EDBT 2026 Demo / reviewers in the wild / expert
Zijian Peng
dblp:316/8604
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2025
0009-0005-6971-0064ORCID · reported
Domains — the database's venue-derived domains; a paper can count in several
Human-computer interaction and ubiquitous computing · 1 · 1 since 2021
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Human-computer interaction and pervasive computing
1 paper |
Human-AI interaction · 67% Interaction techniques and input · 33% |
Topics — the 2 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Human-AI interaction › automation
GUI task automation |
0.9 | 1 | 2025 | Prompt2Task: Automating UI Tasks on Smartphones from Textual Prompts · ACM Trans. Comput. Hum. Interact. 2025 |
Interaction techniques and input
mobile interaction |
0.9 | 1 | 2025 | Prompt2Task: Automating UI Tasks on Smartphones from Textual Prompts · ACM Trans. Comput. Hum. Interact. 2025 |
Methods — techniques the papers use, named apart from their topics
large language model · 0.9intelligent agent · 0.9
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2025 | Prompt2Task: Automating UI Tasks on Smartphones from Textual PromptsabstractUI task automation enables efficient task execution by simulating human interactions with GUIs, without modifying the existing application code. However, its broader adoption is constrained by the need for expertise in both scripting languages and workflow design. To address this challenge, we present Prompt2Task, a system designed to comprehend various task-related textual prompts (e.g., goals, procedures), thereby generating and performing the corresponding automation tasks. Prompt2Task incorporates a suite of intelligent agents that mimic human cognitive functions, specializing in interpreting user intent, managing external information for task generation, and executing operations on smartphones. The agents can learn from user feedback and continuously improve their performance based on the accumulated knowledge. Experimental results indicated a performance jump from a 22.28% success rate in the baseline to 95.24% with Prompt2Task, requiring an average of 0.69 user interventions for each new task. Prompt2Task presents promising applications in fields such as tutorial creation, smart assistance, and customer service. Tian Huang, Chun Yu, Weinan Shi, Zijian Peng, David Yang 0002, Yuanchun Shi |
ACM Trans. Comput. Hum. Interact. | 4 |