ASP-driven User-interaction with Clinguin
- URL: http://arxiv.org/abs/2502.09222v1
- Date: Thu, 13 Feb 2025 11:50:51 GMT
- Title: ASP-driven User-interaction with Clinguin
- Authors: Alexander Beiser, Susana Hahn, Torsten Schaub,
- Abstract summary: clinguin is a system for ASP-driven user interface design.<n>It lets developers build interactive prototypes directly in ASP.<n>Clinguin uses a few dedicated predicates to define user interfaces and the treatment of user-triggered events.
- Score: 45.560812800359685
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: We present clinguin, a system for ASP-driven user interface design. Clinguin streamlines the development of user interfaces for ASP developers by letting them build interactive prototypes directly in ASP, eliminating the need for separate frontend languages. To this end, clinguin uses a few dedicated predicates to define user interfaces and the treatment of user-triggered events. This simple design greatly facilitates the specification of user interactions with an ASP system, in our case clingo.
Related papers
- UserBench: An Interactive Gym Environment for User-Centric Agents [110.77212949007958]
Large Language Models (LLMs)-based agents have made impressive progress in reasoning and tool use, but their ability to proactively collaborate with users remains underexplored.<n>We introduce UserBench, a user-centric benchmark designed to evaluate agents in multi-turn, preference-driven interactions.
arXiv Detail & Related papers (2025-07-29T17:34:12Z) - Smart Expansion Techniques for ASP-based Interactive Configuration [0.0]
We present an ASP-based solver for interactive configuration that can deal with large-scale industrial configuration problems.<n>We also present a user interface that uses our API and is implemented in ASP.
arXiv Detail & Related papers (2025-07-28T17:46:51Z) - Creating General User Models from Computer Use [62.91116265732001]
This paper presents an architecture for a general user model (GUM) that learns about you by observing any interaction you have with your computer.<n>The GUM takes as input any unstructured observation of a user (e.g., device screenshots) and constructs confidence-weighted propositions that capture user knowledge and preferences.
arXiv Detail & Related papers (2025-05-16T04:00:31Z) - Survey of User Interface Design and Interaction Techniques in Generative AI Applications [79.55963742878684]
We aim to create a compendium of different user-interaction patterns that can be used as a reference for designers and developers alike.
We also strive to lower the entry barrier for those attempting to learn more about the design of generative AI applications.
arXiv Detail & Related papers (2024-10-28T23:10:06Z) - Large Language User Interfaces: Voice Interactive User Interfaces powered by LLMs [5.06113628525842]
We present a framework that can serve as an intermediary between a user and their user interface (UI)
We employ a system that stands upon textual semantic mappings of UI components, in the form of annotations.
Our engine can classify the most appropriate application, extract relevant parameters, and subsequently execute precise predictions of the user's expected actions.
arXiv Detail & Related papers (2024-02-07T21:08:49Z) - Interpreting User Requests in the Context of Natural Language Standing
Instructions [89.12540932734476]
We develop NLSI, a language-to-program dataset consisting of over 2.4K dialogues spanning 17 domains.
A key challenge in NLSI is to identify which subset of the standing instructions is applicable to a given dialogue.
arXiv Detail & Related papers (2023-11-16T11:19:26Z) - Multi-User MultiWOZ: Task-Oriented Dialogues among Multiple Users [51.34484827552774]
We release the Multi-User MultiWOZ dataset: task-oriented dialogues among two users and one agent.
These dialogues reflect interesting dynamics of collaborative decision-making in task-oriented scenarios.
We propose a novel task of multi-user contextual query rewriting: to rewrite a task-oriented chat between two users as a concise task-oriented query.
arXiv Detail & Related papers (2023-10-31T14:12:07Z) - User Satisfaction Estimation with Sequential Dialogue Act Modeling in
Goal-oriented Conversational Systems [65.88679683468143]
We propose a novel framework, namely USDA, to incorporate the sequential dynamics of dialogue acts for predicting user satisfaction.
USDA incorporates the sequential transitions of both content and act features in the dialogue to predict the user satisfaction.
Experimental results on four benchmark goal-oriented dialogue datasets show that the proposed method substantially and consistently outperforms existing methods on USE.
arXiv Detail & Related papers (2022-02-07T02:50:07Z) - Learning Implicit User Profiles for Personalized Retrieval-Based Chatbot [29.053654530024083]
IMPChat aims to learn an implicit user profile through modeling user's personalized language style and personalized preferences separately.
To learn a user's personalized language style, we elaborately build language models from shallow to deep using the user's historical responses.
We match each response candidate with the personalized language style and personalized preference, respectively, and fuse the two matching signals to determine the final ranking score.
arXiv Detail & Related papers (2021-08-18T02:07:28Z) - A Cooperative Memory Network for Personalized Task-oriented Dialogue
Systems with Incomplete User Profiles [55.951126447217526]
We study personalized Task-oriented Dialogue Systems without assuming that user profiles are complete.
We propose a Cooperative Memory Network (CoMemNN) that has a novel mechanism to gradually enrich user profiles.
CoMemNN is able to enrich user profiles effectively, which results in an improvement of 3.06% in terms of response selection accuracy.
arXiv Detail & Related papers (2021-02-16T18:05:54Z) - How to build your own ASP-based system?! [4.171595518241986]
This tutorial aims at enabling users to build their own ASP-based systems.
We show how the ASP system CLINGO can be used for extending ASP and for implementing customized special-purpose systems.
arXiv Detail & Related papers (2020-08-15T10:08:50Z)
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.