GameHorizon Suite: Multi-Horizon Data and Evaluation in Gameplay
GameHorizon Suite enhances game AI with multi-horizon data and evaluation, featuring 5,000 hours of data.
Key Findings
Methodology
GameHorizon Suite consists of three components: GameHorizon-Annotator for automated multi-horizon instruction generation, GameHorizon-Data providing a large-scale gameplay dataset, and GameHorizon-Bench for reproducible offline and online testing. It utilizes dynamic programming algorithms and VLM for instruction generation.
Key Results
- Collected 5,000 hours of data from 21 games by 100 expert players, providing 6,184,036 instructions.
- Evaluated 47 models with over one million invocations, revealing task difficulty hierarchy and model capability differences.
- Medium and long-horizon instructions improved future action planning accuracy by 7.2 percentage points.
Significance
This research provides a standardized evaluation tool for game AI, addressing issues of narrow dataset coverage, lack of language instructions, and high variance in online evaluations. It advances game AI capabilities through multi-horizon data and evaluation.
Technical Contribution
Introduced an automated multi-horizon instruction generation process, combining dynamic programming and vision-language models, offering a more comprehensive game AI evaluation tool than existing methods.
Novelty
First to achieve automated multi-horizon instruction generation, providing a unified evaluation standard that surpasses the limitations of single-game or small-scale datasets.
Limitations
- Current models still struggle with complex goal decomposition and future action planning, especially in long-horizon tasks.
- While dataset diversity is improved, it may still not cover all game types.
Future Work
Future work includes expanding the dataset's game variety and scale, enhancing models' planning abilities in long-horizon tasks, and developing new evaluation metrics.
AI Executive Summary
Modern video games provide a measurable testbed for AI models. However, existing datasets and benchmarks suffer from narrow coverage and lack of language instructions. GameHorizon Suite addresses these challenges by introducing multi-horizon data and evaluation. It includes an automated annotation process, GameHorizon-Annotator, a large-scale gameplay dataset, GameHorizon-Data, and a reproducible evaluation benchmark, GameHorizon-Bench. Experimental results show that using multi-horizon instructions significantly improves planning capabilities. This research provides a standardized evaluation tool for game AI, advancing the field. Future work will continue to expand the dataset's scale and variety and enhance models' capabilities in long-horizon tasks.
Deep Analysis
Background
As video games become more complex, game AI has emerged as a crucial research area. Existing datasets like GameWorld and MineDojo cover only a few games, lacking comprehensive language instructions and evaluation tools. GameHorizon Suite provides a more comprehensive game AI evaluation platform through multi-horizon data and evaluation.
Core Problem
Existing game AI datasets have narrow coverage, lack language instructions, and high variance in online evaluations, making it difficult to provide reliable model capability assessments. This limits the development of game AI in complex environments.
Innovation
GameHorizon Suite's innovation lies in its multi-horizon data and evaluation approach. By automating the generation of multi-horizon instructions and combining large-scale datasets with reproducible evaluation benchmarks, it provides a unified evaluation standard for game AI.
Methodology
- �� Use GameHorizon-Annotator to automatically generate multi-horizon instructions. • Generate short, medium, and long-horizon instructions using dynamic programming and VLM. • Construct GameHorizon-Data, covering 5,000 hours of data from 21 games. • Design GameHorizon-Bench for offline and online evaluation.
Experiments
The experimental design includes using GameHorizon-Data for model evaluation, covering various game types. Evaluated 47 models with over one million invocations to test their performance in different tasks.
Results
Experimental results show that multi-horizon instructions significantly improve future action planning accuracy. Models exhibit a clear hierarchy of task difficulty and capability differences.
Applications
GameHorizon Suite can be used to evaluate and enhance game AI capabilities, applicable to various game types and task scenarios. It provides a standardized evaluation tool for game developers and AI researchers.
Limitations & Outlook
Although the dataset's scale and diversity have improved, it may still not cover all game types. Models' planning abilities in long-horizon tasks still need enhancement.
Plain Language Accessible to non-experts
Imagine a kitchen where a chef needs to complete different tasks over different time periods: chopping vegetables in the short term, preparing ingredients in the medium term, and completing the entire dish in the long term. GameHorizon Suite is like the kitchen management system, helping the AI chef plan and execute tasks at different time horizons. Through an automated annotation process, it generates detailed instructions for each task, ensuring the AI chef efficiently completes each stage.
ELI14 Explained like you're 14
Imagine you're playing a complex game like Minecraft. You need to mine in the short term, build a house in the medium term, and defeat enemies in the long term. GameHorizon Suite is like a super helper, helping you plan at different stages. It tells you what to do and when, making you a better player in the game!
Glossary
GameHorizon Suite
A multi-horizon data and evaluation tool for assessing game AI capabilities.
Used for generating and evaluating multi-horizon game data.
VLM (Vision-Language Model)
A model that processes tasks by combining visual and language information.
Used for generating multi-horizon instructions.
AAA Games
High-budget, high-quality games typically developed by large teams.
GameHorizon-Data covers various AAA games.
Multi-Horizon Instructions
Task instructions generated over different time periods, including short, medium, and long horizons.
Used to guide AI in executing tasks in games.
Dynamic Programming Algorithm
An algorithm for solving complex problems by breaking them down into simpler subproblems.
Used for generating time periods for multi-horizon instructions.
Open Questions Unanswered questions from this research
- 1 How to improve models' planning abilities in long-horizon tasks? Current methods perform poorly in complex tasks, requiring new algorithms and evaluation metrics.
- 2 How to further expand the dataset's game variety and scale to cover more game types and task scenarios?
Applications
Immediate Applications
Game Development
Game developers can use GameHorizon Suite to evaluate and enhance AI capabilities, improving game intelligence.
Long-term Vision
AI Education
Promote AI education development through multi-horizon data and evaluation tools, cultivating more talents with game AI capabilities.
Abstract
Modern video games provide a measurable testbed for AI models, combining abilities of visual understanding, instruction decomposition, goal planning, and precise action control over multiple temporal horizons. Existing datasets and benchmarks, however, either cover a narrow range of games, lack language instructions, or rely on high-variance online rollouts. To address these challenges, we introduce GameHorizon, a unified data and evaluation suite that measures gameplay capabilities at different horizons for diverse model families. GameHorizon Suite consists of three components. First, GameHorizon-Annotator is a scalable and automated annotation pipeline for multi-horizon instructions. Second, utilizing the pipeline, we construct GameHorizon-Data, the first large-scale AAA gameplay dataset with temporally aligned videos, player actions, and multi-horizon instructions. It comprises 5,000 hours of recordings from 21 games, collected by 100 human expert players. Third, we build GameHorizon-Bench with reproducible offline and stepwise online testing. The offline track enables reproducible evaluation using thousands of standardized questions organized into three primary tasks and a series of diagnostic variants, while the online track tests whether offline scores reflect actual gameplay capabilities and localizes failures to specific steps within long-horizon gameplay. Based on our GameHorizon Suite, we evaluate 47 models through more than one million model invocations, revealing a meaningful hierarchy of task difficulty and pronounced differences in model capabilities. Our work can provide a standardized yardstick for evaluating gameplay capabilities across horizons and model families. We will release our dataset, annotator, and benchmark to facilitate future research.