Evolving algebras 1993: Lipari guide
Specification and validation methods
Incremental reinforcement learning for designing multi-agent systems
Proceedings of the fifth international conference on Autonomous agents
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Recent Advances in Hierarchical Reinforcement Learning
Discrete Event Dynamic Systems
Multiple stochastic learning automata for vehicle path control in an automated highway system
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
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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.