Name
Title | ||
|---|---|---|
Combining Chat and Task-Based Multimodal Dialogue for More Engaging HRI: A Scalable Method Using Reinforcement Learning. |
Abstract | ||
|---|---|---|
We develop the first system to combine task-based and chatbot-style dialogue in a multimodal system for Human-Robot Interaction. We show that Reinforcement Learning is beneficial for training dialogue management (DM) in such systems -- providing a scalable method for training from data and/or simulated users. We first train in simulation, and evaluate the benefits of a combined chat/task policy over systems which can only perform chat or task-based conversation. In a real user evaluation, we then show that a trained combined chat/task multimodal dialogue policy results in longer dialogue interactions than a rule-based approach, suggesting that the learned dialogue policy provides a more engaging mixture of chat and task interaction than a rule-based DM method. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1145/3029798.3034820 | HRI (Companion) |
Field | DocType | ISSN |
Dialogue management,Conversation,Simulation,Computer science,Human–computer interaction,Multimedia,Human–robot interaction,Scalability,Reinforcement learning | Conference | 2167-2121 |
Citations | PageRank | References |
5 | 0.44 | 4 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ioannis Papaioannou | 1 | 5 | 0.44 |
| Oliver Lemon | 2 | 1072 | 86.38 |