Learning the Designer's Preferences to Drive Evolution

• URL: http://arxiv.org/abs/2003.03268v1
• Date: Fri, 6 Mar 2020 15:10:09 GMT
• Title: Learning the Designer's Preferences to Drive Evolution
• Authors: Alberto Alvarez and Jose Font
• Abstract summary: We present a data-driven solution that pursues to learn from user generated data in a Quality-Diversity Mixed-Initiative Co-Creativity tool. We aim for increasing the user's agency over the generated content in a way that neither stalls the user-tool reciprocal stimuli loop nor fatigues the user with periodical suggestion handpicking.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper presents the Designer Preference Model, a data-driven solution that pursues to learn from user generated data in a Quality-Diversity Mixed-Initiative Co-Creativity (QD MI-CC) tool, with the aims of modelling the user's design style to better assess the tool's procedurally generated content with respect to that user's preferences. Through this approach, we aim for increasing the user's agency over the generated content in a way that neither stalls the user-tool reciprocal stimuli loop nor fatigues the user with periodical suggestion handpicking. We describe the details of this novel solution, as well as its implementation in the MI-CC tool the Evolutionary Dungeon Designer. We present and discuss our findings out of the initial tests carried out, spotting the open challenges for this combined line of research that integrates MI-CC with Procedural Content Generation through Machine Learning.

Related papers

• GUIDE-VAE: Advancing Data Generation with User Information and Pattern Dictionaries [0.0]
  This paper introduces GUIDE-VAE, a novel conditional generative model that leverages user embeddings to generate user-guided data. The proposed GUIDE-VAE was evaluated on a multi-user smart meter dataset characterized by substantial data imbalance across users.
  arXiv  Detail & Related papers  (2024-11-06T14:11:46Z)
• Towards Realistic Evaluation of Commit Message Generation by Matching Online and Offline Settings [77.20838441870151]
  Commit message generation is a crucial task in software engineering that is challenging to evaluate correctly. We use an online metric - the number of edits users introduce before committing the generated messages to the VCS - to select metrics for offline experiments. Our results indicate that edit distance exhibits the highest correlation, whereas commonly used similarity metrics such as BLEU and METEOR demonstrate low correlation.
  arXiv  Detail & Related papers  (2024-10-15T20:32:07Z)
• A Review of Modern Recommender Systems Using Generative Models (Gen-RecSys) [57.30228361181045]
  This survey connects key advancements in recommender systems using Generative Models (Gen-RecSys) It covers: interaction-driven generative models; the use of large language models (LLM) and textual data for natural language recommendation; and the integration of multimodal models for generating and processing images/videos in RS. Our work highlights necessary paradigms for evaluating the impact and harm of Gen-RecSys and identifies open challenges.
  arXiv  Detail & Related papers  (2024-03-31T06:57:57Z)
• RELIC: Investigating Large Language Model Responses using Self-Consistency [58.63436505595177]
  Large Language Models (LLMs) are notorious for blending fact with fiction and generating non-factual content, known as hallucinations. We propose an interactive system that helps users gain insight into the reliability of the generated text.
  arXiv  Detail & Related papers  (2023-11-28T14:55:52Z)
• Provengo: A Tool Suite for Scenario Driven Model-Based Testing [2.4387555567462647]
  Provengo is a suite of tools designed to facilitate the implementation of Scenario-Driven Model-Based Testing (SDMBT) With Provengo, testers gain the ability to effortlessly create natural user stories and seamlessly integrate them into a model capable of generating effective tests.
  arXiv  Detail & Related papers  (2023-08-30T10:34:12Z)
• MISSRec: Pre-training and Transferring Multi-modal Interest-aware Sequence Representation for Recommendation [61.45986275328629]
  We propose MISSRec, a multi-modal pre-training and transfer learning framework for sequential recommendation. On the user side, we design a Transformer-based encoder-decoder model, where the contextual encoder learns to capture the sequence-level multi-modal user interests. On the candidate item side, we adopt a dynamic fusion module to produce user-adaptive item representation.
  arXiv  Detail & Related papers  (2023-08-22T04:06:56Z)
• Creating user stereotypes for persona development from qualitative data through semi-automatic subspace clustering [0.0]
  We propose a method that employs the modelling of user stereotypes to automate part of the persona creation process. Results show that manual techniques differ between human persona designers leading to different results. The proposed algorithm provides similar results based on parameter input, but was more rigorous and will find optimal clusters.
  arXiv  Detail & Related papers  (2023-06-26T09:49:51Z)
• Evaluating Mixed-Initiative Procedural Level Design Tools using a Triple-Blind Mixed-Method User Study [0.0]
  A tool which generates levels using interactive evolutionary optimisation was designed for this study. The tool identifies level design patterns in an initial hand-designed map and uses that information to drive an interactive optimisation algorithm. A rigorous user study was designed which compared the experiences of designers using the mixed-initiative tool to designers who were given a tool which provided completely random level suggestions.
  arXiv  Detail & Related papers  (2020-05-15T11:40:53Z)
• FAIRS -- Soft Focus Generator and Attention for Robust Object Segmentation from Extreme Points [70.65563691392987]
  We present a new approach to generate object segmentation from user inputs in the form of extreme points and corrective clicks. We demonstrate our method's ability to generate high-quality training data as well as its scalability in incorporating extreme points, guiding clicks, and corrective clicks in a principled manner.
  arXiv  Detail & Related papers  (2020-04-04T22:25:47Z)
• Mining Implicit Entity Preference from User-Item Interaction Data for Knowledge Graph Completion via Adversarial Learning [82.46332224556257]
  We propose a novel adversarial learning approach by leveraging user interaction data for the Knowledge Graph Completion task. Our generator is isolated from user interaction data, and serves to improve the performance of the discriminator. To discover implicit entity preference of users, we design an elaborate collaborative learning algorithms based on graph neural networks.
  arXiv  Detail & Related papers  (2020-03-28T05:47:33Z)
• Designing for the Long Tail of Machine Learning [0.0]
  We describe how machine learning performance scales with training data to guide designers in trade-offs between data gathering, model development and designing valuable interactions for a given model performance. We argue that a useful pattern is to design an initial system in a bootstrap phase that aims to exploit the training effect of data collected at increasing orders of magnitude.
  arXiv  Detail & Related papers  (2020-01-21T11:53:28Z)

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.