Navigates Like Me: Understanding How People Evaluate Human-Like AI in Video Games
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Abstract (arXiv)
We aim to understand how people assess human likeness in navigation produced by people and artificially intelligent (AI) agents in a video game. To this end, we propose a novel AI agent with the goal of generating more human-like behavior. We collect hundreds of crowd-sourced assessments comparing the human-likeness of navigation behavior generated by our agent and baseline AI agents with human-generated behavior. Our proposed agent passes a Turing Test, while the baseline agents do not. By passing a Turing Test, we mean that human judges could not quantitatively distinguish between videos of a person and an AI agent navigating. To understand what people believe constitutes human-like navigation, we extensively analyze the justifications of these assessments. This work provides insights into the characteristics that people consider human-like in the context of goal-directed video game navigation, which is a key step for further improving human interactions with AI agents.
Key facts. Cited by 11 papers in the map (data through 14 Oct 2026). Builds on 23 of its 98 references that are in the map. First posted on arXiv 2 Mar 2023.
Builds on
23 of 98 references.
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11 more
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Citing papers per year: 2023: 2; 2024: 3; 2025: 5; 2026: 1.
11 papers.
- 2024Will GPT-4 Run DOOM?cited by 6
- 2023Towards Human-Like RL: Taming Non-Naturalistic Behavior in Deep RL via Adaptive Behavioral Costs in 3D Gamescited by 4
- 2024Reinforcement Learning for High-Level Strategic Control in Tower Defense Gamescited by 3
- 2025Learning Human-Like RL Agents Through Trajectory Optimization With Action Quantizationcited by 3
- 2023Learning to Terminate in Object Navigationcited by 1
- 2024Human-like Bots for Tactical Shooters Using Compute-Efficient Sensorscited by 1
- 2025Path Generation and Evaluation in Video Games: A Nonparametric Statistical Approachcited by 1
- 2025CogniPlay: a work-in-progress Human-like model for General Game Playingcited by 1
- 2025Eval-PPO: Building an Efficient Threat Evaluator Using Proximal Policy Optimizationcited by 0
- 2025Combining Reinforcement Learning and Behavior Trees for NPCs in Video Games with AMD Scholacited by 0
- 2026Enhancing Human-Likeness in Reinforcement Learning Agents via Hierarchical Macro Action Quantizationcited by 0
- 2026Enhancing Human-Likeness in Reinforcement Learning Agents via Hierarchical Macro Action Quantization29 May 2026
- 2025Learning Human-Like RL Agents Through Trajectory Optimization With Action Quantization19 Nov 2025
- 2025Combining Reinforcement Learning and Behavior Trees for NPCs in Video Games with AMD Schola15 Oct 2025
- 2025CogniPlay: a work-in-progress Human-like model for General Game Playing8 Jul 2025
- 2025Path Generation and Evaluation in Video Games: A Nonparametric Statistical Approach4 Jun 2025
- 2025Eval-PPO: Building an Efficient Threat Evaluator Using Proximal Policy Optimization15 Mar 2025
- 2024Human-like Bots for Tactical Shooters Using Compute-Efficient Sensors30 Dec 2024
- 2024Reinforcement Learning for High-Level Strategic Control in Tower Defense Games12 Jun 2024
- 2024Will GPT-4 Run DOOM?8 Mar 2024
- 2023Learning to Terminate in Object Navigation28 Sep 2023
- 2023Towards Human-Like RL: Taming Non-Naturalistic Behavior in Deep RL via Adaptive Behavioral Costs in 3D Games27 Sep 2023