# Opportunistic Synthesis in Reactive Games under Information Asymmetry

## TL;DR

This paper introduces a novel opportunistic synthesis approach for reactive control systems under information asymmetry, leveraging hypergames and MDPs to improve outcomes in adversarial, uncertain environments.

## Contribution

It develops a new method combining hypergames and MDPs for reactive synthesis with incomplete information, enabling robots to exploit informational advantages.

## Key findings

- The method guarantees improved outcomes when opportunities arise.
- Demonstrated effectiveness in a robot motion planning example with adversaries.
- Ensures correctness and optimality of the synthesized strategies.

## Abstract

Reactive synthesis is a class of methods to construct a provably-correct control system, referred to as a robot, with respect to a temporal logic specification in the presence of a dynamic and uncontrollable environment. This is achieved by modeling the interaction between the robot and its environment as a two-player zero-sum game. However, existing reactive synthesis methods assume both players to have complete information, which is not the case in many strategic interactions. In this paper, we use a variant of hypergames to model the interaction between the robot and its environment; which has incomplete information about the specification of the robot. This model allows us to identify a subset of game states from where the robot can leverage the asymmetrical information to achieve a better outcome, which is not possible if both players have symmetrical and complete information. We then introduce a novel method of opportunistic synthesis by defining a Markov Decision Process (MDP) using the hypergame under temporal logic specifications. When the environment plays some stochastic strategy in its perceived sure-winning and sure-losing regions of the game, we show that by following the opportunistic strategy, the robot is ensured to only improve the outcome of the game - measured by satisfaction of sub-specifications - whenever an opportunity becomes available. We demonstrate the correctness and optimality of this method using a robot motion planning example in the presence of an adversary.