Double-oracle sampling method for Stackelberg Equilibrium approximation in general-sum extensive-form games

The paper presents a new method for approximating Strong Stackelberg Equilibrium in general-sum sequential games with imperfect information and perfect recall. The proposed approach is generic as it does not rely on any specific properties of a particular game model. The method is based on iterative interleaving of the two following phases: (1) guided Monte Carlo Tree Search sampling of the Follower's strategy space and (2) building the Leader's behavior strategy tree for which the sampled Follower's strategy is an optimal response. The above solution scheme is evaluated with respect to expected Leader's utility and time requirements on three sets of interception games with variable characteristics, played on graphs. A comparison with three state-of-the-art MILP/LP-based methods shows that in vast majority of test cases proposed simulation-based approach leads to optimal Leader's strategies, while excelling the competitive methods in terms of better time scalability and lower memory requirements

Enhancing the Monte Carlo Tree Search Algorithm for Video Game Testing

In this paper, we study the effects of several Monte Carlo Tree Search (MCTS) modifications for video game testing. Although MCTS modifications are highly studied in game playing, their impacts on finding bugs are blank. We focused on bug finding in our previous study where we introduced synthetic and human-like test goals and we used these test goals in Sarsa and MCTS agents to find bugs. In this study, we extend the MCTS agent with several modifications for game testing purposes. Furthermore, we present a novel tree reuse strategy. We experiment with these modifications by testing them on three testbed games, four levels each, that contain 45 bugs in total. We use the General Video Game Artificial Intelligence (GVG-AI) framework to create the testbed games and collect 427 human tester trajectories using the GVG-AI framework. We analyze the proposed modifications in three parts: we evaluate their effects on bug finding performances of agents, we measure their success under two different computational budgets, and we assess their effects on human-likeness of the human-like agent. Our results show that MCTS modifications improve the bug finding performance of the agents