| 總結: | We present novel techniques for neuro-symbolic
concurrent stochastic games, a recently proposed
modelling formalism to represent a set of probabilistic agents operating in a continuous-space
environment using a combination of neural network based perception mechanisms and traditional
symbolic methods. To date, only zero-sum variants
of the model were studied, which is too restrictive
when agents have distinct objectives. We formalise
notions of equilibria for these models and present
algorithms to synthesise them. Focusing on the
finite-horizon setting, and (global) social welfare
subgame-perfect optimality, we consider two distinct types: Nash equilibria and correlated equilibria. We first show that an exact solution based
on backward induction may yield arbitrarily bad
equilibria. We then propose an approximation algorithm called frozen subgame improvement, which
proceeds through iterative solution of nonlinear
programs. We develop a prototype implementation
and demonstrate the benefits of our approach on
two case studies: an automated car-parking system
and an aircraft collision avoidance system.
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