Partially Observable Stochastic Games with Neural Perception Mechanisms

• URL: http://arxiv.org/abs/2310.11566v3
• Date: Sun, 30 Jun 2024 10:13:42 GMT
• Title: Partially Observable Stochastic Games with Neural Perception Mechanisms
• Authors: Rui Yan, Gabriel Santos, Gethin Norman, David Parker, Marta Kwiatkowska,
• Abstract summary: We propose the model of neuro-symbolic partially-observable games (NS-POSGs) We focus on a one-sided setting with a partially-informed agent using discrete, data-driven observations and another, fully-informed agent. We present a new method, called one-sided NS-HSVI, for approximate solution of one-sided NS-POSGs.
• Score: 31.51588071503617
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Stochastic games are a well established model for multi-agent sequential decision making under uncertainty. In practical applications, though, agents often have only partial observability of their environment. Furthermore, agents increasingly perceive their environment using data-driven approaches such as neural networks trained on continuous data. We propose the model of neuro-symbolic partially-observable stochastic games (NS-POSGs), a variant of continuous-space concurrent stochastic games that explicitly incorporates neural perception mechanisms. We focus on a one-sided setting with a partially-informed agent using discrete, data-driven observations and another, fully-informed agent. We present a new method, called one-sided NS-HSVI, for approximate solution of one-sided NS-POSGs, which exploits the piecewise constant structure of the model. Using neural network pre-image analysis to construct finite polyhedral representations and particle-based representations for beliefs, we implement our approach and illustrate its practical applicability to the analysis of pedestrian-vehicle and pursuit-evasion scenarios.

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