An AsmL model for an Intelligent Vehicle Control System

An abstract state machine (ASM) is a mathematical model of the system’s evolving, runtime state. ASMs can be used to faithfully capture the abstract structure and step-wise behaviour of any discrete systems. An easy way to understand ASMs is to see them as defining a succession of states that may follow an initial state. We present a machine-executable model for an Intelligent Vehicle Control System, implemented in the specification language AsmL. Executable specifications are descriptions of how software components work. AsmL is capable of describing the evolving state of asynchronous, concurrent systems, such as agent based systems. The mathematical background for the intelligent control of vehicles is represented by the stochastic automata. A stochastic automaton can perform a finite number of actions in a random environment. When a specific action is performed, the environment responds by producing an environment output that is stochastically related to the action. This response may be favourable or unfavourable. The proposed model is verified through simulation in SpecExplorer tool from Microsoft Research. Key-Words: Stochastic Learning Automata, Reinforcement Learning, ASMs, systems modeling