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Logic, Infinite Computation, and Coinduction

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### Logic, Infinite Computation, and Coinduction

Gopal Gupta

Neda Saeedloei, Brian DeVries, Kyle Marple Feliks Kluzniak,

Luke Simon, Ajay Bansal, Ajay Mallya, Richard Min

Applied Logic, Programming-Languages

and Systems (ALPS) Lab

The University of Texas at Dallas

UT Dallas Computer Science

- Located in North Dallas in the middle of Telecom Corridor, home of more than 600 high tech companies (TI, Alcatel, EDS/HP, ….)
- 45 T/T faculty members; 13 full-time instructors
- 850 UGs, 700 M.S. students, 130 Ph.D. students
- BS/MS/Ph.D. in CS, SE, CE and TE
- 6 Major research areas: AI, Systems, Theory, SE, N/W, Security
- 10 NSF CAREER award winners; 2 Airforce Young Investigators
- $9M research expenditure in 2009-10
- $12 Million new funding in 2010-11
- 40+ grants in 2011-12 totaling more than $12 Million
- UTD ranked 167 now in London Times world ranking
- Recent work on Frankenstein Virus got national/international coverage
- Please consider applying to our Ph.D. program & for faculty positions

Circular Phenomena in Comp. Sci.

- Circularity has dogged Mathematics and Computer Science ever since Set Theory was first developed:
- The well known Russell’s Paradox:
- R = { x | x is a set that does not contain itself}
Is R contained in R? Yes and No

- R = { x | x is a set that does not contain itself}
- Liar Paradox: I am a liar
- Hypergame paradox (Zwicker & Smullyan)

- The well known Russell’s Paradox:
- All these paradoxes involve self-reference through some type of negation
- Russell put the blame squarely on circularity and sought to ban it from scientific discourse:
``Whatever involves all of the collection must not be one of the collection” -- Russell 1908

Circularity in Computer Science

- Following Russell’s lead, Tarski proposed to ban self-referential sentences in a language
- Rather, have a hierarchy of languages
- Kripke’s paper challenged this in a1975 paper:
argued that circular phenomenon are far more common and circularity can’t simply be banned.

- Circularity has been banned from automated theorem proving and logic programming through the occurs check rule:
An unbound variable cannot be unified with a term containing that variable (i.e., X = f(X) not allowed)

- What if we allowed such unification to proceed (as LP systems always did for efficiency reasons)?

Circularity in Computer Science

- If occurs check is removed, we’ll generate circular (infinite) structures:
X = [1,2,3 | X] X = f(X)

- Such structures, of course, arise in computing (circular linked lists), but banned in logic/LP.
- Subsequent LP systems did allow for such circular structures (rational terms), but they only exist as data-structures, there is no proof theory to go along with it.
- One can hold the data-structure in memory within an LP execution, but one can’t reason about it.

Circularity in Everyday Life

- Circularity arises in every day life
- Most natural phenomenon are cyclical
- Cyclical movement of the earth, moon, etc.
- Our digestive system works in cycles

- Social interactions are cyclical:
- Conversation = (1st speaker, (2nd Speaker, Conversation)
- Shared conventions are cyclical concepts

- Most natural phenomenon are cyclical
- Numerous other examples can be found elsewhere (Barwise & Moss 1996)

Circularity in Computer Science

- Circular phenomenon are quite common in Computer Science:
- Circular linked lists
- Graphs (with cycles)
- Controllers (run forever)
- Bisimilarity
- Interactive systems
- Automata over infinite strings/Kripke structures
- Perpetual processes

- Logic/LP not equipped to model circularity directly

Coinduction

- Circular structures are infinite structures
X = [1, 2 | X] is logically speaking X = [1, 2, 1, 2, ….]

- Proofs about their properties are infinite-sized
- Coinduction is the technique for proving these properties
- first proposed by Peter Aczel in the 80s

- Systematic presentation of coinduction & its application to computing, math. and set theory: “Vicious Circles” by Moss and Barwise (1996)
- Our focus: inclusion of coinductive reasoning techniques in LP and theorem proving

Induction vs Coinduction

- Induction is a mathematical technique for finitely reasoning about an infinite (countable) no. of things.
- Examples of inductive structures:
- Naturals: 0, 1, 2, …
- Lists: [ ], [X], [X, X], [X, X, X], …

- 3 components of an inductive definition:
(1) Initiality, (2) iteration, (3) minimality

- for example, the set of lists is specified as follows:
[ ] – an empty list is a list (initiality) ……(i)

[H | T] is a list if T is a list and H is an element (iteration) ..(ii)

minimal set that satisfies (i) and (ii) (minimality)

- for example, the set of lists is specified as follows:

Induction vs Coinduction

- Coinduction is a mathematical technique for (finitely) reasoning about infinite things.
- Mathematical dual of induction
- If all things were finite, then coinduction would not be needed.
- Perpetual programs, automata over infinite strings

- 2 components of a coinductive definition:
(1) iteration, (2) maximality

- for example, for a list:
[ H | T ] is a list if T is a list and H is an element (iteration).

Maximal set that satisfies the specification of a list.

- This coinductive interpretation specifies all infinite sized lists

- for example, for a list:

Example: Natural Numbers

- (S) = { 0 } { succ(x) | x S }
- N =
- where is least fixed-point.

- aka “inductive definition”
- Let N be the smallest set such that
- 0 N
- x N implies x + 1 N

- Let N be the smallest set such that
- Induction corresponds to Least Fix Point (LFP) interpretation.

Example: Natural Numbers and Infinity

- (S) = { 0 } { succ(x) | x S }
- unambiguously defines another set
- N’ = = N { }
- = succ( succ( succ( ... ) ) ) = succ( ) = + 1
- where is a greatest fixed-point

- Coinduction corresponds to Greatest Fixed Point (GFP) interpretation.

Mathematical Foundations

- Duality provides a source of new mathematical tools that reflect the sophistication of tried and true techniques.

- Co-recursion: recursive def’n without a base case

Applications of Coinduction

- model checking
- bisimilarity proofs
- lazy evaluation in FP
- reasoning with infinite structures
- perpetual processes
- cyclic structures
- operational semantics of “coinductive logic programming”
- Type inference systems for lazy functional languages

Inductive Logic Programming

- Logic Programming
- is actually inductive logic programming.
- has inductive definition.
- useful for writing programs for reasoning about finite things:
- data structures

- properties

Infinite Objects and Properties

- Traditional logic programming is unable to reason about infinite objects and/or properties.
- (The glass is only half-full)
- Example: perpetual binary streams
- traditional logic programming cannot handle
bit(0).

bit(1).

bitstream( [ H | T ] ) :- bit( H ), bitstream( T ).

|?- X = [ 0, 1, 1, 0 | X ], bitstream( X ).

- traditional logic programming cannot handle
- Goal: Combine traditional LP with coinductive LP

Overview of Coinductive LP

- Coinductive Logic Program is
a definite program with maximal co-Herbrand model declarative semantics.

- Declarative Semantics: across the board dual of traditional LP:
- greatest fixed-points
- terms: co-Herbrand universe Uco(P)
- atoms: co-Herbrand base Bco(P)
- program semantics: maximal co-Herbrand model Mco(P).

Operational Semantics: co-SLD Resolution

- nondeterministic state transition system
- states are pairs of
- a finite list of syntactic atoms [resolvent] (as in Prolog)
- a set of syntactic term equations of the form x = f(x) or x = t
- For a program p :- p. => the query |?- p. will succeed.
- p( [ 1 | T ] ) :- p( T ). => |?- p(X) to succeed with X= [ 1 | X ].

- transition rules
- definite clause rule
- “coinductive hypothesis rule”
- if a coinductive goal G is called,
and G unifies with a call made earlier

then G succeeds.

- if a coinductive goal G is called,

?-G

….

G

coinductive

success

Correctness

- Theorem (soundness). If atom A has a successful co-SLD derivation in program P, then E(A) is true in program P, where E is the resulting variable bindings for the derivation.
- Theorem (completeness). If A Mco(P) has a rational proof, then A has a successful co-SLD derivation in program P.
- Completeness only for rational/regular proofs

Implementation

- Search strategy: hypothesis-first, leftmost, depth-first
- Meta-Interpreter implementation.
query(Goal) :- solve([],Goal).

solve(Hypothesis, (Goal1,Goal2)) :- solve( Hypothesis, Goal1), solve(Hypothesis,Goal2).

solve( _ , Atom) :- builtin(Atom), Atom.

solve(Hypothesis,Atom):- member(Atom, Hypothesis).

solve(Hypothesis,Atom):- notbuiltin(Atom), clause(Atom,Atoms), solve([Atom|Hypothesis],Atoms).

- A complete meta-interpreter available
- Implementation on top of YAP, SWI Prolog available
- Implementation within Logtalk + library of examples

Example: Number Stream

:- coinductive stream/1.

stream( [ H | T ] ) :- num( H ), stream( T ).

num( 0 ).

num( s( N ) ) :- num( N ).

|?- stream( [ 0, s( 0 ), s( s ( 0 ) ) | T ] ).

- MEMO: stream( [ 0, s( 0 ), s( s ( 0 ) ) | T ] )
- MEMO: stream( [ s( 0 ), s( s ( 0 ) ) | T ] )
- MEMO: stream( [ s( s ( 0 ) ) | T ] )
- stream(T)
Answers:

T = [ 0, s(0), s(s(0)) | T ]

T = [ 0, s(0), s(s(0)), s(0), s(s(0)) | T ]

T = [ 0, s(0), s(s(0)) | T ] . . .

T = [ 0, s(0), s(s(0)) | X ] (where X is any rational list of numbers.)

Example: Append

:- coinductive append/3.

append( [ ], X, X ).

append( [ H | T ], Y, [ H | Z ] ) :- append( T, Y, Z ).

|?- Y = [ 4, 5, 6 | Y ], append( [ 1, 2, 3 ], Y, Z).

Answer: Z = [ 1, 2, 3 | Y ], Y=[ 4, 5, 6 | Y]

|?- X = [ 1, 2, 3 | X ], Y = [ 3, 4 | Y ], append( X, Y, Z).

Answer: Z = [ 1, 2, 3 | Z ].

|?- Z = [ 1, 2 | Z ], append( X, Y, Z ).

Answer: X = [ ], Y = [ 1, 2 | Z ] ; X = [1, 2 | X], Y = _

X = [ 1 ], Y = [ 2 | Z ] ;

X = [ 1, 2 ], Y = Z; …. ad infinitum

Example: Comember

member(H, [ H | T ]).

member(H, [ X | T ]) :- member(H, T).

?- L = [1,2 | L], member(3, L). succeeds. Instead:

:- coinductive comember/2. %drop/3 is inductive

comember(X, L) :- drop(X, L, R), comember(X, R).

drop(H, [ H | T ], T).

drop(H, [ X | T ], T1) :- drop(H, T, T1).

?- X=[ 1, 2, 3 | X ], comember(2,X). ?- X = [1,2 | X], comember(3, X).

Answer: yes. Answer: no

?- X=[ 1, 2, 3, 1, 2, 3], comember(2, X).

Answer: no.

?- X=[1, 2, 3 | X], comember(Y, X).

Answer: Y = 1;

Y = 2;

Y = 3;

Co-Logic Programming

- combines both halves of logic programming:
- traditional logic programming
- coinductive logic programming

- syntactically identical to traditional logic programming, except predicates are labeled:
- Inductive, or
- coinductive

- and stratification restriction enforced where:
- inductive and coinductive predicates cannot be mutually recursive. e.g.,
p :- q.

q :- p.

Program rejected, if p coinductive & q inductive

- inductive and coinductive predicates cannot be mutually recursive. e.g.,

Application of Co-LP

- Co-LP allows one to compute both LFP & GFP
- Computable functions can be specified more elegantly
- Interepreters for Modal Logics can be elegantly specified:
- Model Checking: LTL interpreter elegantly specified
- Timed -automata: elegantly modeled and properties verified
- Modeling/Verification of Cyber Physical Systems/Hybrid automata
- Goal-directed execution of Answer Set Programs
- Goal-directed SAT solvers (Davis-Putnam like procedure)
- Planning under real-time constraints
- Operational semantics of the π-calculus (incl. timed π -calculus)
- infinite replication operator modeled with co-induction
Co-LP allows systems to be modeled naturally & elegantly

- infinite replication operator modeled with co-induction

Application: Model Checking

- automated verification of hardware and software systems
- -automata
- accept infinite strings
- accepting state must be traversed infinitely often

- requires computation of lfp and gfp
- co-logic programming provides an elegant framework for model checking
- traditional LP works for safety property (that is based on lfp) in an elegant manner, but not for liveness .

Verification of Properties

- Types of properties: safety and liveness
- Search for counter-example

Safety versus Liveness

- Safety
- “nothing bad will happen”
- naturally described inductively
- straightforward encoding in traditional LP

- liveness
- “something good will eventually happen”
- dual of safety
- naturally described coinductively
- straightforward encoding in coinductive LP

Finite Automata

automata([X|T], St):- trans(St, X, NewSt), automata(T, NewSt).

automata([ ], St) :- final(St).

trans(s0, a, s1). trans(s1, b, s2). trans(s2, c, s3).

trans(s3, d, s0). trans(s2, 3, s0). final(s2).

?- automata(X,s0).

X=[ a, b];

X=[ a, b, e, a, b];

X=[ a, b, e, a, b, e, a, b];

……

……

……

Infinite Automata

automata([X|T], St):- trans(St, X, NewSt), automata(T, NewSt).

trans(s0,a,s1). trans(s1,b,s2). trans(s2,c,s3).

trans(s3,d,s0). trans(s2,3,s0). final(s2).

?- automata(X,s0).

X=[ a, b, c, d | X ];

X=[ a, b, e | X ];

Verifying Liveness Properties

- Verifying safety properties in LP is relatively easy: safety modeled by reachability
- Accomplished via tabled logic programming
- Verifying liveness is much harder: a counterexample to liveness is an infinite trace
- Verifying liveness is transformed into a safety check via use of negations in model checking and tabled LP
- Considerable overhead incurred

- Co-LP solves the problem more elegantly:
- Infinite traces that serve as counter-examples are produced as answers

Verifying Liveness Properties

- Consider Safety:
- Question: Is an unsafe state, Su, reachable?
- If answer is yes, the path to Su is the counter-ex.

- Consider Liveness, then dually
- Question: Is a state, D, that should be dead, live?
- If answer is yes, the infinite path containing D is the counter example
- Co-LP will produce this infinite path as the answer

- Checking for liveness is just as easy as checking for safety

Nested Finite and Infinite Automata

:- coinductive state/2.

state(s0, [s0,s1 | T]):- enter, work, state(s1,T).

state(s1, [s1 | T]):- exit, state(s2,T).

state(s2, [s2 | T]):- repeat, state(s0,T).

state(s0, [s0 | T]):- error, state(s3,T).

state(s3, [s3 | T]):- repeat, state(s0,T).

work. enter. repeat. exit. error.

work :- work.

|?- state(s0,X), absent(s2,X).

X=[ s0, s3 | X ]

An Interpreter for LTL

%--- nots have been pushed to propositions

:- tabled verify/2.

verify(S, [S], A) :- proposition(A), holds(S,A). % p

verify(S, [S], not(A)) :- proposition(A), \+holds(S,A). % not(p)

verify(S,P, or(A,B)) :- verify(S, P, A) ; verify(S, P, B). %A or B

verify(S,P, and(A,B)) :- verify(S,P1, A), verify(S,P2, B). %A and B

(prefix(P2, P1), P=P1 ; prefix(P2,P1), P=P2)

verify(S, [S|P], x(A)) :- trans(S, S1), verify(S1, P, A). % X(A)

verify(S, P, f(A)) :- verify(S, P, A); verify(S, P, x(f(A))). % F(A)

verify(S, P, g(A)) :- coverify(S, P, g(A)). % G(A)

verify(S, P,u(A,B)) :- verify(S, P,B);

verify(S, P,and(A, x(u(A,B)))). % A u B

verify(S, r(A,B)) :- coverify(S, r(A,B)). % A r B

:- coinductive coverify/2.

coverify(S, g(A)) :- verify(S, P, and(A, x(g(A))).

coverify(S, r(A,B)) :- verify(S, P, and(A,B)).

coverify(S, r(A,B)) :- verify(S, P, and(B, x(r(A,B)))).

Verification of Real-Time Systems“Train, Controller, Gate”

Timed Automata

- -automata w/ time constrained transitions & stopwatches
- straightforward encoding into CLP(R) + Co-LP

Verification of Real-Time Systems “Train, Controller, Gate”

:- use_module(library(clpr)).

:- coinductive driver/9.

train(X, up, X, T1,T2,T2). % up=idle

train(s0,approach,s1,T1,T2,T3) :- {T3=T1}.

train(s1,in,s2,T1,T2,T3):-{T1-T2>2,T3=T2}

train(s2,out,s3,T1,T2,T3).

train(s3,exit,s0,T1,T2,T3):-{T3=T2,T1-T2<5}.

train(X,lower,X,T1,T2,T2).

train(X,down,X,T1,T2,T2).

train(X,raise,X,T1,T2,T2).

Verification of Real-Time Systems “Train, Controller, Gate”

contr(s0,approach,s1,T1,T2,T1).

contr(s1,lower,s2,T1,T2,T3):- {T3=T2, T1-T2=1}.

contr(s2,exit,s3,T1,T2,T1).

contr(s3,raise,s0,T1,T2,T2):-{T1-T2<1}.

contr(X,in,X,T1,T2,T2).

contr(X,up,X,T1,T2,T2).

contr(X,out,X,T1,T2,T2).

contr(X,down,X,T1,T2,T2).

Verification of Real-Time Systems “Train, Controller, Gate”

gate(s0,lower,s1,T1,T2,T3):- {T3=T1}.

gate(s1,down,s2,T1,T2,T3):- {T3=T2,T1-T2<1}.

gate(s2,raise,s3,T1,T2,T3):- {T3=T1}.

gate(s3,up,s0,T1,T2,T3):- {T3=T2,T1-T2>1,T1-T2<2 }.

gate(X,approach,X,T1,T2,T2).

gate(X,in,X,T1,T2,T2).

gate(X,out,X,T1,T2,T2).

gate(X,exit,X,T1,T2,T2).

Verification of Real-Time Systems

:- coinductive driver/9.

driver(S0,S1,S2, T,T0,T1,T2, [ X | Rest ], [ (X,T) | R ]) :-

train(S0,X,S00,T,T0,T00), contr(S1,X,S10,T,T1,T10),

gate(S2,X,S20,T,T2,T20), {TA > T},

driver(S00,S10,S20,TA,T00,T10,T20,Rest,R).

|?- driver(s0,s0,s0,T,Ta,Tb,Tc,X,R).

R=[(approach,A), (lower,B), (down,C), (in,D), (out,E), (exit,F),

(raise,G), (up,H) | R ],

X=[approach, lower, down, in, out, exit, raise, up | X] ;

R=[(approach,A),(lower,B),(down,C),(in,D),(out,E),(exit,F),(raise,G),

(approach,H),(up,I)|R],

X=[approach,lower,down,in,out,exit,raise,approach,up | X] ;

% where A, B, C, ... H, I are the corresponding wall clock time of events generated.

DPP – Safety: Deadlock Free

- One potential solution
- Force one philosopher to pick forks in different order than others

- Checking for deadlock
- Bad state is not reachable
- Implemented using Tabled LP

:- table reach/2.

reach(Si, Sf) :- trans(_,Si,Sf).

reach(Si, Sf) :- trans(_,Si,Sfi),

reach(Sfi,Sf).

?- reach([1,1,1,1,1], [2,2,2,2,2]).

no

DPP – Liveness: Starvation Free

- Phil. waits forever on a fork
- One potential solution
- phil. waiting longest gets the access
- implemented using CLP(R)

- Checking for starvation
- once in bad state, is it possible to remain there forever?
- implemented using co-LP

?- starved(X).

no

Other Applications

- Advanced -structures can also be modeled and reasoned about: -PTA and -grammars
- Non monotonic reasoning:
- CoLP allows goal-directed execution of Answer Set Programs (ASP)
- Abductive reasoners can be elegantly implemented
- Answer sets programming can be extended to predicates
- ASP can be elegantly extended with constraints: advanced applications such as planning under real-time constraints become possible

- SAT Solvers can be elegantly written
- Operational semantics of pi-calculus can be given (infinite replication operator modeled with co-induction)

Goal-directed execution of ASP

- Answer set programming (ASP) is a popular formalism for non monotonic reasoning
- Applications in real-world reasoning, planning, etc.
- Semantics given via lfp of a residual program obtained after “Gelfond-Lifschitz” transform
- Popular implementations: Smodels, DLV, etc.
- No goal-directed execution strategy available
- ASP limited to only finitely groundable programs

- Co-logic programming solves both these problems.
- Also provides a goal-directed method to check if a proposition is true in some model of a prop. formula

Why Goal-directed ASP?

- Most of the time, given a theory, we are interested in knowing if a particular goal is true or not.
- Top down goal-directed execution provides operational semantics (important for usability)
- Execution more efficient.
- Tabled LP vs bottom up Deductive Databases

- Why check the consistency of the whole knowledgebase?
- Inconsistency in some unrelated part will scuttle the whole system

- Most practical examples anyway add a constraint to force the answer set to contain a certain goal.
- E.g. Zebra puzzle: :- not satisfied.

- Answer sets of non-finitely groundable programs computable & Constraints incorporated in Prolog style.

Negation in Co-LP: co-SLDNF Resolution

- Given a clause such as
p :- q, not p.

?- p. fails coinductively when not p is encountered

- To incorporate negation in coinductive reasoning, need a negative coinductive hypothesis rule:
- In the process of establishing not(p), if not(p) is seen again in the resolvent, then not(p) succeeds [co-SLDNF Resolution]

- Also, not not p reduces to p.
- Answer set programming makes the “glass completely full” by taking into account failing computations:
- p :- q, not p. is consistent if p = false and q = false

- However, this takes away monotonicity: q can be constrained to false, causing q to be withdrawn, if it was established earlier.

ASP

- Consider the following program, A:
p :- not q. t. r :- t, s.

q :- not p. s.

A has 2 answer sets: {p, r, t, s} & {q, r, t, s}.

- Now suppose we add the following rule to A:
h :- p, not h. (falsify p)

Only one answer set remains: {q, r, t, s}

- Gelfond-Lifschitz Method:
- Given an answer set S, for each p S, delete all rules whose body contains “not p”;
- delete all goals of the form “not q” in remaining rules
- Compute the least fix point, L, of the residual program
- If S = L, then S is an answer set

Goal-directed ASP

- Consider the following program, A’:
p :- not q. t. r :- t, s.

q :- not p, r. s. h :- p, not h.

- Separate into constraint and non-constraint rules: only 1 constraint rule in this case.
- Execute the query under co-LP, candidate answer sets will be generated.
- Keep the ones not rejected by the constraints.
- Suppose the query is ?- q. Execution: q not p, r not not q, r q, r r t, s s success. Ans = {q, r, t, s}
- Next, we need to check that constraint rules will not reject the generated answer set.
- (it doesn’t in this case)

Goal-directed ASP

- In general, for the constraint rules of p as head, p1:- B1. p2:- B2. ... pn :- Bn., generate rule(s) of the form:
chk_p1 :- not(p1), B1.

chk_p2 :- not(p2), B2.

...

chk_pn :- not(p), Bn.

- Generate: nmr_chk :- not(chk_p1), ... , not(chk_pn).
- For each pred. definition, generate its negative version:
not_p :- not(B1), not(B2), ... , not(Bn).

- If you want to ask query Q, then ask ?- Q, nmr_chk.
- Execution keeps track of atoms in the answer set (PCHS) and atoms not in the answer set (NCHS).

Goal-directed ASP

- Consider the following program, P1:
(i) p :- not q. (ii) q:- not r. (iii) r :- not p. (iv) q :- not p.

P1 has 1 answer set: {q, r}.

- Separate into: 3 constraint rules (i, ii, iii)
2 non-constraint rules (i, iv).

p :- not(q).q :- not(r).r :- not(p).q :- not(p).

chk_p :- not(p), not(q).chk_q :- not(q), not(r).chk_r :- not(r), not(p). nmr_chk :- not(chk_p), not(chk_q), not(chk_r).

not_p :- q. not_q :- r, p.not_r :- p.

Suppose the query is ?- r.

Expand as in co-LP: r not p not not q q ( not r fail, backtrack) not p success. Ans={r, q} which satisfies the constraint rules of nmr_chk.

- CPS:
- -- Networked/distributed Hybrid Systems
- -- Discrete digital systems with
- Inputs: continuous physical quantities
- e.g., time, distance, acceleration, temperature, etc.

- Outputs: control physical (analog) devices

- Inputs: continuous physical quantities

- Elegantly modeled via co-LP extended with constraints
- Characteristics of CPS:
- -- perform discrete computations (modeled via LP)

- -- deal with continuous physical quantities (modeled via constraints)
- -- are concurrent (modeled via LP coroutining)
- -- run forever (modeled via coinduction)

CPS Example

Reactor Temperature Control System

θ = θm

r1 >= W

θ = θM

θ = θM

rod2

no_rod

rod1

add2 , c2 := 0

add1

add1 , c1 := 0

r1 = W

in1

out1

r1 := 0

θ = θm

θ = θm

θm <= θ

θ <= θM

θm <= θ

remove1

remove2 , c2 := 0

remove1 , c1 := 0

θ = θM

r2 >= W

.

θ

add2

r2 = W

θ = ― - 50

.

in2

out2

θ

10

θ = ― - 56

r2 := 0

10

shutdown

remove2

.

θ

θ = ― - 60

10

Rod1 & Rod2

trans_r1(out1, add1, in1, T, Ti, To, W) :-

{T – Ti >= W, To = Ti}.

trans_r1(in1, remove1, out1, T, Ti, To, W) :- {To = T}.

trans_r2(out2, add2, in2, T, Ti, To, W) :-

{T – Ti >= W, To = Ti}.

trans_r2(in2, remove2, out2, T, Ti, To, W) :- {To = T}.

r1 >= W

add1

r1 = W

in1

out1

r1 := 0

remove1

r2 >= W

add2

r2 = W

in2

out2

r2 := 0

remove2

Controller

trans_c(norod, add1, rod1, Tetai, Tetao, T, Ti1, Ti2, To1, To2, F) :-

(F == 1 -> Ti = Ti1; Ti = Ti2),

{Tetai < 550, Tetao = 550, exp(e, (T - Ti)/10) = 5,

To1 = T, To2 = Ti2}.

trans_c(rod1, remove1, norodTetai, Tetao, T, Ti1, Ti2, To1, To2, F) :-

{Tetai > 510 Tetao = 510, exp(e, (T - Ti1)/10) = 5,

To1 = T, To2 = Ti2}.

trans_c(norod, add2, rod2, Tetai, Tetao, T, Ti1, Ti2, To1, To2, F) :-

(F == 1 -> Ti = Ti1; Ti = Ti2),

{Tetai < 550, Tetao = 550, exp(e, (T - Ti)/10) = 5,

To1 = Ti1, To2 = T}.

trans_c(rod2, remove2, norod, Tetai, Tetao, T, Ti1, Ti2, To1, To2, F) :-

{Tetai > 510, Tetao = 510, exp(e, (T - Ti2)/10) = 9/5,

To1 = Ti1, To2 = T}.

trans_c(norod, _, shutdown, Tetai, Tetao, T, Ti1, Ti2, To1, To2, F) :-

(F == 1 -> Ti = Ti1; Ti = Ti2),

{Tetai < 550 Tetao = 550, exp(e, (T - Ti)/10) = 5,

To1 = Ti1, To2 = Ti2}.

θ = θm

θ = θM

θ = θM

rod2

no_rod

rod1

add2 , c2 := 0

add1 , c1 := 0

θ = θm

θ = θm

θm <= θ

θ <= θM

θm <= θ

remove2 , c2 := 0

remove1 , c1 := 0

θ = θM

.

θ

θ = ― - 50

.

θ

10

θ = ― - 56

10

shutdown

.

θ

θ = ― - 60

10

Controller | Rod1 | Rod2

:- coinductive(contr/7).

contr(X, Si, T, Tetai, Ti1, Ti2, Fi) :-

(H = add1; H = remove1; H = add2; H = remove2; H = shutdown),

{Ta > T},

freeze(X, contr(Xs, So, Ta, Tetao, To1, To2, Fo)),

trans_c(Si, H, So, Tetai, Tetao, T, Ti1, Ti2, To1, To2, Fi),

((H=add1; H=remove1) -> Fo = 1; Fo = 2),

((H=add1; H=remove1; H=add2; H=remove2) -> X = [ (H, T) | Xs]; X = [ (H, T) ] ).

:- coinductive(rod2/6).

rod2([ (H, T)| Xs], Si1, Si2, Ti1, Ti2, W) :-

H = add2 ->

freeze(Xs,rod2(Xs, Si1, So2, Ti1, To2, W));

H = remove2 ->

freeze(Xs,rod1(Xs, Si1, So2, Ti1, To2, W);

rod2(Xs, Si1, So2, Ti1, To2, W)),

trans_r2(Si2, H, So2, T, Ti2, To2, W);

H = shutdown -> {T - Ti1 < A, T - Ti2 < A}.

:- coinductive(rod1/6).

rod1([ (H, T)| Xs], Si1, Si2, Ti1, Ti2, W) :-

H = add1 ->

freeze(Xs,rod1(Xs, So1, Si2, To1, Ti2, W));

H = remove1 ->

freeze(Xs,rod1(Xs, So1, Si2, To1, Ti2, W);

rod2(Xs, So1, Si2, To1, Ti2, W)),

trans_r1(Si1, H, So1, T, Ti1, To1, W);

H = shutdown -> {T - Ti1 < A, T - Ti2 < A}.

Controller || Rod1 || Rod2

main(S, T, W) :-{T - Tr1 = W, T - Tr2 = W},

freeze(S, (rod1(S, s0, s0, Tr1, Tr2, W);

rod2(S, s0, s0, Tr1, Tr2, W))),

contr(S, s0, T, 510, Tc1, Tc2, 1).

- With this elegant modeling, we were able to improve the bounds on W compared to previous work
- HyTech determines W < 20.44 to prevent shutdown
- Subsequently, using linear hybrid automata with clock translation, HyTech improves to W < 37.8
- Using our LP method, we refine it to W < 38.06

Related Publications

- L. Simon, A. Mallya, A. Bansal, and G. Gupta. Coinductive logic programming. In ICLP’06 .
- L. Simon, A. Bansal, A. Mallya, and G. Gupta. Co-Logic programming: Extending logic programming with coinduction. In ICALP’07.
- G. Gupta et al. Co-LP and its applications, ICLP’07 (tutorial)
- G. Gupta et al. Infinite computation, coinduction and computational logic. CALCO’11
- R. Min, A. Bansal, G. Gupta. Co-LP with negation, LOPSTR 2009
- R. Min, G. Gupta. Towards Predicate ASP, AIAI’09
- N. Saeedloei, G. Gupta. Coinductive Constraint Programming FLOPS12.
- N. Saeedloei, G. Gupta, Timed π-Calculus
- N. Saeedloei, G. Gupta. Modeling/verification of CPS with coinductive coroutined CLP(R)
- K. Marple, A. Bansal, R. Min, G. Gupta. Goal-directed Execution of ASP. PPDP’12
- K. Marple, G. Gupta, Galliwasp: A Goal-Directed Answer Set Solver . LOPSTR 2012

Conclusion

- Circularity is a common concept in everyday life and computer science:
- Logic/LP is unable to cope with circularity
- Solution: introduce coinduction in Logic/LP
- dual of traditional logic programming
- operational semantics for coinduction
- combining both halves of logic programming

- applications to verification, non monotonic reasoning, negation in LP, propositional satisfiability, hybrid systems, cyberphysical systems
- Goal-directed impl. of non-monotonic reasoning avail.
- Metainterpreter available:
http://www.utdallas.edu/~gupta/meta.tar.gz

Conclusion (cont’d)

- Computation can be classified into two types:
- Well-founded,
- Based on computing elements in the LFP
- Implemented w/ recursion (start from a call, end in base case)

- Consistency-based
- Based on computing elements in the GFP (but not LFP)
- Implemented via co-recursion (look for consistency)

- Well-founded,
- Combining the two allows one to compute any computable function elegantly:
- Implementations of modal logics (LTL, etc.)
- Complex reasoning systems (NM reasoners)

- Combining them is challenging

Conclusions: Future Work

- Design execution strategies that enumerate all rational infinite solutions while avoiding redundant solutions
p([a|X]) :- p(X).

p([b|X]) :- p(X).

-- If X = [a|X] is reported, then avoid X = [a, a | X], X = [a,a,a|X], etc.

-- A fair depth first search strategy that will produce

X = [a,b|X]

- Combining induction (tabling) and co-induction:
- Stratified co-LP: equivalent to stratified Büchi tree automata (SBTAs)
- Non-stratified co-LP: inspired by Rabin automata; 3 class of predicates (i) coinductive, (ii) weakly coinductive and (iii) strongly coinductive

Coinductive LP: An Example

- Let P1 be the following coinductive program.
:- coinductive from/2. from(x) = x cons from(x+1)

from( N, [ N | T ] ) :- from( s(N), T ).

|?- from( 0, X ).

- co-Herbrand Universe: Uco(P1) = N L where
N=[0, s(0), s(s(0)), ... ], ={ s(s(s( . . . ) ) ) }, and L is the the set of all finite and infinite lists of elements in N, and L.

- co-Herbrand Model:
Mco(P1)={ from(t, [t, s(t), s(s(t)), ... ]) | t Uco(P1) }

- from(0, [0, s(0), s(s(0)), ... ]) Mco(P1) implies the query holds
- Without “coinductive” declaration of from, Mco(P1’)=
This corresponds to traditional semantics of LP with infinite trees.

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