Fedor Tsarev

Extended Finite-State Machine Induction Using SAT-Solver

In the paper we describe the extended finite-state machine (EFSM) induction method that uses SAT-solver. Input data for the induction algorithm is a set of test scenarios. The algorithm consists of several steps: scenarios tree construction, compatibility graph construction, Boolean formula construction, SAT-solver invocation and finite-state machine construction from satisfying assignment. These extended finite-state machines can be used in automata-based programming, where programs are designed as automated controlled objects. Each automated controlled object contains a finite-state machine and a controlled object. The method described has been tested on randomly generated scenario sets of size from 250 to 2000 and on the alarm clock controlling EFSM induction problem where it has greatly outperformed genetic algorithm.

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

In this paper we consider the problem of extended finite-state machines induction. The input data for this problem is a set of tests. Each test consists of two sequences - an input sequence and a corresponding output sequence. We present a new method of Extended Finite-State Machines (EFSM) induction based on an Ant Colony Optimization algorithm (ACO) and a new meaningful test-based crossover operator for EFSMs. New algorithms are compared with a genetic algorithm (GA) using a traditional crossover, a (1+1) evolutionary strategy and a random mutation hill climber. This comparison shows that the use of test-based crossover greatly improves performance of GA. GA on average also significantly outperforms the hill climber and evolutionary strategy. ACO outperforms GA, and the difference between average performance of ACO and GA hybridized with hill climber is insignificant.

Search-based construction of finite-state machines with real-valued actions: new representation model

In this paper a search-based method for constructing finite-state machines (FSMs) with continuous (real-valued) output actions is improved. A more flexible FSM representation model is presented and compared with the previous one on the problem of unmanned aircraft control.

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

The use of finite-state machines (FSMs) is a reliable choice for control system design since they can be formally verified. In this paper a problem of constructing FSMs with real-valued input and control parameters is considered. It is supposed that a set of human-created behavior examples, or tests, is available. One of the earlier approaches for solving the problem suggested using genetic algorithms together with a transition labeling algorithm. This paper improves this approach via the use of real-valued variables which are calculated using the FSMs input data. FSMs with real-valued variables are represented as systems of linear controllers. The new approach allows to synthesize FSMs of better quality than it was possible earlier.

Application of Genetic Programming for Generation of Controllers represented by Automata

Abstract This paper proposes an application of genetic programming for construction of state machines controlling systems with complex behavior. Application of this method is illustrated on example of unmanned aerial vehicle (UAV) control. It helps to find control strategies of collaborative behavior of UAV teams. Multi-agent approach is used, where every agent that controls a UAV is presented by a deterministic finite state machine. Two representations of finite state machines are used: abridged transition tables and decision trees. Novel algorithms for fixing connections between states and for removing unachievable branches of trees are proposed.

Application of Genetic Algorithms for Construction of Moore Automaton and Systems of Interacting Mealy Automata in "Artificial Ant" Problem

a genetic algorithm for construction of Moore finite state machines is described in the paper. This algorithm can be also applied to construct systems of interacting Mealy finite state machines. An example of application of these algorithms for “Artificial ant” problem is also described.