INA: An Integrative Approach for Enhancing Negotiation Strategies with
  Reward-Based Dialogue System
Zishan Ahmad Suman Saurabh Vaishakh Sreekanth Menon Asif Ekbal Roshni Ramnani Anutosh Maitra
In this paper, we propose a novel negotiation dialogue agent designed for the
online marketplace. Our agent is integrative in nature i.e, it possesses the
capability to negotiate on price as well as other factors, such as the addition
or removal of items from a deal bundle, thereby offering a more flexible and
comprehensive negotiation experience. We create a new dataset called
Integrative Negotiation Dataset (IND) to enable this functionality. For this
dataset creation, we introduce a new semi-automated data creation method, which
combines defining negotiation intents, actions, and intent-action simulation
between users and the agent to generate potential dialogue flows. Finally, the
prompting of GPT-J, a state-of-the-art language model, is done to generate
dialogues for a given intent, with a human-in-the-loop process for post-editing
and refining minor errors to ensure high data quality. We employ a set of novel
rewards, specifically tailored for the negotiation task to train our
Negotiation Agent, termed as the Integrative Negotiation Agent (INA). These
rewards incentivize the chatbot to learn effective negotiation strategies that
can adapt to various contextual requirements and price proposals. By leveraging
the IND, we train our model and conduct experiments to evaluate the
effectiveness of our reward-based dialogue system for negotiation. Our results
demonstrate that the proposed approach and reward system significantly enhance
the agent's negotiation capabilities. The INA successfully engages in
integrative negotiations, displaying the ability to dynamically adjust prices
and negotiate the inclusion or exclusion of items in a bundle deal