Multipurpose Intelligent Process Automation via Conversational Assistant

• URL: http://arxiv.org/abs/2001.02284v2
• Date: Thu, 21 May 2020 12:10:49 GMT
• Title: Multipurpose Intelligent Process Automation via Conversational Assistant
• Authors: Alena Moiseeva, Dietrich Trautmann, Michael Heimann, Hinrich Sch\"utze
• Abstract summary: Intelligent Process Automation (IPA) is an emerging technology with a primary goal to assist the knowledge worker by taking care of repetitive, routine and low-cognitive tasks. We tackle a challenge of implementing an IPA conversational assistant in a real-world industrial setting with a lack of structured training data. Our proposed system brings two significant benefits: First, it reduces repetitive and time-consuming activities and, therefore, allows workers to focus on more intelligent processes.
• Score: 3.808063547958558
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Intelligent Process Automation (IPA) is an emerging technology with a primary goal to assist the knowledge worker by taking care of repetitive, routine and low-cognitive tasks. Conversational agents that can interact with users in a natural language are potential application for IPA systems. Such intelligent agents can assist the user by answering specific questions and executing routine tasks that are ordinarily performed in a natural language (i.e., customer support). In this work, we tackle a challenge of implementing an IPA conversational assistant in a real-world industrial setting with a lack of structured training data. Our proposed system brings two significant benefits: First, it reduces repetitive and time-consuming activities and, therefore, allows workers to focus on more intelligent processes. Second, by interacting with users, it augments the resources with structured and to some extent labeled training data. We showcase the usage of the latter by re-implementing several components of our system with Transfer Learning (TL) methods.

Related papers

• Memento No More: Coaching AI Agents to Master Multiple Tasks via Hints Internalization [56.674356045200696]
  We propose a novel method to train AI agents to incorporate knowledge and skills for multiple tasks without the need for cumbersome note systems or prior high-quality demonstration data. Our approach employs an iterative process where the agent collects new experiences, receives corrective feedback from humans in the form of hints, and integrates this feedback into its weights. We demonstrate the efficacy of our approach by implementing it in a Llama-3-based agent which, after only a few rounds of feedback, outperforms advanced models GPT-4o and DeepSeek-V3 in a taskset.
  arXiv  Detail & Related papers  (2025-02-03T17:45:46Z)
• InsTALL: Context-aware Instructional Task Assistance with Multi-modal Large Language Models [11.913271486031201]
  We develop a Context-aware instructional task assistant with multi-modal large language models (InsTALL) InsTALL responds in real-time to user queries related to the task at hand. We show InsTALL achieves state-of-the-art performance across proposed sub-tasks considered for multimodal activity understanding.
  arXiv  Detail & Related papers  (2025-01-21T15:55:06Z)
• From Words to Workflows: Automating Business Processes [0.2796197251957245]
  The limitations of Robotic Process Automation (RPA) have become apparent. Recent advancements in Artificial Intelligence (AI) have paved the way for Intelligent Automation (IA) This paper introduces Text2Workflow, a novel method that automatically generates from natural language user requests.
  arXiv  Detail & Related papers  (2024-12-04T16:34:35Z)
• Interpretable Robotic Manipulation from Language [11.207620790833271]
  We introduce an explainable behavior cloning agent, named Ex-PERACT, specifically designed for manipulation tasks. At the top level, the model is tasked with learning a discrete skill code, while at the bottom level, the policy network translates the problem into a voxelized grid and maps the discretized actions to voxel grids. We evaluate our method across eight challenging manipulation tasks utilizing the RLBench benchmark, demonstrating that Ex-PERACT not only achieves competitive policy performance but also effectively bridges the gap between human instructions and machine execution in complex environments.
  arXiv  Detail & Related papers  (2024-05-27T11:02:21Z)
• The Foundations of Computational Management: A Systematic Approach to Task Automation for the Integration of Artificial Intelligence into Existing Workflows [55.2480439325792]
  This article introduces Computational Management, a systematic approach to task automation. The article offers three easy step-by-step procedures to begin the process of implementing AI within a workflow.
  arXiv  Detail & Related papers  (2024-02-07T01:45:14Z)
• Empowering Private Tutoring by Chaining Large Language Models [87.76985829144834]
  This work explores the development of a full-fledged intelligent tutoring system powered by state-of-the-art large language models (LLMs) The system is into three inter-connected core processes-interaction, reflection, and reaction. Each process is implemented by chaining LLM-powered tools along with dynamically updated memory modules.
  arXiv  Detail & Related papers  (2023-09-15T02:42:03Z)
• Collecting Interactive Multi-modal Datasets for Grounded Language Understanding [66.30648042100123]
  We formalized the collaborative embodied agent using natural language task. We developed a tool for extensive and scalable data collection. We collected the first dataset for interactive grounded language understanding.
  arXiv  Detail & Related papers  (2022-11-12T02:36:32Z)
• Asking for Knowledge: Training RL Agents to Query External Knowledge Using Language [121.56329458876655]
  We introduce two new environments: the grid-world-based Q-BabyAI and the text-based Q-TextWorld. We propose the "Asking for Knowledge" (AFK) agent, which learns to generate language commands to query for meaningful knowledge.
  arXiv  Detail & Related papers  (2022-05-12T14:20:31Z)
• KETOD: Knowledge-Enriched Task-Oriented Dialogue [77.59814785157877]
  Existing studies in dialogue system research mostly treat task-oriented dialogue and chit-chat as separate domains. We investigate how task-oriented dialogue and knowledge-grounded chit-chat can be effectively integrated into a single model.
  arXiv  Detail & Related papers  (2022-05-11T16:01:03Z)

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.