Gear Aditya Srinivas received his MS Dual Degree in Computer Science and Engineering (CSE). His research work was supervised by Dr. Praveen Paruchuri. Here’s a summary of Gear Aditya Srinivas’s thesis PredictRV: A Prediction Based Strategy for Negotiations with Dynamically Changing Reservation Value:
Negotiation is an important component of the interaction process among humans. With increasing automation, autonomous agents are expected to takeover a lot of this interaction process. Much of automated negotiation literature focuses on agents having a static and known reservation value. Fur- thermore, a good part of these works focus on negotiation between two agents. In situations involving dynamic environments with multiple agents involved e.g., an agent negotiating on behalf of a human regarding a meeting, agents can have a Reservation Value (RV) that is a function of time. This leads to a different set of challenges that may need additional reasoning about the concession behavior. In this thesis, we build upon Negotiation algorithms such as ONAC (Optimal Non-Adaptive Concession), Time Dependent Techniques such as Boulware and OptimalBidder which work on settings where the reservation value of the agent is fixed and known. Although these algorithms can encode dynamic RV, their concession behavior and hence the properties they were expected to display would be different from when the RV is static, even though the underlying negotiation algorithm remains the same. We therefore propose to use one of Counter, Bayesian Learning with Regression Analysis or LSTM model on top of each algorithm to develop the PredictRV strategy and show that PredictRV indeed performs better on two different metrics tested on two different domains on a variety of parameter settings. We present results for two settings: (a) One involving negotiations between two agents i.e, one agent that use ONAC or Boulware and other with one of these algorithms in combination with one of the PredictRV models leading to 8 (2+2*3) possible agent models and (b) The second for heterogeneous parties of four and five agents i.e., agents that use one of OptimalBidder or Boulware strategies or these two algorithms in combination with one of the 3 PredictRV models leading to 8 (2+2*3) possible agent models.