Generative Personas That Behave and Experience Like Humans

• URL: http://arxiv.org/abs/2209.00459v1
• Date: Fri, 26 Aug 2022 12:04:53 GMT
• Title: Generative Personas That Behave and Experience Like Humans
• Authors: Matthew Barthet, Ahmed Khalifa, Antonios Liapis and Georgios N. Yannakakis
• Abstract summary: generative AI agents attempt to imitate particular playing behaviors represented as rules, rewards, or human demonstrations. We extend the notion of behavioral procedural personas to cater for player experience, thus examining generative agents that can both behave and experience their game as humans would. Our findings suggest that the generated agents exhibit distinctive play styles and experience responses of the human personas they were designed to imitate.
• Score: 3.611888922173257
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Using artificial intelligence (AI) to automatically test a game remains a critical challenge for the development of richer and more complex game worlds and for the advancement of AI at large. One of the most promising methods for achieving that long-standing goal is the use of generative AI agents, namely procedural personas, that attempt to imitate particular playing behaviors which are represented as rules, rewards, or human demonstrations. All research efforts for building those generative agents, however, have focused solely on playing behavior which is arguably a narrow perspective of what a player actually does in a game. Motivated by this gap in the existing state of the art, in this paper we extend the notion of behavioral procedural personas to cater for player experience, thus examining generative agents that can both behave and experience their game as humans would. For that purpose, we employ the Go-Explore reinforcement learning paradigm for training human-like procedural personas, and we test our method on behavior and experience demonstrations of more than 100 players of a racing game. Our findings suggest that the generated agents exhibit distinctive play styles and experience responses of the human personas they were designed to imitate. Importantly, it also appears that experience, which is tied to playing behavior, can be a highly informative driver for better behavioral exploration.

Related papers

• Reinforcement Learning for High-Level Strategic Control in Tower Defense Games [47.618236610219554]
  In strategy games, one of the most important aspects of game design is maintaining a sense of challenge for players. We propose an automated approach that combines traditional scripted methods with reinforcement learning. Results show that combining a learned approach, such as reinforcement learning, with a scripted AI produces a higher-performing and more robust agent than using only AI.
  arXiv  Detail & Related papers  (2024-06-12T08:06:31Z)
• Toward Human-AI Alignment in Large-Scale Multi-Player Games [24.784173202415687]
  We analyze extensive human gameplay data from Xbox's Bleeding Edge (100K+ games) We find that while human players exhibit variability in fight-flight and explore-exploit behavior, AI players tend towards uniformity. These stark differences underscore the need for interpretable evaluation, design, and integration of AI in human-aligned applications.
  arXiv  Detail & Related papers  (2024-02-05T22:55:33Z)
• Enhancing Human Experience in Human-Agent Collaboration: A Human-Centered Modeling Approach Based on Positive Human Gain [18.968232976619912]
  We propose a "human-centered" modeling scheme for collaborative AI agents. We expect that agents should learn to enhance the extent to which humans achieve these goals while maintaining agents' original abilities. We evaluate the RLHG agent in the popular Multi-player Online Battle Arena (MOBA) game, Honor of Kings.
  arXiv  Detail & Related papers  (2024-01-28T05:05:57Z)
• Behavioural Cloning in VizDoom [1.4999444543328293]
  This paper describes methods for training autonomous agents to play the game "Doom 2" through Imitation Learning (IL) We also explore how Reinforcement Learning (RL) compares to IL for humanness by comparing camera movement and trajectory data.
  arXiv  Detail & Related papers  (2024-01-08T16:15:43Z)
• 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)
• DanZero: Mastering GuanDan Game with Reinforcement Learning [121.93690719186412]
  Card game AI has always been a hot topic in the research of artificial intelligence. In this paper, we are devoted to developing an AI program for a more complex card game, GuanDan. We propose the first AI program DanZero for GuanDan using reinforcement learning technique.
  arXiv  Detail & Related papers  (2022-10-31T06:29:08Z)
• Human-Like Navigation Behavior: A Statistical Evaluation Framework [0.0]
  We build a non-parametric two-sample hypothesis test designed to compare the behaviors of artificial agents to those of human players. We show that the resulting $p$-value not only aligns with anonymous human judgment of human-like behavior, but also that it can be used as a measure of similarity.
  arXiv  Detail & Related papers  (2022-03-10T01:07:34Z)
• AI in (and for) Games [0.9920773256693857]
  This chapter outlines the relation between artificial intelligence (AI) / machine learning (ML) algorithms and digital games. On one hand, AI/ML researchers can generate large, in-the-wild datasets of human affective activity, player behaviour. On the other hand, games can utilise intelligent algorithms to automate testing of game levels, generate content, develop intelligent and responsive non-player characters (NPCs) or predict and respond player behaviour.
  arXiv  Detail & Related papers  (2021-05-07T08:57:07Z)
• Policy Fusion for Adaptive and Customizable Reinforcement Learning Agents [137.86426963572214]
  We show how to combine distinct behavioral policies to obtain a meaningful "fusion" policy. We propose four different policy fusion methods for combining pre-trained policies. We provide several practical examples and use-cases for how these methods are indeed useful for video game production and designers.
  arXiv  Detail & Related papers  (2021-04-21T16:08:44Z)
• Teach me to play, gamer! Imitative learning in computer games via linguistic description of complex phenomena and decision tree [55.41644538483948]
  We present a new machine learning model by imitation based on the linguistic description of complex phenomena. The method can be a good alternative to design and implement the behaviour of intelligent agents in video game development.
  arXiv  Detail & Related papers  (2021-01-06T21:14:10Z)
• DeepCrawl: Deep Reinforcement Learning for Turn-based Strategy Games [137.86426963572214]
  We introduce DeepCrawl, a fully-playable Roguelike prototype for iOS and Android in which all agents are controlled by policy networks trained using Deep Reinforcement Learning (DRL) Our aim is to understand whether recent advances in DRL can be used to develop convincing behavioral models for non-player characters in videogames.
  arXiv  Detail & Related papers  (2020-12-03T13:53:29Z)

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.