (p len =i j 0,j+j <i ( l r (len =j r (len =j q)))) Guido Sciavicco (Univ. of Murcia) Metric Propositional Neighborhood Logics ECAI Lisbon 19 / 40

33 Outline 1 Interval Temporal Logics 2 Extending PNL with Metric Features 3 Decidability of MPNL l 4 Expressive Completeness Results 5 Classification w.r.t. Expressive Power 6 Conclusions and Future Research Directions Guido Sciavicco (Univ. of Murcia) Metric Propositional Neighborhood Logics ECAI Lisbon 20 / 40

34 Basic ingredients Definition An atom is a maximal, locally consistent set of subformulae of ϕ. A relation connecting atoms Connect every pair of atoms that can be associated with neighbor intervals preserving the universal quantifiers: A R ϕ B iff { 1 [A]ψ A ψ B 2 [A]ψ B ψ A Guido Sciavicco (Univ. of Murcia) Metric Propositional Neighborhood Logics ECAI Lisbon 21 / 40

35 Labelled Interval Structures Definition A Labelled Interval Structure (LIS) is a pair I(D), L where: I(D) is the set of intervals over D; the labelling function L assigns an atom to every interval [d i, d j ]; atoms assigned to neighbor intervals are related by R ϕ. A LIS is fulfilling if: metric formulae in L([d i, d j ]) are consistent with respect to the interval length; for every [d i, d j ] and A ψ (resp., A ψ) L([d i, d j ]) there exists d k > d j (resp., d k < d i ) such that ψ L([d j, d k ]) (resp., L([d k, d i ])). Theorem A formula ϕ is satisfiable if and only if there exists a fulfilling LIS I(D), L and an interval [d i, d j ] such that ϕ L([d i, d j ]). Guido Sciavicco (Univ. of Murcia) Metric Propositional Neighborhood Logics ECAI Lisbon 22 / 40

36 A small-model theorem for LIS We have reduced the satisfiability problem for MPNL l to the problem of finding a (fulfilling) LIS for ϕ. LIS can be of arbitrary size and even infinite! Problems How to bound the size of finite LIS? How to finitely represent infinite LIS? Solution Any large (resp., infinite) model can be turned into a bounded (resp., bounded periodic) one by progressively removing exceeding points Guido Sciavicco (Univ. of Murcia) Metric Propositional Neighborhood Logics ECAI Lisbon 23 / 40

37 The set of requests of a point d j d i 2 d i 1 d i Guido Sciavicco (Univ. of Murcia) Metric Propositional Neighborhood Logics ECAI Lisbon 24 / 40

38 The set of requests of a point d j d i 2 d i 1 d i REQ R (d i ) Guido Sciavicco (Univ. of Murcia) Metric Propositional Neighborhood Logics ECAI Lisbon 24 / 40

39 The set of requests of a point d j d i 2 d i 1 d i REQ R (d i ) Guido Sciavicco (Univ. of Murcia) Metric Propositional Neighborhood Logics ECAI Lisbon 24 / 40

40 The set of requests of a point d j d i 2 d i 1 d i REQ L (d j ) REQ R (d i ) Guido Sciavicco (Univ. of Murcia) Metric Propositional Neighborhood Logics ECAI Lisbon 24 / 40

41 The set of requests of a point d j d i 2 d i 1 d i REQ L (d j ) REQ R (d i ) REQ(d i ) = REQ R (d i ) REQ L (d i ) Guido Sciavicco (Univ. of Murcia) Metric Propositional Neighborhood Logics ECAI Lisbon 24 / 40

42 k-sequences of requests Given a formula ϕ, let k be the greatest constant that appears in ϕ. Definition Given a LIS, a k-sequence is a sequence of k consecutive points. Given a sequence σ, its sequence of requests REQ(σ) is defined as the sequence of temporal requests at the points in σ. σ { }} { d 1 d 2 d 3 d k REQ(d 1 ) } REQ(d 2 ) REQ(d 3 ) {{ REQ(d k ) } REQ(σ) Guido Sciavicco (Univ. of Murcia) Metric Propositional Neighborhood Logics ECAI Lisbon 25 / 40

43 Removing k-sequences from a LIS Lemma Let m be the number of A -subformulae of ϕ and r the number of possible sets of requests REQ. Let I(D), L be a fulfilling LIS for ϕ and REQ(σ) be a k-sequence of request that occurs more than 2(m 2 + m)r + 1 times. We can remove one occurrence of REQ(σ) from the LIS in such a way that the resulting LIS is still fulfilling. Guido Sciavicco (Univ. of Murcia) Metric Propositional Neighborhood Logics ECAI Lisbon 26 / 40

44 The removal process: fixing the length of intervals REQ(σ) {}}{ d d 1 d e d k { REQ(σ) }} { d 1 d e d k l > k l k Remove all points up to the next occurrence of REQ(σ) Some intervals became shorter, and do not respect metric formulas anymore Since REQ(d e ) = REQ(d e ), we can relabel problematic intervals Guido Sciavicco (Univ. of Murcia) Metric Propositional Neighborhood Logics ECAI Lisbon 27 / 40

45 The removal process: fixing the length of intervals REQ(σ) {}}{ d d 1 d e d k { REQ(σ) }} { d 1 d e d k l > k l k Remove all points up to the next occurrence of REQ(σ) Some intervals became shorter, and do not respect metric formulas anymore Since REQ(d e ) = REQ(d e ), we can relabel problematic intervals Guido Sciavicco (Univ. of Murcia) Metric Propositional Neighborhood Logics ECAI Lisbon 27 / 40

46 The removal process: fixing the length of intervals REQ(σ) {}}{ d d 1 d e d k { REQ(σ) }} { d 1 d e d k l > k l k Remove all points up to the next occurrence of REQ(σ) Some intervals became shorter, and do not respect metric formulas anymore Since REQ(d e ) = REQ(d e ), we can relabel problematic intervals Guido Sciavicco (Univ. of Murcia) Metric Propositional Neighborhood Logics ECAI Lisbon 27 / 40

47 The removal process: fixing the length of intervals REQ(σ) {}}{ d d 1 d e d k { REQ(σ) }} { d 1 d e d k l > k l k l k Remove all points up to the next occurrence of REQ(σ) Some intervals became shorter, and do not respect metric formulas anymore Since REQ(d e ) = REQ(d e ), we can relabel problematic intervals Guido Sciavicco (Univ. of Murcia) Metric Propositional Neighborhood Logics ECAI Lisbon 27 / 40

48 The removal process: fixing defects on A -formulae d d e A ψ 1 ψ 1,l > k m points on the right of d e with the same set of requests of d e Guido Sciavicco (Univ. of Murcia) Metric Propositional Neighborhood Logics ECAI Lisbon 28 / 40

49 The removal process: fixing defects on A -formulae d d e A ψ 1 ψ 1,l > k m points on the right of d e with the same set of requests of d e Guido Sciavicco (Univ. of Murcia) Metric Propositional Neighborhood Logics ECAI Lisbon 28 / 40

50 The removal process: fixing defects on A -formulae d d e A ψ 1 ψ 1,l > k A ψ 2 A ψ 3. A ψ m m points on the right of d e with the same set of requests of d e Guido Sciavicco (Univ. of Murcia) Metric Propositional Neighborhood Logics ECAI Lisbon 28 / 40

51 The removal process: fixing defects on A -formulae d d e d 1 d 2 d m 1 d m A ψ 1 ψ 1,l > k A ψ 2 A ψ 3. A ψ m m points on the right of d e with the same set of requests of d e Guido Sciavicco (Univ. of Murcia) Metric Propositional Neighborhood Logics ECAI Lisbon 28 / 40

52 The removal process: fixing defects on A -formulae d d e d 1 d 2 d m 1 d m A ψ 1 ψ 1,l > k A ψ 2 A ψ 3. A ψ m ψ 1,l > k m points on the right of d e with the same set of requests of d e Guido Sciavicco (Univ. of Murcia) Metric Propositional Neighborhood Logics ECAI Lisbon 28 / 40

53 The small model theorem for MPNL l By taking advantage of such a removal process, we can prove the following theorem: Theorem (Small model theorem) A formula ϕ is satisfiable if and only if there exists a LIS I(D), L such that: if D is finite, then every k-sequence of requests occurs at most 2(m 2 + m)r + 1 times in D if D is infinite, then the LIS is ultimately periodic with prefix and period bounded by r k (2(m 2 + m)r + 1)k + k 1 Guido Sciavicco (Univ. of Murcia) Metric Propositional Neighborhood Logics ECAI Lisbon 29 / 40

54 Decidability and complexity Plain RPNL is known to be NEXPTIME-complete NEXPTIME-hardness A model for an MPNL l formula ϕ can be obtained by a non-deterministic decision procedure that runs in time O(2 k n ). Guido Sciavicco (Univ. of Murcia) Metric Propositional Neighborhood Logics ECAI Lisbon 30 / 40

55 Decidability and complexity Plain RPNL is known to be NEXPTIME-complete NEXPTIME-hardness A model for an MPNL l formula ϕ can be obtained by a non-deterministic decision procedure that runs in time O(2 k n ). The exact complexity class depends on how k is encoded: k is a constant: k = O(1) MPNL l is NEXPTIME-complete k is encoded in unary: k = O(n) MPNL l is NEXPTIME-complete k is encoded in binary: k = O(2 n ) MPNL l is in 2NEXPTIME but is EXPSPACE-hard (since RPNL+INT is EXPSPACE-complete) The exact complexity class is an open problem!!! Guido Sciavicco (Univ. of Murcia) Metric Propositional Neighborhood Logics ECAI Lisbon 30 / 40

56 Outline 1 Interval Temporal Logics 2 Extending PNL with Metric Features 3 Decidability of MPNL l 4 Expressive Completeness Results 5 Classification w.r.t. Expressive Power 6 Conclusions and Future Research Directions Guido Sciavicco (Univ. of Murcia) Metric Propositional Neighborhood Logics ECAI Lisbon 31 / 40

57 PNL and Two-Variable Fragment of First Order Logic Syntax of FO 2 [<,=]: α ::= A 0 A 1 α α α xα yα A 0 ::= x = x x = y y = x y = y x < y y < x A 1 ::= P(x, x) P(x, y) P(y, x) P(y, y) Guido Sciavicco (Univ. of Murcia) Metric Propositional Neighborhood Logics ECAI Lisbon 32 / 40

58 PNL and Two-Variable Fragment of First Order Logic Syntax of FO 2 [<,=]: α ::= A 0 A 1 α α α xα yα A 0 ::= x = x x = y y = x y = y x < y y < x A 1 ::= P(x, x) P(x, y) P(y, x) P(y, y) Theorem (Bresolin et al., On Decidability and Expressiveness of PNL, LFCS 2007) PNL π+ FO 2 [<,=] PNL π+ FO 2 [N, =, <] Guido Sciavicco (Univ. of Murcia) Metric Propositional Neighborhood Logics ECAI Lisbon 32 / 40

59 PNL and Two-Variable Fragment of First Order Logic Syntax of FO 2 [<,=]: α ::= A 0 A 1 α α α xα yα A 0 ::= x = x x = y y = x y = y x < y y < x A 1 ::= P(x, x) P(x, y) P(y, x) P(y, y) Theorem (Bresolin et al., On Decidability and Expressiveness of PNL, LFCS 2007) PNL π+ FO 2 [<,=] Theorem (Y. Venema, A Modal Logic for Chopping intervals, JLC, 1991) CDT FO 3 2 [=,<] CDT FO 3 2[=, <] PNL π+ FO 2 [N, =, <] Guido Sciavicco (Univ. of Murcia) Metric Propositional Neighborhood Logics ECAI Lisbon 32 / 40

60 The logic FO 2 [N, <, =, s] Syntax of FO 2 [N,<,=, s]: t 1, t 2 = s k (z), z {x, y} α ::= A 0 α α α xα yα A 0 ::= t 1 = t 2 t 1 < t 2 P(t 1, t 2 ) Guido Sciavicco (Univ. of Murcia) Metric Propositional Neighborhood Logics ECAI Lisbon 33 / 40

61 The logic FO 2 [N, <, =, s] Syntax of FO 2 [N,<,=, s]: t 1, t 2 = s k (z), z {x, y} α ::= A 0 α α α xα yα A 0 ::= t 1 = t 2 t 1 < t 2 P(t 1, t 2 ) Theorem The satisfiability problem for FO 2 [N,<,=, s] is undecidable Guido Sciavicco (Univ. of Murcia) Metric Propositional Neighborhood Logics ECAI Lisbon 33 / 40

62 The logic MPNL + l : an extension of MPNL l Additional modalities +k e, +k b, +k be Guido Sciavicco (Univ. of Murcia) Metric Propositional Neighborhood Logics ECAI Lisbon 34 / 40

63 The logic MPNL + l : an extension of MPNL l Additional modalities +k +k e ψ e, +k b, +k be Guido Sciavicco (Univ. of Murcia) Metric Propositional Neighborhood Logics ECAI Lisbon 34 / 40

64 The logic MPNL + l : an extension of MPNL l Additional modalities +k +k e ψ ψ } {{ } k e, +k b, +k be Guido Sciavicco (Univ. of Murcia) Metric Propositional Neighborhood Logics ECAI Lisbon 34 / 40

65 The logic MPNL + l : an extension of MPNL l Additional modalities +k +k e ψ e, +k b, +k be +k b ψ ψ } {{ } k Guido Sciavicco (Univ. of Murcia) Metric Propositional Neighborhood Logics ECAI Lisbon 34 / 40

66 The logic MPNL + l : an extension of MPNL l Additional modalities +k +k e ψ e, +k b, +k be +k b ψ ψ ψ } {{ } k }{{} k Guido Sciavicco (Univ. of Murcia) Metric Propositional Neighborhood Logics ECAI Lisbon 34 / 40

67 The logic MPNL + l : an extension of MPNL l Additional modalities +k +k e ψ ψ } {{ } k e, +k b, +k be +k b ψ } {{ ψ } k Guido Sciavicco (Univ. of Murcia) Metric Propositional Neighborhood Logics ECAI Lisbon 34 / 40

68 The logic MPNL + l : an extension of MPNL l Additional modalities +k +k e ψ ψ } {{ } k e, +k b, +k be +k b ψ } {{ ψ } k +k be ψ Guido Sciavicco (Univ. of Murcia) Metric Propositional Neighborhood Logics ECAI Lisbon 34 / 40

69 The logic MPNL + l : an extension of MPNL l Additional modalities +k +k e ψ ψ } {{ } k e, +k b, +k be +k b ψ } {{ ψ } k +k be ψ } ψ {{} k }{{ k } Guido Sciavicco (Univ. of Murcia) Metric Propositional Neighborhood Logics ECAI Lisbon 34 / 40

70 The logic MPNL + l : an extension of MPNL l Additional modalities +k +k e ψ e, +k b, +k be ψ } {{ } k +k b ψ } {{ ψ } k +k be ψ } ψ {{} k }{{ k } Theorem MPNL + l FO 2 [N,<,=, s] Guido Sciavicco (Univ. of Murcia) Metric Propositional Neighborhood Logics ECAI Lisbon 34 / 40

71 The logic MPNL + l : an extension of MPNL l Additional modalities +k +k e ψ e, +k b, +k be ψ } {{ } k +k b ψ } {{ ψ } k +k be ψ } ψ {{} k }{{ k } Theorem MPNL + l FO 2 [N,<,=, s] CDT FO 3 2[=, <] PNL π+ FO 2 [N, =, <] Guido Sciavicco (Univ. of Murcia) Metric Propositional Neighborhood Logics ECAI Lisbon 34 / 40

72 The logic MPNL + l : an extension of MPNL l Additional modalities +k +k e ψ e, +k b, +k be ψ } {{ } k +k b ψ } {{ ψ } k +k be ψ } ψ {{} k }{{ k } Theorem MPNL + l FO 2 [N,<,=, s] CDT FO 3 2[=, <] + MPNL l FO 2 [N, =, <, s] PNL π+ FO 2 [N, =, <] Guido Sciavicco (Univ. of Murcia) Metric Propositional Neighborhood Logics ECAI Lisbon 34 / 40

73 Expressive completeness of MPNL l The fragment FO 2 r [N, <,=, s] of FO 2 [N, <,=, s] If both variables x and y occur in the scope of a relation, then the successor function cannot appear in that scope. Guido Sciavicco (Univ. of Murcia) Metric Propositional Neighborhood Logics ECAI Lisbon 35 / 40

74 Expressive completeness of MPNL l The fragment FO 2 r [N, <,=, s] of FO 2 [N, <,=, s] If both variables x and y occur in the scope of a relation, then the successor function cannot appear in that scope. Example R(x, y) and R(s(x), s(s(x))) belong to the logic R(s(x), y) does not Guido Sciavicco (Univ. of Murcia) Metric Propositional Neighborhood Logics ECAI Lisbon 35 / 40

75 Expressive completeness of MPNL l The fragment FO 2 r [N, <,=, s] of FO 2 [N, <,=, s] If both variables x and y occur in the scope of a relation, then the successor function cannot appear in that scope. Example R(x, y) and R(s(x), s(s(x))) belong to the logic R(s(x), y) does not Theorem MPNL l FO 2 r [N,<,=, s] Guido Sciavicco (Univ. of Murcia) Metric Propositional Neighborhood Logics ECAI Lisbon 35 / 40

76 Expressive completeness of MPNL l The fragment FO 2 r [N, <,=, s] of FO 2 [N, <,=, s] If both variables x and y occur in the scope of a relation, then the successor function cannot appear in that scope. Example R(x, y) and R(s(x), s(s(x))) belong to the logic R(s(x), y) does not Theorem MPNL l FO 2 r [N,<,=, s] CDT FO 3 2[=, <] + MPNL l FO 2 [N, =, <, s] PNL π+ FO 2 [N, =, <] Guido Sciavicco (Univ. of Murcia) Metric Propositional Neighborhood Logics ECAI Lisbon 35 / 40

77 Expressive completeness of MPNL l The fragment FO 2 r [N, <,=, s] of FO 2 [N, <,=, s] If both variables x and y occur in the scope of a relation, then the successor function cannot appear in that scope. Example R(x, y) and R(s(x), s(s(x))) belong to the logic R(s(x), y) does not Theorem MPNL l FO 2 r [N,<,=, s] CDT FO 3 2[=, <] + MPNL l FO 2 [N, =, <, s] MPNL l FO 2 r[n, =, <, s] PNL π+ FO 2 [N, =, <] Guido Sciavicco (Univ. of Murcia) Metric Propositional Neighborhood Logics ECAI Lisbon 35 / 40

78 Outline 1 Interval Temporal Logics 2 Extending PNL with Metric Features 3 Decidability of MPNL l 4 Expressive Completeness Results 5 Classification w.r.t. Expressive Power 6 Conclusions and Future Research Directions Guido Sciavicco (Univ. of Murcia) Metric Propositional Neighborhood Logics ECAI Lisbon 36 / 40

79 Relative expressive power of logics in MPNL MPNL = l MPNL () l MPNL = MPNL () MPNL < MPNL > Guido Sciavicco (Univ. of Murcia) Metric Propositional Neighborhood Logics ECAI Lisbon 37 / 40

80 Relative expressive power of logics in MPNL MPNL l MPNL = l MPNL [] MPNL () l MPNL =,ɛ MPNL <,> MPNL (),ɛ MPNL = MPNL <,ɛ MPNL >,ɛ MPNL () MPNL < MPNL ɛ PNL MPNL > Guido Sciavicco (Univ. of Murcia) Metric Propositional Neighborhood Logics ECAI Lisbon 37 / 40

81 Relative expressive power of logics in MPNL MPNL l in 2NEXPTIME EXPSPACE-hard MPNL = l MPNL [] MPNL () l MPNL =,ɛ MPNL <,> MPNL (),ɛ MPNL = MPNL <,ɛ MPNL >,ɛ MPNL () MPNL < MPNL ɛ PNL MPNL > NEXPTIME-complete Guido Sciavicco (Univ. of Murcia) Metric Propositional Neighborhood Logics ECAI Lisbon 37 / 40

82 Outline 1 Interval Temporal Logics 2 Extending PNL with Metric Features 3 Decidability of MPNL l 4 Expressive Completeness Results 5 Classification w.r.t. Expressive Power 6 Conclusions and Future Research Directions Guido Sciavicco (Univ. of Murcia) Metric Propositional Neighborhood Logics ECAI Lisbon 38 / 40

83 Conclusions To do The class MPNL of metric logics based on PNL Decidability of the most expressive logic (MPNL l ) Undecidability of FO 2 [N,<,=, s] Expressive completeness results: MPNL + l FO 2 [N, <, =, s] undecidability of MPNL + l MPNLl FO 2 r [N, <, =, s] decidability of FO 2 r [N, <, =, s] Relative expressive power of logics in MPNL From N to Z and all linear orderings From standard distance functions to other distance functions From constant constraint to arithmetic constraints Where is the complexity jump? To identify the precise complexity class of MPNL l (2NEXPTIME or EXPSPACE?) Guido Sciavicco (Univ. of Murcia) Metric Propositional Neighborhood Logics ECAI Lisbon 39 / 40

84 More research directions Decidability/undecidability of other (Metric) Interval Logics: the sub-interval logic D other combinations of Allen s relations Model Checking of (Metric) Interval logics: no known results; Tableau method for Metric Interval Logics in particular, the extension of the tableau method for PNL to the metric case; Metric PNL over dense orderings PNL is decidable even in the dense case (Q); can we extend the language with metric features in this case too? Guido Sciavicco (Univ. of Murcia) Metric Propositional Neighborhood Logics ECAI Lisbon 40 / 40