On games and simulators as a platform for development of artificial
intelligence for command and control
- URL: http://arxiv.org/abs/2110.11305v1
- Date: Thu, 21 Oct 2021 17:39:58 GMT
- Title: On games and simulators as a platform for development of artificial
intelligence for command and control
- Authors: Vinicius G. Goecks, Nicholas Waytowich, Derrik E. Asher, Song Jun
Park, Mark Mittrick, John Richardson, Manuel Vindiola, Anne Logie, Mark
Dennison, Theron Trout, Priya Narayanan, Alexander Kott
- Abstract summary: Games and simulators can be a valuable platform to execute complex multi-agent, multiplayer, imperfect information scenarios.
The success of artificial intelligence algorithms in real-time strategy games such as StarCraft II have also attracted the attention of the military research community.
- Score: 46.33784995107226
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Games and simulators can be a valuable platform to execute complex
multi-agent, multiplayer, imperfect information scenarios with significant
parallels to military applications: multiple participants manage resources and
make decisions that command assets to secure specific areas of a map or
neutralize opposing forces. These characteristics have attracted the artificial
intelligence (AI) community by supporting development of algorithms with
complex benchmarks and the capability to rapidly iterate over new ideas. The
success of artificial intelligence algorithms in real-time strategy games such
as StarCraft II have also attracted the attention of the military research
community aiming to explore similar techniques in military counterpart
scenarios. Aiming to bridge the connection between games and military
applications, this work discusses past and current efforts on how games and
simulators, together with the artificial intelligence algorithms, have been
adapted to simulate certain aspects of military missions and how they might
impact the future battlefield. This paper also investigates how advances in
virtual reality and visual augmentation systems open new possibilities in human
interfaces with gaming platforms and their military parallels.
Related papers
- BattleAgent: Multi-modal Dynamic Emulation on Historical Battles to Complement Historical Analysis [62.60458710368311]
This paper presents BattleAgent, an emulation system that combines the Large Vision-Language Model and Multi-agent System.
It aims to simulate complex dynamic interactions among multiple agents, as well as between agents and their environments.
It emulates both the decision-making processes of leaders and the viewpoints of ordinary participants, such as soldiers.
arXiv Detail & Related papers (2024-04-23T21:37:22Z) - Scaling Intelligent Agents in Combat Simulations for Wargaming [0.0]
Deep reinforcement learning (RL) continues to show promising results in intelligent agent behavior development in games.
Our research is investigating and extending the use of HRL to create intelligent agents capable of performing effectively in large and complex simulation environments.
Our ultimate goal is to develop an agent capable of superhuman performance that could then serve as an AI advisor to military planners and decision-makers.
arXiv Detail & Related papers (2024-02-08T21:57:10Z) - DanZero+: Dominating the GuanDan Game through Reinforcement Learning [95.90682269990705]
We develop an AI program for an exceptionally complex and popular card game called GuanDan.
We first put forward an AI program named DanZero for this game.
In order to further enhance the AI's capabilities, we apply policy-based reinforcement learning algorithm to GuanDan.
arXiv Detail & Related papers (2023-12-05T08:07:32Z) - Games for Artificial Intelligence Research: A Review and Perspectives [4.44336371847479]
This paper reviews the games and game-based platforms for artificial intelligence research.
It provides guidance on matching particular types of artificial intelligence with suitable games for testing and matching particular needs in games with suitable artificial intelligence techniques.
arXiv Detail & Related papers (2023-04-26T03:42:31Z) - A Deep Reinforcement Learning Strategy for UAV Autonomous Landing on a
Platform [0.0]
We proposed a reinforcement learning framework based on Gazebo that is a kind of physical simulation platform (ROS-RL)
We used three continuous action space reinforcement learning algorithms in the framework to dealing with the problem of autonomous landing of drones.
arXiv Detail & Related papers (2022-09-07T06:33:57Z) - Artificial Intelligence for the Metaverse: A Survey [66.57225253532748]
We first deliver a preliminary of AI, including machine learning algorithms and deep learning architectures, and its role in the metaverse.
We then convey a comprehensive investigation of AI-based methods concerning six technical aspects that have potentials for the metaverse.
Several AI-aided applications, such as healthcare, manufacturing, smart cities, and gaming, are studied to be deployed in the virtual worlds.
arXiv Detail & Related papers (2022-02-15T03:34:56Z) - Mimicking Playstyle by Adapting Parameterized Behavior Trees in RTS
Games [0.0]
Behavior Trees (BTs) impacted the field of Artificial Intelligence (AI) in games.
BTs forced complexity of handcrafted BTs to became barely-tractable and error-prone.
Recent trends in the field focused on automatic creation of AI-agents.
We present a novel approach to semi-automatic construction of AI-agents, that mimic and generalize given human gameplays.
arXiv Detail & Related papers (2021-11-23T20:36:28Z) - From Motor Control to Team Play in Simulated Humanoid Football [56.86144022071756]
We train teams of physically simulated humanoid avatars to play football in a realistic virtual environment.
In a sequence of stages, players first learn to control a fully articulated body to perform realistic, human-like movements.
They then acquire mid-level football skills such as dribbling and shooting.
Finally, they develop awareness of others and play as a team, bridging the gap between low-level motor control at a timescale of milliseconds.
arXiv Detail & Related papers (2021-05-25T20:17:10Z) - Neural MMO v1.3: A Massively Multiagent Game Environment for Training
and Evaluating Neural Networks [48.5733173329785]
We present Neural MMO, a massively multiagent game environment inspired by MMOs.
We discuss our progress on two more general challenges in multiagent systems engineering for AI research: distributed infrastructure and game IO.
arXiv Detail & Related papers (2020-01-31T18:50:02Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.