Using Backward Chained Behavior Trees to Control Cooperative Minecraft Agents

Abstract: This report presents a strategy to control multiple collaborative intelligent agents acting in a complex, versatile environment. The proposed method utilizes back-chained behavior trees and 1-to-1 task distribution. The agents claim a task, which prevents other agents in the system to start working on the same task. Backward chaining is an algorithm for generating reactive agents from a set of goals. The method was evaluated in Minecraft with Microsoft’s Project Malmo API. Two different scenarios were considered. In the first one, a group of agents collaborated to build a structure. In the second one, a group of agents collaborated while gathering material. We propose and evaluate three algorithms with different levels of agent-cooperation and complexity (Algorithm 1, Algorithm 2, and Algorithm 3). The evaluation shows that backward chained Behaviour Trees (BTs) works well for multiagent coordination in complex versatile environments and that adding 1-to-1 task distribution increases the efficiency of the agents when completing the experiment tasks.