Dialogue Game Tree with Nondeterministic Additive Consolidation

2007 ◽  
pp. 115-133
Author(s):  
Yoshitaka Suzuki
Keyword(s):  
Game Tree ◽  
Dialogue Game

scholarly journals A game strategy model in the digital curling system based on NFSP

2021 ◽  
Author(s):  
Yuntao Han ◽  
Qibin Zhou ◽  
Fuqing Duan
Keyword(s):  
Reinforcement Learning ◽  
Nash Equilibrium ◽  
Action Space ◽  
Learning Networks ◽  
Game Tree ◽  
Continuous Action ◽  
Extensive Game ◽  
Strategy Model ◽  
Zero Sum ◽  
Tree Searching

AbstractThe digital curling game is a two-player zero-sum extensive game in a continuous action space. There are some challenging problems that are still not solved well, such as the uncertainty of strategy, the large game tree searching, and the use of large amounts of supervised data, etc. In this work, we combine NFSP and KR-UCT for digital curling games, where NFSP uses two adversary learning networks and can automatically produce supervised data, and KR-UCT can be used for large game tree searching in continuous action space. We propose two reward mechanisms to make reinforcement learning converge quickly. Experimental results validate the proposed method, and show the strategy model can reach the Nash equilibrium.

scholarly journals Boosting material modeling using game tree search

2018 ◽  
Vol 2 (10) ◽  
Author(s):  
Ryohto Sawada ◽  
Yuma Iwasaki ◽  
Masahiko Ishida
Keyword(s):  
Material Modeling ◽  
Tree Search ◽  
Game Tree ◽  
Game Tree Search

EVA 2.0: Emotional and rational multimodal argumentation between virtual agents

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Niklas Rach ◽  
Klaus Weber ◽  
Yuchi Yang ◽  
Stefan Ultes ◽  
Elisabeth André ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
User Feedback ◽  
Virtual Agents ◽  
Multi Agent System ◽  
Dialogue Game ◽  
Multi Agent ◽  
The Individual ◽  
Minimal Bias ◽  
Intuitive Interface ◽  
Emotional Level

Abstract Persuasive argumentation depends on multiple aspects, which include not only the content of the individual arguments, but also the way they are presented. The presentation of arguments is crucial – in particular in the context of dialogical argumentation. However, the effects of different discussion styles on the listener are hard to isolate in human dialogues. In order to demonstrate and investigate various styles of argumentation, we propose a multi-agent system in which different aspects of persuasion can be modelled and investigated separately. Our system utilizes argument structures extracted from text-based reviews for which a minimal bias of the user can be assumed. The persuasive dialogue is modelled as a dialogue game for argumentation that was motivated by the objective to enable both natural and flexible interactions between the agents. In order to support a comparison of factual against affective persuasion approaches, we implemented two fundamentally different strategies for both agents: The logical policy utilizes deep Reinforcement Learning in a multi-agent setup to optimize the strategy with respect to the game formalism and the available argument. In contrast, the emotional policy selects the next move in compliance with an agent emotion that is adapted to user feedback to persuade on an emotional level. The resulting interaction is presented to the user via virtual avatars and can be rated through an intuitive interface.

Evaluation of Game Tree Search Methods by Game Records

2010 ◽  
Vol 2 (4) ◽  
pp. 288-302 ◽  
Author(s):  
Shogo Takeuchi ◽  
Tomoyuki Kaneko ◽  
Kazunori Yamaguchi
Keyword(s):  
Tree Search ◽  
Game Tree ◽  
Search Methods ◽  
Game Tree Search

scholarly journals Multi-cut αβ-pruning in game-tree search

2001 ◽  
Vol 252 (1-2) ◽  
pp. 177-196 ◽  
Author(s):  
Yngvi Björnsson ◽  
Tony A. Marsland
Keyword(s):  
Tree Search ◽  
Game Tree ◽  
Game Tree Search

A General Branch-and-Bound Formulation for AND/OR Graph and Game Tree Search

1988 ◽  
pp. 91-130 ◽  
Author(s):  
Vipin Kumar ◽  
Dana S. Nau ◽  
Laveen N. Kanal
Keyword(s):  
Branch And Bound ◽  
Tree Search ◽  
Game Tree ◽  
Game Tree Search
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