Efficient Customer Service Combining Human Operators and Virtual Agents
- URL: http://arxiv.org/abs/2209.05226v1
- Date: Mon, 12 Sep 2022 13:23:42 GMT
- Title: Efficient Customer Service Combining Human Operators and Virtual Agents
- Authors: Yaniv Oshrat, Yonatan Aumann, Tal Hollander, Oleg Maksimov, Anita
Ostroumov, Natali Shechtman, Sarit Kraus
- Abstract summary: We show that it is possible to decrease the cost and efforts of building and maintaining such virtual agents by enabling the virtual agent to incrementally learn from the human operators.
We employ queuing theory to identify the key parameters that govern the behavior and efficiency of such hybrid systems.
- Score: 18.073808397410257
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: The prospect of combining human operators and virtual agents (bots) into an
effective hybrid system that provides proper customer service to clients is
promising yet challenging. The hybrid system decreases the customers'
frustration when bots are unable to provide appropriate service and increases
their satisfaction when they prefer to interact with human operators.
Furthermore, we show that it is possible to decrease the cost and efforts of
building and maintaining such virtual agents by enabling the virtual agent to
incrementally learn from the human operators. We employ queuing theory to
identify the key parameters that govern the behavior and efficiency of such
hybrid systems and determine the main parameters that should be optimized in
order to improve the service. We formally prove, and demonstrate in extensive
simulations and in a user study, that with the proper choice of parameters,
such hybrid systems are able to increase the number of served clients while
simultaneously decreasing their expected waiting time and increasing
satisfaction.
Related papers
- CRMArena: Understanding the Capacity of LLM Agents to Perform Professional CRM Tasks in Realistic Environments [90.29937153770835]
We introduce CRMArena, a benchmark designed to evaluate AI agents on realistic tasks grounded in professional work environments.
We show that state-of-the-art LLM agents succeed in less than 40% of the tasks with ReAct prompting, and less than 55% even with function-calling abilities.
Our findings highlight the need for enhanced agent capabilities in function-calling and rule-following to be deployed in real-world work environments.
arXiv Detail & Related papers (2024-11-04T17:30:51Z) - Interactive Speculative Planning: Enhance Agent Efficiency through Co-design of System and User Interface [38.76937539085164]
This paper presents a human-centered efficient agent planning method -- Interactive Speculative Planning.
We aim at enhancing the efficiency of agent planning through both system design and human-AI interaction.
arXiv Detail & Related papers (2024-09-30T16:52:51Z) - Human-Agent Joint Learning for Efficient Robot Manipulation Skill Acquisition [48.65867987106428]
We introduce a novel system for joint learning between human operators and robots.
It enables human operators to share control of a robot end-effector with a learned assistive agent.
It reduces the need for human adaptation while ensuring the collected data is of sufficient quality for downstream tasks.
arXiv Detail & Related papers (2024-06-29T03:37:29Z) - AutoPal: Autonomous Adaptation to Users for Personal AI Companionship [39.03695909247373]
This paper emphasizes the necessity of autonomous adaptation in personal AI companionship.
We devise a hierarchical framework, AutoPal, that enables controllable and authentic adjustments to the agent's persona.
Experiments demonstrate the effectiveness of AutoPal and highlight the importance of autonomous adaptability in AI companionship.
arXiv Detail & Related papers (2024-06-20T03:02:38Z) - Predicting the Intention to Interact with a Service Robot:the Role of Gaze Cues [51.58558750517068]
Service robots need to perceive as early as possible that an approaching person intends to interact.
We solve this perception task with a sequence-to-sequence classifier of a potential user intention to interact.
Our main contribution is a study of the benefit of features representing the person's gaze in this context.
arXiv Detail & Related papers (2024-04-02T14:22:54Z) - Mixed-Initiative Human-Robot Teaming under Suboptimality with Online Bayesian Adaptation [0.6591036379613505]
We develop computational modeling and optimization techniques for enhancing the performance of suboptimal human-agent teams.
We adopt an online Bayesian approach that enables a robot to infer people's willingness to comply with its assistance in a sequential decision-making game.
Our user studies show that user preferences and team performance indeed vary with robot intervention styles.
arXiv Detail & Related papers (2024-03-24T14:38:18Z) - AgentCF: Collaborative Learning with Autonomous Language Agents for
Recommender Systems [112.76941157194544]
We propose AgentCF for simulating user-item interactions in recommender systems through agent-based collaborative filtering.
We creatively consider not only users but also items as agents, and develop a collaborative learning approach that optimize both kinds of agents together.
Overall, the optimized agents exhibit diverse interaction behaviors within our framework, including user-item, user-user, item-item, and collective interactions.
arXiv Detail & Related papers (2023-10-13T16:37:14Z) - Multi-Agent Deep Reinforcement Learning for Dynamic Avatar Migration in
AIoT-enabled Vehicular Metaverses with Trajectory Prediction [70.9337170201739]
We propose a model to predict the future trajectories of intelligent vehicles based on their historical data.
We show that our proposed algorithm can effectively reduce the latency of executing avatar tasks by around 25% without prediction.
arXiv Detail & Related papers (2023-06-26T13:27:11Z) - Watch-And-Help: A Challenge for Social Perception and Human-AI
Collaboration [116.28433607265573]
We introduce Watch-And-Help (WAH), a challenge for testing social intelligence in AI agents.
In WAH, an AI agent needs to help a human-like agent perform a complex household task efficiently.
We build VirtualHome-Social, a multi-agent household environment, and provide a benchmark including both planning and learning based baselines.
arXiv Detail & Related papers (2020-10-19T21:48:31Z) - A Unified Conversational Assistant Framework for Business Process
Automation [9.818380332602622]
Business process automation is a booming multi-billion-dollar industry that promises to remove menial tasks from workers' plates.
A simple and user-friendly interface with a low learning curve is necessary to increase the adoption of such agents in banking, insurance, retail and other domains.
We present a multi-agent orchestration framework to develop such proactive chatbots by discussing the types of skills that can be composed into agents and how to orchestrate these agents.
arXiv Detail & Related papers (2020-01-07T22:30:05Z)
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.