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AUTOMATA PROGRAMS CONSTRUCTION FROM SPECIFICATION WITH AN ANT COLONY OPTIMIZATION ALGORITHM BASED ON MUTATION GRAPH

Daniil S. Chivilikhin, Vladimir I. Ulyantsev, Valeriy V. Vyatkin, Anatoly A. Shalyto
2014 Naučno-tehničeskij Vestnik Informacionnyh Tehnologij, Mehaniki i Optiki  
The procedure of testing traditionally used in software engineering cannot guarantee program correctness; therefore verification is used at the excess requirements to programs reliability. Verification makes it possible to check certain properties of programs in all possible computational states; however, this process is very complex. In the model checking method a model of the program is built (often, manually) and requirements in terms of temporal logic are formulated. Such temporal
more » ... of the model can be checked automatically. The main issue in this framework is the gap between the program and its model. Automata-based programming paradigm gives the possibility to overcome this limitation. In this paradigm, program logic is represented using finite-state machines. The advantage of finite-state machines is that their models can be constructed automatically. The paper deals with the application of mutation-based ant colony optimization algorithm to the problem of finite-state machine construction from their specification, defined by test scenarios and temporal properties. The presented approach has been tested on the elevator doors control problem as well as on randomly generated data. Obtained results show the ant colony algorithm is two-three times faster than the previously used genetic algorithm. The proposed approach can be recommended for inferring control programs for critical systems.
doaj:f6208e0ac267470c9af90b169b79138e fatcat:tdsomgqyrfhbdjjacwkmff5pbm

Inferring automata-based programs from specification with mutation-based ant colony optimization

Daniil Chivilikhin, Vladimir Ulyantsev
2014 Proceedings of the 2014 conference companion on Genetic and evolutionary computation companion - GECCO Comp '14  
events, ∆ is a set of output actions, δ : S × Σ × 2 Z → S is the transitions function and λ : S × Σ × 2 Z → ∆ * is the actions function.  ...  For all other uses, contact the owner/author(s). Copyright is held by the author/owner(s).  ... 
doi:10.1145/2598394.2598446 dblp:conf/gecco/ChivilikhinU14 fatcat:naliu5f3jzfstb4lfh5ny73xha

Experimental Study of Automated Parameter Tuning on the Example of irace and the Traveling Salesman Problem

Daniil Chivilikhin
2016 Proceedings of the 2016 on Genetic and Evolutionary Computation Conference Companion - GECCO '16 Companion  
For all other uses, contact the owner/author(s).  ...  For both T = 6 h and T = 12 h, results for t = 60 s are significantly better than for t = 15 s and t = 30 s (the paired Wilcoxon signed-rank test [3] was used to check statistical significance of differences  ... 
doi:10.1145/2908961.2908978 dblp:conf/gecco/Chivilikhin16 fatcat:q6yjdvken5hybaf5fnw2iohem4

Genetic Search of Pickup and Delivery Problem Solutions for Self-driving Taxi Routing [chapter]

Viacheslav Shalamov, Andrey Filchenkov, Anatoly Shalyto
2016 IFIP Advances in Information and Communication Technology  
Authors would like to thank Daniil Chivilikhin for useful comments. This work was financially supported by the Government of the Russian Federation, Grant 074-U01.  ...  S F ∝ {f i }; S F = (0.327, 0.09, 0.158, 0.16, 0.265), S C ∝ {c i }; S C = (0.348, 0.166, 0.160, 0.16, 0.166), S F/N ∝ {f i /n i }; S F/N = (0.4417, 0.1215, 0.2134, 0.2162, 0.007).  ...  Thus, there five pure strategies: S L20 = (1, 0, 0, 0, 0), S DB = (0, 1, 0, 0, 0), S CE = (0, 0, 1, 0, 0), S PE = (0, 0, 0, 1, 0), S RB = (0, 0, 0, 0, 1).  ... 
doi:10.1007/978-3-319-44944-9_30 fatcat:bd2lsncxkzexbiodahawcndyga

Improving the quality of supervised finite-state machine construction using real-valued variables

Igor Buzhinsky, Daniil Chivilikhin, Vladimir Ulyantsev, Fedor Tsarev
2014 Proceedings of the 2014 conference companion on Genetic and evolutionary computation companion - GECCO Comp '14  
PROBLEM STATEMENT An FSM is a sextuple (S, s0, E, A, δ, λ) where S is a finite set of states, s0 ∈ S is a start state, E is a set of input events, A is a set of output actions, δ : S × E → S is a transition  ...  In each state only several of them are significant: for each state sS and a combination of significant predicate values in s, a transition is defined.  ... 
doi:10.1145/2598394.2605679 dblp:conf/gecco/BuzhinskyCUT14 fatcat:tdq7ywb4jrc4hpmm76amdyckii

Small-Moves Based Mutation For Pick-Up And Delivery Problem

Viacheslav Shalamov, Andrey Filchenkov, Daniil Chivilikhin
2016 Proceedings of the 2016 on Genetic and Evolutionary Computation Conference Companion - GECCO '16 Companion  
For a vertex subset S, let δ(S) denote a set of edges, which have exactly one vertex belonging to S. For E ⊆ E let x(E ) = P (i,j)∈E xij. And for S ⊆ V let x(S) = i,j∈S xij.  ... 
doi:10.1145/2908961.2931666 dblp:conf/gecco/ShalamovFC16 fatcat:7s3iy2aimbe6hlp5c2unnbm4ae

Learning Finite-State Machines with Ant Colony Optimization [chapter]

Daniil Chivilikhin, Vladimir Ulyantsev
2012 Lecture Notes in Computer Science  
between strings s 1 and s 2 .  ...  λ is a transition function mapping a state and an event to an output action, i.e. λ(s, e) = a, where sS, e ∈ Σ, a ∈ Δ and s 0 is the initial state.  ... 
doi:10.1007/978-3-642-32650-9_27 fatcat:cig3j5pwo5a7lggejj7o6wmvs4

Test-based extended finite-state machines induction with evolutionary algorithms and ant colony optimization

Daniil Chivilikhin, Vladimir Ulyantsev, Fedor Tsarev
2012 Proceedings of the fourteenth international conference on Genetic and evolutionary computation conference companion - GECCO Companion '12  
|T| denotes the cardinality of set T, which is the number of test examples, len(s) denotes the length of sequence s and ( ) 2 1 , s s ED denotes the edit distance between sequences s 1 and s 2 .Here M  ...  Definition of an EFSM An EFSM is a seven-tuple <E, X, Z, Σ, s 0 , φ, δ>, where E is a set of events, X is a set of Boolean input variables, Z is a set of output actions, Σ is a set of states, s 0 ∈Σ is  ... 
doi:10.1145/2330784.2330883 dblp:conf/gecco/ChivilikhinUT12 fatcat:6es463qyp5epjmjcadithj4t5u

Inferring Temporal Properties of Finite-State Machine Models with Genetic Programming

Daniil Chivilikhin, Ilya Ivanov, Anatoly Shalyto
2015 Proceedings of the Companion Publication of the 2015 on Genetic and Evolutionary Computation Conference - GECCO Companion '15  
Formula weight W is defined in the following way: W (s) = ws, sS; W (o(arg 1 , . . . , arg n )) = wo + n i=1 W (arg i ), n = {1, 2}.  ...  We assign each operator o ∈ O and propositional variable sS their weight, wo and ws, respectively.  ... 
doi:10.1145/2739482.2768475 dblp:conf/gecco/ChivilikhinIS15 fatcat:3urjrhquzvhdfjiu54zfob5gve

Reconstruction of function block logic using metaheuristic algorithm: Initial explorations

Daniil Chivilikhin, Anatoly Shalyto, Sandeep Patil, Valeriy Vyatkin
2015 2015 IEEE 13th International Conference on Industrial Informatics (INDIN)  
An execution scenario s is a series of execution scenario elements s i , where each element consists of a set of input variable values in i and a set of output variable values out i .  ... 
doi:10.1109/indin.2015.7281912 dblp:conf/indin/ChivilikhinSPV15 fatcat:sufmjof6gvbmbjpllhsdkgllgu

SAT-based Counterexample-Guided Inductive Synthesis of Distributed Controllers

Konstantin Chukharev, Dmitrii Suvorov, Daniil Chivilikhin, Valeriy Vyatkin
2020 IEEE Access  
DANIIL CHIVILIKHIN received the bachelor's and master's degrees in applied mathematics and informatics and the Ph.D. degree in technical sciences (mathematics and software for computing systems) from ITMO  ...  Thus, Sender has the following interface: • I s = {REQ}; • O s = {CNF}; • X s = {send, timeout, acknowledge, input_bit}; • Z s = {done, packet, output_bit}.  ... 
doi:10.1109/access.2020.3037780 fatcat:a2v6an3grngpfcufospvcpehfu

Learning Finite-State Machines with Classical and Mutation-Based Ant Colony Optimization: Experimental Evaluation

Daniil Chivilikhin, Vladimir Ulyantsev
2013 2013 BRICS Congress on Computational Intelligence and 11th Brazilian Congress on Computational Intelligence  
. δ : S ×Σ → S is the transitions function and λ : S × Σ → ∆ is the actions function.  ...  The set of feasible solutions isS =X ∩ S and S * ⊂S is a non-empty set of optimal feasible solutions.  ... 
doi:10.1109/brics-cci-cbic.2013.93 fatcat:euyevpcz45citmbr6alskeudlq

Inferring Automata Logic from Manual Control Scenarios: Implementation in Function Blocks

Daniil Chivilikhin, Anatoly Shalyto, Valeriy Vyatkin
2015 2015 IEEE Trustcom/BigDataSE/ISPA  
S applyAlg(s 0 . out, a) = s 1 . out} for all sS do for http://www.holobloc.com/doc/fbdk  ...  Firstly, for each scenario s and each two consequent scenario elements s i and s i+1 we add to A an algorithm that transforms s i . out to s i+1 . out.  ... 
doi:10.1109/trustcom.2015.649 dblp:conf/trustcom/ChivilikhinSV15 fatcat:ncjwvnu4frb43dh3lrnqh4kl3u

Learning Finite-State Machines: Conserving Fitness Function Evaluations by Marking Used Transitions

Daniil Chivilikhin, Vladimir Ulyantsev
2013 2013 12th International Conference on Machine Learning and Applications  
denotes the length of sequence s and ED (s 1 , s 2 ) is the edit distance between sequences s 1 and s 2 .  ...  A Mealy FSM is formally defined as a six-tuple (S, s 0 , Σ, Δ, δ, λ), where S is a set of states, s 0 ∈ S is the start state, Σ is a set of input events and Δ is a set of output actions. δ : S × Σ → S  ... 
doi:10.1109/icmla.2013.111 dblp:conf/icmla/ChivilikhinU13 fatcat:isnch4nsfbcqxkj2wf3bwxeop4

Reconstruction of Function Block Logic Using Metaheuristic Algorithm

Daniil Chivilikhin, Anatoly Shalyto, Sandeep Patil, Valeriy Vyatkin
2017 IEEE Transactions on Industrial Informatics  
Chivilikhin would not help in frequently encountered situations when the source code is no longer available.  ...  to |s| − 1 do 5: y next ← M y .nextState(s i .e in , s i .χ) 6: if y next = −1 then 7: y ← y next , z ← a y .apply(z) 8: n sc ← n sc + 1 9: e out ← M y .e out 10: δ var ← 1 |z| ∆ H (s i .ζ, z)  ... 
doi:10.1109/tii.2017.2710224 fatcat:nw4r2fmeazb7nomga26fhjevai
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