Abstract
Abstract Our work is a contribution to automation design for human-machine cooperation with explicit emancipated cooperative decision making. We propose an adaptive negotiation framework as a model for human-machine interaction on decision level. This expands modeling of human-machine cooperation, starting at the stabilization and trajectory level with approaches such as shared control, towards higher levels of interaction as guidance and navigation. In essence, the framework extends the well-known basic negotiation model of multi-agent systems by an explicit adaptation of the agent’s negotiation behavior. The adaptation is based on an opponent model using a Bayesian learning approach. An exemplary implementation for the application of human-automation interaction in autonomous driving is introduced. First results prove the high flexibility of the framework to model human negotiation behavior.
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