HeapCraft Social Tools: Understanding and Improving Player Collaboration in Minecraft

We introduce a framework to infuence and analyze player collaboration in Minecraft. The framework consists of a telemetry system and several tools to influence player behavior and provide value to server administrators to increase adoption. The data collection includes almost every aspect of gameplay and can be used for analysis beyond player collaboration.1 We started collecting data from several Minecraft servers in March 2015. Most data will be made available to researchers upon request.2 We have also demonstrated the use of our framework to statistically analyze player behavior in Minecraft. More details can be found [1]

A Deep Hierarchical Approach to Lifelong Learning in Minecraft

We propose a lifelong learning system that has the ability to reuse and transfer knowledge from one task to another while efficiently retaining the previously learned knowledge-base. Knowledge is transferred by learning reusable skills to solve tasks in Minecraft, a popular video game which is an unsolved and high-dimensional lifelong learning problem. These reusable skills, which we refer to as Deep Skill Networks, are then incorporated into our novel Hierarchical Deep Reinforcement Learning Network (H-DRLN) architecture using two techniques: (1) a deep skill array and (2) skill distillation, our novel variation of policy distillation (Rusu et. al. 2015) for learning skills. Skill distillation enables the HDRLN to efficiently retain knowledge and therefore scale in lifelong learning, by accumulating knowledge and encapsulating multiple reusable skills into a single distilled network. The H-DRLN exhibits superior performance and lower learning sample complexity compared to the regular Deep Q Network (Mnih et. al. 2015) in sub-domains of Minecraft

Generative Design in Minecraft (GDMC), Settlement Generation Competition

This paper introduces the settlement generation competition for Minecraft, the first part of the Generative Design in Minecraft challenge. The settlement generation competition is about creating Artificial Intelligence (AI) agents that can produce functional, aesthetically appealing and believable settlements adapted to a given Minecraft map - ideally at a level that can compete with human created designs. The aim of the competition is to advance procedural content generation for games, especially in overcoming the challenges of adaptive and holistic PCG. The paper introduces the technical details of the challenge, but mostly focuses on what challenges this competition provides and why they are scientifically relevant.Comment: 10 pages, 5 figures, Part of the Foundations of Digital Games 2018 proceedings, as part of the workshop on Procedural Content Generatio