HENRI: High Efficiency Negotiation-based Robust Interface for Multi-party Multi-issue Negotiation over the Internet
This paper proposes a framework for a full fledged negotiation system that allows multi party multi issue negotiation. It focuses on the negotiation protocol to be observed and provides a platform for concurrent and independent negotiation on individual issues using the concept of multi threading. It depicts the architecture of an agent detailing its components. The paper sets forth a hierarchical pattern for the multiple issues concerning every party. The system also provides enhancements such as the time-to-live counters for every advertisement, refinement of utility considering non-functional attributes, prioritization of issues, by assigning weights to issues.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
NegotiationGym: Self-Optimizing Agents in a Multi-Agent Social Simulation Environment
We design and implement NegotiationGym, an API and user interface for configuring and running multi-agent social simulations focused upon negotiation and cooperation. The NegotiationGym codebase offers a user-friendly, c…
Towards Emotion-Aware Agents For Negotiation Dialogues
Negotiation is a complex social interaction that encapsulates emotional encounters in human decision-making. Virtual agents that can negotiate with humans are useful in pedagogy and conversational AI. To advance the deve…
Decision MakingEvaluating Persuasion Strategies and Deep Reinforcement Learning methods for Negotiation Dialogue agents
In this paper we present a comparative evaluation of various negotiation strategies within an online version of the game {``}Settlers of Catan{''}. The comparison is based on human subjects playing games against artifici…
Deep Reinforcement LearningPersuasion Strategiesreinforcement-learningReinforcement Learning+1Multi-task Safe Reinforcement Learning for Navigating Intersections in Dense Traffic
Multi-task intersection navigation including the unprotected turning left, turning right, and going straight in dense traffic is still a challenging task for autonomous driving. For the human driver, the negotiation skil…
Autonomous Drivingreinforcement-learningReinforcement LearningReinforcement Learning (RL)+1EvoEmo: Towards Evolved Emotional Policies for Adversarial LLM Agents in Multi-Turn Price Negotiation
Recent research on Chain-of-Thought (CoT) reasoning in Large Language Models (LLMs) has demonstrated that agents can engage in \textit{complex}, \textit{multi-turn} negotiations, opening new avenues for agentic AI. Howev…
Reinforcement Learning