Alpha-Beta Pruning for Games with Simultaneous Moves

Heuristic search has been very successful in abstract game domains such as Chess and Go. In video games, however, adoption has been slow due to the fact that state and move spaces are much larger, real-time constraints are harsher, and constraints on computational resources are tighter. In this paper we present a fast search method — Alpha-Beta search for durative moves— that can defeat commonly used AI scripts in RTS game combat scenarios of up to 8 vs. 8 units running on a single core in under 5ms per search episode. This performance is achieved by using standard search enhancements such as transposition tables and iterative deepening, and novel usage of combat AI scripts for sorting moves and state evaluation via playouts. We also present evidence that commonly used combat scripts are highly exploitable — opening the door for a promising line of research on opponent combat modelling.

Section: Solution Concepts For Combat Gamesmentioning

confidence: 99%

Fast Heuristic Search for RTS Game Combat Scenarios

Churchill

Saffidine

Buro

2021

“…While some of these problems have been addressed, like durative actions (Churchill, Saffidine, and Buro 2012) or simultaneous moves (Kovarsky and Buro 2005;Saffidine, Finnsson, and Buro 2012), the branching factor in RTS games is too large for current state-of-the-art techniques. To see why, we should distinguish what we call unitactions (actions that a unit executes) from player-actions.…”

Section: Real-time Strategy Gamesmentioning

confidence: 99%

“…Additionally, RTS games are simultaneous-action domains, where more than one player can issue actions at the same instant of time. Algorithms like minimax might result in under or overestimating the value of positions, and several solutions have been proposed (Kovarsky and Buro 2005;Saffidine, Finnsson, and Buro 2012). However, we noticed that this had a very small effect on the practical performance of our algorithm in RTS games, so we have not incorporated any of these techniques into NaïveMCTS.…”

Section: Naïve Monte Carlo Tree Search In Rts Gamesmentioning

confidence: 99%

The Combinatorial Multi-Armed Bandit Problem and Its Application to Real-Time Strategy Games

Ontañón

2021

Game tree search in games with large branching factors is a notoriously hard problem. In this paper, we address this problem with a new sampling strategy for Monte Carlo Tree Search (MCTS) algorithms, called "Naive Sampling", based on a variant of the Multi-armed Bandit problem called the "Combinatorial Multi-armed Bandit" (CMAB) problem. We present a new MCTS algorithm based on Naive Sampling called NaiveMCTS, and evaluate it in the context of real-time strategy (RTS) games. Our results show that as the branching factor grows, NaiveMCTS performs significantly better than other algorithms.

“…To make game-tree search applicable at this level, they perform an abstraction of the game state representation grouping the units in groups but keeping information of each individual unit at the same time, and allowing only two types of actions per group: attack and merge with another group. Alpha-beta has been used in scenarios with simultaneous moves (Saffidine, Finnsson, and Buro 2012) and Churchill et al (Churchill, Saffidine, and Buro 2012) extended it with durative actions, being able to handle situations with up to eight versus eight units without using abstraction. An improvement of this work is presented in (Churchill and Buro 2013), where they defined scripts to improve the move-ordering; and experiments with UCT considering durations and a Portfolio Greedy Search; showing good results in larger combat scenarios than before.…”

Section: Introductionmentioning

confidence: 99%

Game-Tree Search over High-Level Game States in RTS Games

Uriarte

Ontañón

2021

From an AI point of view, Real-Time Strategy (RTS) games are hard because they have enormous state spaces, they are real-time and partially observable. In this paper, we present an approach to deploy game-tree search in RTS games by using game state abstraction. We propose a high-level abstract representation of the game state, that significantly reduces the branching factor when used for game-tree search algorithms. Using this high-level representation, we evaluate versions of alpha-beta search and of Monte Carlo Tree Search (MCTS). We present experiments in the context of StarCraft showing promising results in dealing with the large branching factors present in RTS games.