Toki Takahashi — accepted AI-conference papers

2023

Reward-Based Negotiating Agent Strategies

Ryota Higa, Katsuhide Fujita, Toki Takahashi, Takumu Shimizu, Shinji Nakadai

AAAI 2023technical

This study proposed a novel reward-based negotiating agent strategy using an issue-based represented deep policy network. We compared the negotiation strategies with reinforcement learning (RL) by the tournaments toward heuristics-based champion agents in multi-issue negotiation. A bilateral multi-i…

Cited by 10 Source ↗PDF ↗Scholar ↗

2023

Scalable Negotiating Agent Strategy via Multi-Issue Policy Network (Student Abstract)

Takumu Shimizu, Ryota Higa, Toki Takahashi, Katsuhide Fujita, Shinji Nakadai

AAAI 2023technical

Previous research on the comprehensive negotiation strategy using deep reinforcement learning (RL) has scalability issues of not performing effectively in the large-sized domains. We improve negotiation strategy via deep RL by considering an issue-based represented deep policy network to deal with m…

Cited by 1 Source ↗PDF ↗Scholar ↗