Adaptive Multi-agent Programming in GTGolog. We present a novel approach to adaptive multi-agent programming, which is based on an integration of the agent programming language GTGolog with adaptive dynamic programming techniques. GTGolog combines explicit agent programming in Golog with multi-agent planning in stochastic games. A drawback of this framework, however, is that the transition probabilities and reward values of the domain must be known in advance and then cannot change anymore. But such data is often not available in advance and may also change over the time. The adaptive generalization of GTGolog in this paper is directed towards letting the agents themselves explore and adapt these data, which is more useful for realistic applications. We use high-level programs for generating both abstract states and optimal policies, which benefits from the deep integration between action theory and high-level programs in the Golog framework.

References in zbMATH (referenced in 2 articles )

Showing results 1 to 2 of 2.
Sorted by year (citations)

  1. Finzi, Alberto; Lukasiewicz, Thomas: Partially observable game-theoretic agent programming in Golog (2020)
  2. Finzi, Alberto; Lukasiewicz, Thomas: Adaptive multi-agent programming in GTGolog (2006) ioport