Structured Control for Active Tree The Decidability of AXML.

Slides:



Advertisements
Similar presentations
Routing Complexity of Faulty Networks Omer Angel Itai Benjamini Eran Ofek Udi Wieder The Weizmann Institute of Science.
Advertisements

Black Box Checking Book: Chapter 9 Model Checking Finite state description of a system B. LTL formula. Translate into an automaton P. Check whether L(B)
Automatic Verification Book: Chapter 6. How can we check the model? The model is a graph. The specification should refer the the graph representation.
Example of Constructing the DAG (1)t 1 := 4 * iStep (1):create node 4 and i 0 Step (2):create node Step (3):attach identifier t 1 (2)t 2 := a[t 1 ]Step.
Graph Algorithms Algorithm Design and Analysis Victor AdamchikCS Spring 2014 Lecture 11Feb 07, 2014Carnegie Mellon University.
Bart Jansen 1.  Problem definition  Instance: Connected graph G, positive integer k  Question: Is there a spanning tree for G with at least k leaves?
Distributed Computing 5. Snapshot Shmuel Zaks ©
Data-Flow Analysis II CS 671 March 13, CS 671 – Spring Data-Flow Analysis Gather conservative, approximate information about what a program.
CS 267: Automated Verification Lecture 2: Linear vs. Branching time. Temporal Logics: CTL, CTL*. CTL model checking algorithm. Counter-example generation.
Automatic Verification Book: Chapter 6. What is verification? Traditionally, verification means proof of correctness automatic: model checking deductive:
Petri Nets Section 2 Roohollah Abdipur.
Based on: Petri Nets and Industrial Applications: A Tutorial
François Fages MPRI Bio-info 2006 Formal Biology of the Cell Modeling, Computing and Reasoning with Constraints François Fages, Constraints Group, INRIA.
Introduction to Graph “theory”
MINIMUM COST FLOWS: NETWORK SIMPLEX ALGORITHM A talk by: Lior Teller 1.
From Monotonic Transition Systems to Monotonic Games Parosh Aziz Abdulla Uppsala University.
On the Dynamics of PB Systems with Volatile Membranes Giorgio Delzanno* and Laurent Van Begin** * Università di Genova, Italy ** Universitè Libre de Bruxelles,
IE 469 Manufacturing Systems
Models for AXML Keeping Decidability in Mind.. AXML (on 1 peer) felony age query last felonies of name_of_a_child, and append it under.
The Out of Kilter Algorithm in Introduction The out of kilter algorithm is an example of a primal-dual algorithm. It works on both the primal.
1 Introduction to Computability Theory Lecture12: Decidable Languages Prof. Amos Israeli.
1 Formal Methods in SE Qaisar Javaid Assistant Professor Lecture # 11.
Topics: 1. Finding a cycle in a graph 2. Propagation delay - example 3. Trees - properties מבנה המחשב - אביב 2004 תרגול 3#
Design and Analysis of Algorithms
CS 536 Spring Global Optimizations Lecture 23.
Validating Streaming XML Documents Luc Segoufin & Victor Vianu Presented by Harel Paz.
Temporal Logic and Model Checking. Reactive Systems We often classify systems into two types: Transformational: functions from inputs available at the.
Ecs289m Spring, 2008 Network Formation S. Felix Wu Computer Science Department University of California, Davis
1 Petri Nets Marco Sgroi EE249 - Fall 2001 Most slides borrowed from Luciano Lavagno’s lecture ee249 (1998)
Prof. Fateman CS 164 Lecture 221 Global Optimization Lecture 22.
Chapter 11: Limitations of Algorithmic Power
DANSS Colloquium By Prof. Danny Dolev Presented by Rica Gonen
*Department of Computing Science University of Newcastle upon Tyne **Institut für Informatik, Universität Augsburg Canonical Prefixes of Petri Net Unfoldings.
Flavio Lerda 1 LTL Model Checking Flavio Lerda. 2 LTL Model Checking LTL –Subset of CTL* of the form: A f where f is a path formula LTL model checking.
Prof. Bodik CS 164 Lecture 16, Fall Global Optimization Lecture 16.
Binary Decision Diagrams for First Order Predicate Logic By: Jan Friso Groote Afsaneh Shirazi.
15-820A 1 LTL to Büchi Automata Flavio Lerda A 2 LTL to Büchi Automata LTL Formulas Subset of CTL* –Distinct from CTL AFG p  LTL  f  CTL. f.
Complexity and Computability Theory I Lecture #13 Instructor: Rina Zviel-Girshin Lea Epstein Yael Moses.
1.3 Modeling with exponentially many constr.  Some strong formulations (or even formulation itself) may involve exponentially many constraints (cutting.
Structured Control for Active Tree Tree Pattern Rewriting Systems (TPRS)
Model Checking Lecture 4 Tom Henzinger. Model-Checking Problem I |= S System modelSystem property.
Networks of Queues Plan for today (lecture 6): Last time / Questions? Product form preserving blocking Interpretation traffic equations Kelly / Whittle.
Theory of Computation, Feodor F. Dragan, Kent State University 1 TheoryofComputation Spring, 2015 (Feodor F. Dragan) Department of Computer Science Kent.
Pushdown Automata Chapters Generators vs. Recognizers For Regular Languages: –regular expressions are generators –FAs are recognizers For Context-free.
Ivan Lanese Computer Science Department University of Bologna/INRIA Italy Decidability Results for Dynamic Installation of Compensation Handlers Joint.
Oct 26, 2001CSE 373, Autumn A Forest of Trees Binary search trees: simple. –good on average: O(log n) –bad in the worst case: O(n) AVL trees: more.
CSCI1600: Embedded and Real Time Software Lecture 11: Modeling IV: Concurrency Steven Reiss, Fall 2015.
AVL Trees An AVL tree is a binary search tree with a balance condition. AVL is named for its inventors: Adel’son-Vel’skii and Landis AVL tree approximates.
1 Reasoning with Infinite stable models Piero A. Bonatti presented by Axel Polleres (IJCAI 2001,
CSE 311 Foundations of Computing I Lecture 28 Computability: Other Undecidable Problems Autumn 2011 CSE 3111.
2/1/20161 Computer Security Foundational Results.
1 CSEP590 – Model Checking and Automated Verification Lecture outline for July 9, 2003.
Fault tolerance and related issues in distributed computing Shmuel Zaks GSSI - Feb
Overview of the theory of computation Episode 3 0 Turing machines The traditional concepts of computability, decidability and recursive enumerability.
Giansalvo EXIN Cirrincione unit #4 Single-layer networks They directly compute linear discriminant functions using the TS without need of determining.
The minimum cost flow problem. Solving the minimum cost flow problem.
Operational Semantics Mooly Sagiv Reference: Semantics with Applications Chapter 2 H. Nielson and F. Nielson
15.082J and 6.855J and ESD.78J Network Simplex Animations.
Technology of information systems Lecture 5 Process management.
Computational Geometry
The minimum cost flow problem
Clockless Computing COMP
Automatic Verification
Paul Ammann & Jeff Offutt
AVL Trees CENG 213 Data Structures.
Introduction to Petri Nets (PNs)
Optimizations using SSA
1.3 Modeling with exponentially many constr.
Network Simplex Animations
Program correctness Model-checking CTL
Presentation transcript:

Structured Control for Active Tree The Decidability of AXML

AXML (on 1 peer) trophy age query last trophys of name_of_a_child, and append it under trophy F.Landis Paris-Nice 26 trophy age F.Landis Paris-Nice 35 Tour de France M.Indurain - Confluence: does asking first Landis or Indurain result in same doc? - Termination: Is there no infinite sequence of fireable services? - Reachability: Can some configuration be reached? Yes No ??? invoc. of service = rewriting rule

Positive AXML query last trophys of name_of_a_child, and append it under trophy Positive AXML: If a service invocation is possible some day, it is possible forever. => Services can only add, never delete. Services cannot stop. Ex: Non Positive: can also delete trophy if doping. Confluence: - Termination: - Positive Term.: Any sequence ultimately stays in same equivalence class - Reachability: Always Yes Always No decidable ???

Over Positivity? Positive AXML: Services can only add, never delete. Services cannot change. What if things can be changed? Termination/Confluence becomes non trivial, interesting under simple queries

Distributed Tennis Fields Federer Play Roland Garros S.Lenglen Central Booked S. Lenglen Federer Fields act independently, can book themselves if find a request (2 can be booked for the same player!) Free LeaveRequest root

X(playing) Y(playing) Rules = Tree Transformations Free root Booked root Request root player $ Booked root player court Request root player root $ player play Query one-in root Leave root player Free root player Booked Query not-empty play root player root Query all root $ Variable of query changed One answer of query, created deleted X+subtree deleted All playing players created ancestor variable

Rules = Tree Transformations Court Free Request root Court Booked Play root Court No query player We can also do it in one step:

Rules = Tree Transformations Query one-in is not needed, can be done in Tree pattern Free Request root Booked Request root No query player Free root Request root player $ Query one-in Booked

Rules = Tree Transformations Query-all + guard counting number answers Tree Pattern TTree Pattern T’ Nodes in T’ and not in T are created Nodes in T and not in T’ are deleted + its subtree deleted Nodes in T and T’ are conserved with its subtree (can be moved) $ in T’ is replaced by the forest of results of query. Rule = (T,query,guard,T’):

Rewriting Step Court Free Request root Court Booked Play root Court No query player Federer Roland Garros S.Lenglen Central Booked Federer Free Request Federer Play Roland Garros S.Lenglen Central Booked Central Federer injective Document New Document Rewriting Rule

Formatting of the query play root player Query = 2 Tree Pattern, transformation as before to format result player court playing Simple query: use variables General query: use same name of nodes (copy subtree) play root Moya Central play Nadal Lenglen play Federer Lenglen Nadal Lenglen playing Federer Lenglen Moya Central query

Possible Options Depth of Tree (Bounded/unbounded) Degree of Tree (Bounded/unbounded) Successors on brothers Number of data type (finite/infinite) Service can only delete itself or not, or nothing deleted Well structured query (cannot test non existence of a TP, or at most…)

Options and Undecidability The following leads to undecidability: Non positive query + any infiniteness (unbounded depth or degree or data type is |N) (2 counters machine) or New (Last time): positive query + service can only delete itself + any linear order (successor on brothers or unbounded tree or data type is |N). Unbounded degree does not suffice (coding of turing machine on words-rewriting with query) or use Loeding’s Thesis and rewriting on trees

Options and Decidability The following leads to some decidability: Depth and Degree of Tree Bounded + finite set of data type (finite state systems) or Service cannot move/delete (monotonic systems) or New: Depth of Tree Bounded + no Successors on brothers + finite number of data type + Positive guards. (Well Structured Transition System) Allow : Unbounded Degree of Tree Service can delete,move anything

WSTS the following < is a well quasi order: A< B if A can be injectively send on B (son/label preserved). Then, In any infinite sequence, there exists u_i > u_j with i>j trophy 26 F.Landis TdF M.Indurain trophy 42 TdETdF WSTS for well quasi order < finite degree/number of rules If X  Y and X ->* X’ then  Y’ with X’  Y’ and Y ->* Y’ X<YX<Y X’ <Y’

WSTS WSTS for well quasi order < finite degree/number of rules If X  Y and X ->* X’ then  Y’ with X’  Y’ and Y ->* Y’ X<YX<Y X’ <Y’ -Build the transition system TS - Do not extend Y with X -> Y and Y  X. - Mark such Y. Prop: TS has finite number of states contradiction: Koenig with Finite degree, finite number of initial states, infinite number of states: inifinite path. With Extended Dickson: there is X<Y on that path and Y is extended, contradiction. false with guards « less than »…

Relevant Properties Finite State = is there finite number of documents Termination: is there no cycle nor marked states Reachability: can i reach doc D. more complicated than for Petri Nets. Confluence: not clear how to separete even and odd inifinite sequence Weak reachability: Given D, can i reach D’  D. Backward methods exist. Weak confluence: all reachable documents s,s’, can reach respectively some t,t’ with t>t’ = Is there a unique maximal strongly connected component in abst. graph of docs Complexity: probably tower of exponential wrt depth of tree. Lower bound? what if we assume that a service can only close itself? -Do not extend Y with X -> Y and Y  X, Mark such Y.

(Un)Decidability Strict = no deletion allowed, but moves are allowed. Not strict, we can have (delete subtree) X  Y and X ->* X’ and Y’ with X’  Y’ and Y ->* Y’ Strict, we always have If X  Y and X ->* X’ then  Y’ with X’  Y’ and Y ->* Y’ Harder than reachability

Discussion Cannot handle optimization: no guards « less than » (think Dell supply chain, if less than 3 items in revolver, order something) So far, set of labels is finite (we know set of players, fields beforehand). Might work with inifinite set (generator of new players, fields, open systems). We have bag semantics, it makes sense with finite set of labels abstracting inifinite set. players Federer Moya Nadal players player Abstracted in Weak reachability = regular well structured properties. We can know whether there exists a path wtih (TP 1 or TP 2 ) Until (TP 3 ) (no negation). (add one node = state + make new rules updating state depending on TP_i)