scholarly journals Decision Theoretic Dialogue Planning for Initiative Problems

2005 ◽  
pp. 169-173
Author(s):  
Bryan McEleney ◽  
Gregory O’Hare
Keyword(s):  
Dialogue Planning

scholarly journals Dialogue Strategies for Multimedia Retrieval: Intertwining Abductive Reasoning and Dialogue Planning

1995 ◽  
Author(s):  
Ulrich Thiel ◽  
Jon Atle Gulla ◽  
Adrian Muller ◽  
Adelheit Stein
Keyword(s):  
Abductive Reasoning ◽  
Multimedia Retrieval ◽  
Dialogue Planning

Example-based training of dialogue planning incorporating user and situation models

2004 ◽  
Author(s):  
Ian Richard Lane ◽  
Tatsuya Kawahara ◽  
Shinichi Ueno
Keyword(s):  
Situation Models ◽  
Dialogue Planning

A two-layer model for behavior and dialogue planning in conversational service robots

2005 ◽  
Author(s):  
M. Nakano ◽  
Y. Hasegawa ◽  
K. Nakadai ◽  
T. Nakamura ◽  
J. Takeuchi ◽  
...  
Keyword(s):  
Service Robots ◽  
Layer Model ◽  
Two Layer Model ◽  
Dialogue Planning

An architecture for argumentative dialogue planning

1996 ◽  
pp. 555-566 ◽  
Author(s):  
Chris Reed ◽  
Derek Long ◽  
Maria Fox
Keyword(s):  
Dialogue Planning

Issues in computerizing the inquiry dialogue planning process

1996 ◽  
pp. 252-260 ◽  
Author(s):  
Lung-Hsiang Wong ◽  
Chee-Kit Looi ◽  
Hiok-Chai Quek
Keyword(s):  
Planning Process ◽  
Inquiry Dialogue ◽  
Dialogue Planning

scholarly journals Bayes-Adaptive Monte-Carlo Planning and Learning for Goal-Oriented Dialogues

2020 ◽  
Vol 34 (05) ◽  
pp. 7994-8001
Author(s):  
Youngsoo Jang ◽  
Jongmin Lee ◽  
Kee-Eung Kim
Keyword(s):  
Reinforcement Learning ◽  
Task Performance ◽  
State Of The Art ◽  
Search Space ◽  
Difficult Problem ◽  
The Other ◽  
Adaptive Planning ◽  
Adaptive Monte Carlo ◽  
Planning Algorithm ◽  
Dialogue Planning

We consider a strategic dialogue task, where the ability to infer the other agent's goal is critical to the success of the conversational agent. While this problem can be naturally formulated as Bayesian planning, it is known to be a very difficult problem due to its enormous search space consisting of all possible utterances. In this paper, we introduce an efficient Bayes-adaptive planning algorithm for goal-oriented dialogues, which combines RNN-based dialogue generation and MCTS-based Bayesian planning in a novel way, leading to robust decision-making under the uncertainty of the other agent's goal. We then introduce reinforcement learning for the dialogue agent that uses MCTS as a strong policy improvement operator, casting reinforcement learning as iterative alternation of planning and supervised-learning of self-generated dialogues. In the experiments, we demonstrate that our Bayes-adaptive dialogue planning agent significantly outperforms the state-of-the-art in a negotiation dialogue domain. We also show that reinforcement learning via MCTS further improves end-task performance without diverging from human language.

Dialogue-based human-computer interfaces and active language understanding

2002 ◽  
Vol 1 (2) ◽  
pp. 275-286 ◽  
Author(s):  
Will Fitzgerald ◽  
R. James Firby
Keyword(s):  
Task Planning ◽  
Main Mode ◽  
Language Understanding ◽  
Computer Interfaces ◽  
Embedded Computer ◽  
Computer Technologies ◽  
Human Computer Interfaces ◽  
Recent Developments ◽  
Dialogue Planning ◽  
Close Integration

Recent developments in speech, network and embedded-computer technologies indicate that human–computer interfaces that use speech as one or the main mode of interaction will become increasingly prevalent. Such interfaces must move beyond simple voice commands to support a dialogue-based interface if they are to provide for common requirements such as description resolution, perceptual anchoring, and deixis. To support human–computer dialogue effectively, architectures must support active language understanding: that is, they must support the close integration of dialogue planning and execution with general task planning and execution.

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