Multiagent Negotiation and Resource Allocation

---

Negotiation with private knowledge

We present offer generation methods for negotiation among multiple agents on multiple issues where agents have no knowledge about the preferences of other agents. Most of the existing negotiation literature considers agents with either full information or probabilistic beliefs about the other agents preferences on the issues. However, in reality, it is usually not possible for agents to have complete information about other agents preferences or accurate probability distributions. Moreover, the extant literature typically assumes linear utility functions. We present a reactive offer generation method for general \emph{ multiagent multi-attribute negotiation}, where the agents have \emph{ non-linear utility functions and no information} about the utility functions of other agents. We prove the convergence of the proposing method to an agreement acceptable to the agents. We also prove that rational agents do not have any incentive to deviate from the proposed strategy. We further present simulation results to demonstrate that on randomly generated problem instances the negotiation solution obtained by using our strategy is quite close to the Nash bargaining solution.
• R. Zheng, N. Chakraborty, T. Dai, and K. Sycara, `` Automated Multiagent Negotiation on Multiple Issues with Private Information '', Autonomous Agents and Multiagent Systems (Under Review). Available as Technical Report CMU-RI-TR-13-04.

• R. Zheng, N. Chakraborty, T. Dai, and K. Sycara, `` Automated Bilateral Multiple-Issue Negotiation with No Information about Opponent '', 2013 Hawaai International Conference on Systems Sciences , Maui, Hawaii, January 2013. (Nominated for Best Paper Award.)

---

Scheduling energy consumption with private knowledge

A key challenge to create a sustainable and energy-efficient society is in making consumer demand adaptive to energy supply, especially renewable supply. In this paper, we propose a partially-centralized organization of consumers, namely, a consumer cooperative for purchasing electricity from the market. We propose a novel multiagent coordination algorithm to shape the energy consumption of the cooperative. In the cooperative, a central coordinator buys the electricity for the whole group and consumers make their own consumption decisions based on their private consumption constraints and preferences. To coordinate individual consumers under incomplete information, we propose an iterative algorithm in which a virtual price signal is sent by the coordinator to induce consumers to shift demand. We prove that our algorithm converges to the central optimal solution. Additionally we analyze the convergence rate of the algorithm via simulations on randomly generated instances. The results indicate scalability with respect to the number of agents and consumption slots.
• A. Veit, Y. Xue, R. Zheng, N. Chakraborty, and K. Sycara, `` Multiagent Coordination for Energy Consumption Scheduling in Consumer Cooperatives '', 27th AAAI Conference, Bellevue, WA, July 2013.

---



Back to top.
Back to Nilanjan's research interests

---