An Appraisal Transition System for Event-driven Emotions in Agent-based Player Experience Testing

• URL: http://arxiv.org/abs/2105.05589v1
• Date: Wed, 12 May 2021 11:09:35 GMT
• Title: An Appraisal Transition System for Event-driven Emotions in Agent-based Player Experience Testing
• Authors: Saba Gholizadeh Ansari, I. S. W. B. Prasetya, Mehdi Dastani, Frank Dignum, Gabriele Keller
• Abstract summary: We propose an automated player experience testing approach by suggesting a formal model of event-based emotions. A working prototype of the model is integrated on top of Aplib, a tactical agent programming library, to create intelligent PX test agents.
• Score: 9.26240699624761
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Player experience (PX) evaluation has become a field of interest in the game industry. Several manual PX techniques have been introduced to assist developers to understand and evaluate the experience of players in computer games. However, automated testing of player experience still needs to be addressed. An automated player experience testing framework would allow designers to evaluate the PX requirements in the early development stages without the necessity of participating human players. In this paper, we propose an automated player experience testing approach by suggesting a formal model of event-based emotions. In particular, we discuss an event-based transition system to formalize relevant emotions using Ortony, Clore, & Collins (OCC) theory of emotions. A working prototype of the model is integrated on top of Aplib, a tactical agent programming library, to create intelligent PX test agents, capable of appraising emotions in a 3D game case study. The results are graphically shown e.g. as heat maps. Emotion visualization of the test agent would ultimately help game designers in creating content that evokes a certain experience in players.

Related papers

• Facial Emotion Recognition in VR Games [2.5382095320488665]
  We use a Convolutional Neural Network (CNN) to train a model to predict emotions in full-face images where the eyes and eyebrows are covered. The model in these images can accurately recognize seven different emotions which are anger, happiness, disgust, fear, impartiality, sadness and surprise. We analyzed the data collected from our experiment to understand which emotions players experience during the gameplay.
  arXiv  Detail & Related papers  (2023-12-12T01:40:14Z)
• Character-LLM: A Trainable Agent for Role-Playing [67.35139167985008]
  Large language models (LLMs) can be used to serve as agents to simulate human behaviors. We introduce Character-LLM that teach LLMs to act as specific people such as Beethoven, Queen Cleopatra, Julius Caesar, etc.
  arXiv  Detail & Related papers  (2023-10-16T07:58:56Z)
• BDD-Based Framework with RL Integration: An approach for videogames automated testing [0.0]
  Testing in video games differs from traditional software development practices. We propose the integration of Behavior Driven Development (BDD) with Reinforcement Learning (RL)
  arXiv  Detail & Related papers  (2023-10-08T20:05:29Z)
• Promptable Game Models: Text-Guided Game Simulation via Masked Diffusion Models [68.85478477006178]
  We present a Promptable Game Model (PGM) for neural video game simulators. It allows a user to play the game by prompting it with high- and low-level action sequences. Most captivatingly, our PGM unlocks the director's mode, where the game is played by specifying goals for the agents in the form of a prompt. Our method significantly outperforms existing neural video game simulators in terms of rendering quality and unlocks applications beyond the capabilities of the current state of the art.
  arXiv  Detail & Related papers  (2023-03-23T17:43:17Z)
• Emotion Twenty Questions Dialog System for Lexical Emotional Intelligence [0.0]
  This paper presents a web-based demonstration of Emotion Twenty Questions (EMO20Q), a dialog game whose purpose is to study how people describe emotions. EMO20Q can also be used to develop artificially intelligent dialog agents that can play the game.
  arXiv  Detail & Related papers  (2022-10-05T17:20:26Z)
• Generative Personas That Behave and Experience Like Humans [3.611888922173257]
  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.
  arXiv  Detail & Related papers  (2022-08-26T12:04:53Z)
• CCPT: Automatic Gameplay Testing and Validation with Curiosity-Conditioned Proximal Trajectories [65.35714948506032]
  The Curiosity-Conditioned Proximal Trajectories (CCPT) method combines curiosity and imitation learning to train agents to explore. We show how CCPT can explore complex environments, discover gameplay issues and design oversights in the process, and recognize and highlight them directly to game designers.
  arXiv  Detail & Related papers  (2022-02-21T09:08:33Z)
• An Empirical Study on the Generalization Power of Neural Representations Learned via Visual Guessing Games [79.23847247132345]
  This work investigates how well an artificial agent can benefit from playing guessing games when later asked to perform on novel NLP downstream tasks such as Visual Question Answering (VQA) We propose two ways to exploit playing guessing games: 1) a supervised learning scenario in which the agent learns to mimic successful guessing games and 2) a novel way for an agent to play by itself, called Self-play via Iterated Experience Learning (SPIEL)
  arXiv  Detail & Related papers  (2021-01-31T10:30:48Z)
• 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)
• Learning to Simulate Dynamic Environments with GameGAN [109.25308647431952]
  In this paper, we aim to learn a simulator by simply watching an agent interact with an environment. We introduce GameGAN, a generative model that learns to visually imitate a desired game by ingesting screenplay and keyboard actions during training.
  arXiv  Detail & Related papers  (2020-05-25T14:10:17Z)
• I Feel I Feel You: A Theory of Mind Experiment in Games [1.857766632829209]
  We focus on the perception of frustration as it is a prevalent affective experience in human-computer interaction. We present a testbed game tailored towards this end, in which a player competes against an agent with a frustration model based on theory. We examine the collected data through correlation analysis and predictive machine learning models, and find that the player's observable emotions are not correlated highly with the perceived frustration of the agent.
  arXiv  Detail & Related papers  (2020-01-23T16:49:39Z)

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.