Call for Participation

After the successes of the previous two years, we would like to invite you to participate in the third Automated Negotiating Agents Competition (ANAC). This competition brings together researchers from the negotiation community and provides a unique benchmark for evaluating practical negotiation strategies in multi-issue domains. In particular, the goals include:

1. to encourage the design of practical negotiation agents that can proficiently negotiate against unknown opponents and in a variety of circumstances,
2. to provide a benchmark for objectively evaluating different negotiation strategies,
3. to explore different learning and adaptation strategies and opponent models, and
4. to collect state-of-the-art negotiating agents and negotiation scenarios, and making them available to the wider research community.

Entrants

The aim for the entrants to the competition was to develop an autonomous negotiation agent as well as submit a negotiation scenario. Performance of the agents was then evaluated in a tournament setting, where each agent was matched with all other submitted agents, and each pair of agents negotiated in each submitted negotiation scenario. Negotiations were repeated several times to obtain statistically significant results. The winning agent was defined as the one with the highest overall score.

A negotiation scenario consisted of a specification of the objectives and issues to be resolved by means of negotiation. This included the preferences of both negotiating parties about the possible agreements. The preferences of a party were modelled using linearly additive, multi-issue utility functions.

Rules of Encounter

Negotiations were bilateral and based on the alternating-offers protocol. Offers were exchanged in real time with a deadline after 3 minutes. This meant that the number of offers exchanged within a certain time period varied and depended on the computation required by the agents. If no agreement was reached by the deadline, or if either agent choose to terminate the negotiation before the deadline, both agents received their utility of conflict. In addition, there was a discount factor in about half of the domains, where the value of an agreement decreased over time.

The challenge for an agent was to negotiate without any knowledge of the opponent's preferences and strategy. Although each agent participated in many negotiation sessions, against different opponents, and in a wide variety of negotiation scenarios, agents could not learn between negotiations. This meant that negotiation agents only had the opportunity to adapt and learn from the offers they received within a single negotiation session.

Changes with respect to ANAC 2011

In 2012, the competition introduced, for the first time, a private reservation value as part of the tournament. The reservation value of an agent was the utility of conflict, and was achieved if either the agent failed to reach an agreement by the deadline, or if one of the agents terminated the negotiation. The reservation values could be different for each agent and for each negotiation scenario. Each agent only knew its own reservation value, and not that of its opponent. The reservation value was discounted in the same way that an agreement would be. This made it rational, in certain circumstances, for an agent to terminate an agreement early, in order to take the reservation value with a smaller loss due to discounting.

GENIUS

The negotiation tournament was run using the java-based GENIUS negotiation platform, which has been developed to facilitate research in the area of bilateral multi-issue negotiation. It has an open architecture that allows for easy development and integration of existing negotiating agents using design patterns. GENIUS can be used to simulate individual negotiation sessions as well as tournaments between negotiating agents in various negotiation scenarios. The core functionality of the system includes:

1. specification of negotiation domains and preference profiles;
2. simulation of a bilateral negotiation between agents;
3. analysis of the negotiation outcomes and negotiation dynamics.

It furthermore allows the specification of negotiation domains and preference profiles by means of a graphical user interface.

The GENIUS platform, together with the agents and scenarios from the previous competitions are available for download. More information about the platform can be found at the GENIUS web page. The agents from the 2011 competition are included in this download. Their source code is also available for referece.

Qualifying Round and Finals

There was an initial qualifying round, and the top 8 performing agents continued to the finals, which were held at the AAMAS conference.

Each team that made it through to the finals sent a representative to the AAMAS 2012 conference. Furthermore, each team in the final was given the opportunity to give a brief presentation describing their agent.

The full results, including detailed breakdowns, along with the presentation slides and photos of the awards being presented can be found via our results page.

Prizes

There was a total reward of US$1500 which was divided between the top performing entrants.

Contact

For any questions, the main contact is