Results of multi-agent system and ontology to manage ideas and represent knowledge in a challenge of creativity

• URL: http://arxiv.org/abs/2009.05282v1
• Date: Fri, 11 Sep 2020 08:31:30 GMT
• Title: Results of multi-agent system and ontology to manage ideas and represent knowledge in a challenge of creativity
• Authors: Pedro Barrios, Davy Monticolo (ENSGSI), Sahbi Sidhom (KIWI)
• Abstract summary: This article is about an intelligent system to support ideas management as a result of a multi-agent system used in a distributed system. The intelligent system assists participants of the creativity workshop to manage their ideas and consequently proposing an ontology dedicated to ideas.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This article is about an intelligent system to support ideas management as a result of a multi-agent system used in a distributed system with heterogeneous information as ideas and knowledge, after the results about an ontology to describe the meaning of these ideas. The intelligent system assists participants of the creativity workshop to manage their ideas and consequently proposing an ontology dedicated to ideas. During the creative workshop many creative activities and collaborative creative methods are used by roles immersed in this creativity workshop event where they share knowledge. The collaboration of these roles is physically distant, their interactions might be synchrony or asynchrony, and the information of the ideas are heterogeneous, so we can say that the process is distributed. Those ideas are writing in natural language by participants which have a role and the ideas are heterogeneous since some of them are described by schema, text or scenario of use. This paper presents first, our MAS and second our Ontology design.

Related papers

• A Framework for Collaborating a Large Language Model Tool in Brainstorming for Triggering Creative Thoughts [2.709166684084394]
  This study proposes a framework called GPS, which employs goals, prompts, and strategies to guide designers to systematically work with an LLM tool for improving the creativity of ideas generated during brainstorming. Our framework, tested through a design example and a case study, demonstrates its effectiveness in stimulating creativity and its seamless LLM tool integration into design practices.
  arXiv  Detail & Related papers  (2024-10-10T13:39:27Z)
• Good Idea or Not, Representation of LLM Could Tell [86.36317971482755]
  We focus on idea assessment, which aims to leverage the knowledge of large language models to assess the merit of scientific ideas. We release a benchmark dataset from nearly four thousand manuscript papers with full texts, meticulously designed to train and evaluate the performance of different approaches to this task. Our findings suggest that the representations of large language models hold more potential in quantifying the value of ideas than their generative outputs.
  arXiv  Detail & Related papers  (2024-09-07T02:07:22Z)
• Learning Interpretable Concepts: Unifying Causal Representation Learning and Foundation Models [51.43538150982291]
  We study how to learn human-interpretable concepts from data. Weaving together ideas from both fields, we show that concepts can be provably recovered from diverse data.
  arXiv  Detail & Related papers  (2024-02-14T15:23:59Z)
• Getting aligned on representational alignment [89.81370730647467]
  We study the study of representational alignment in cognitive science, neuroscience, and machine learning. There is limited knowledge transfer between research communities interested in representational alignment. We propose a unifying framework that can serve as a common language between researchers studying representational alignment.
  arXiv  Detail & Related papers  (2023-10-18T17:47:58Z)
• The flow of ideas in word embeddings [0.0]
  Flow of ideas has been extensively studied by physicists, psychologists, and machine learning engineers. This paper adopts specific tools from microrheology to investigate the similarity-based flow of ideas.
  arXiv  Detail & Related papers  (2023-07-26T15:51:31Z)
• Exploring and Verbalizing Academic Ideas by Concept Co-occurrence [42.16213986603552]
  This study devises a framework based on concept co-occurrence for academic idea inspiration. We construct evolving concept graphs according to the co-occurrence relationship of concepts from 20 disciplines or topics. We generate a description of an idea based on a new data structure called co-occurrence citation quintuple.
  arXiv  Detail & Related papers  (2023-06-04T07:01:30Z)
• Understanding Idea Creation in Collaborative Discourse through Networks: The Joint Attention-Interaction-Creation (AIC) Framework [0.42303492200814446]
  The Joint Attention-Interaction-Creation (AIC) framework captures important dynamics in collaborative discourse. The framework was developed from the networked lens, informed by natural language processing techniques, and inspired by socio-semantic network analysis.
  arXiv  Detail & Related papers  (2023-05-25T17:18:19Z)
• Pathway to Future Symbiotic Creativity [76.20798455931603]
  We propose a classification of the creative system with a hierarchy of 5 classes, showing the pathway of creativity evolving from a mimic-human artist to a Machine artist in its own right. In art creation, it is necessary for machines to understand humans' mental states, including desires, appreciation, and emotions, humans also need to understand machines' creative capabilities and limitations. We propose a novel framework for building future Machine artists, which comes with the philosophy that a human-compatible AI system should be based on the "human-in-the-loop" principle.
  arXiv  Detail & Related papers  (2022-08-18T15:12:02Z)
• Rethinking Explainability as a Dialogue: A Practitioner's Perspective [57.87089539718344]
  We ask doctors, healthcare professionals, and policymakers about their needs and desires for explanations. Our study indicates that decision-makers would strongly prefer interactive explanations in the form of natural language dialogues. Considering these needs, we outline a set of five principles researchers should follow when designing interactive explanations.
  arXiv  Detail & Related papers  (2022-02-03T22:17:21Z)
• Generating Music and Generative Art from Brain activity [0.0]
  This research work introduces a computational system for creating generative art using a Brain-Computer Interface (BCI) The generated artwork uses brain signals and concepts of geometry, color and spatial location to give complexity to the autonomous construction.
  arXiv  Detail & Related papers  (2021-08-09T19:33:45Z)
• Explaining Creative Artifacts [69.86890599471202]
  We develop an inverse problem formulation to deconstruct the products of and compositional creativity into associative chains. In particular, our formulation is structured as solving a traveling salesman problem through a knowledge graph of associative elements.
  arXiv  Detail & Related papers  (2020-10-14T14:32:38Z)

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.