NPC engine: a High-Performance, Modular, and Domain-Agnostic MCTS Framework for Emergent Narrative
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Date
2022
Publication Type
Other Conference Item
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yes
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Abstract
The quality of emergent narrative is strongly linked to the capabilities of the underlying simulation. A powerful way to drive character activity is to use a performant and dependable planner. In this paper, we present a domain-agnostic multi-agent Monte Carlo tree search planner implemented using the Rust programming language. The planner supports tasks of varying duration and a dynamic number of agents with heterogeneous properties. It also provides optional concurrency, allowing for scalable simulations of many agents planning in parallel on multiple threads. In addition to simulation-based rollout, it supports custom state value estimators and offers a basic adaptive implementation using neural networks. The planner also includes a variety of debugging features, such as the ability to plot the search tree. For easy adoption, we provide several documented application examples.
Publication status
unpublished
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Event
13th Intelligent Narrative Technologies Workshop (at AIIDE 2022)
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Software
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Subject
Organisational unit
08698 - Game Technology Center (GTC)
Notes
Conference lecture on October 25, 2022