ANAC 2014 - Fifth Automated Negotiating Agents Competition

The ANAC competition brings together researchers from the negotiation community and provides a unique benchmark for evaluating practical negotiation strategies in multi-issue domains. The four previous competitions have spawned novel research in AI in the field of autonomous agent design which are available to the wider research community. The focus of this year's competition is on nonlinear utility functions. The goals of the competition are
to encourage the design of practical negotiation agents that can proficiently negotiate against unknown opponents and in a variety of circumstances,
to provide a benchmark for objectively evaluating different negotiation strategies,
to explore different learning and adaptation strategies and opponent models, and
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 is to develop an autonomous negotiation agent. Performance of the agents will be evaluated in a tournament setting, where each agent is matched with all other submitted agents, and each pair of agents will negotiate in a number of nonlinear negotiation scenarios. Negotiations are repeated several times to obtain statistically significant results. The winning agent will be the one with the highest overall score.
A negotiation scenario consists of a specification of the objectives and issues to be resolved by means of negotiation. This includes the preferences of both negotiating parties about the possible agreements. The preferences of a party are modelled using nonlinear, multi-issue utility functions.
Rules of Encounter
Negotiations are bilateral and based on the alternating-offers protocol. Offers are exchanged in real time with a deadline after 3 minutes. This means that the number of offers exchanged within a certain time period varies and depends on the computation required by the agents. If no agreement is reached by the deadline, or if either agent chooses to terminate the negotiation before the deadline, both agents receive their utility of conflict. In addition, there will be a discount factor in about half of the domains, where the value of an agreement decreases over time. The challenge for an agent is to negotiate without any knowledge of the opponent's preferences and strategy. Although each agent participates in many negotiation sessions, against different opponents, and in a wide variety of negotiation scenarios, agents cannot learn between negotiations. This means that negotiation agents only have the opportunity to adapt and learn from the offers they receive within a single negotiation session.
Agents can be disqualified for violating the spirit of fair play. The competition rules allow multiple entries from a single institution, but require each agent to be developed independently. Furthermore it is prohibited to design an agent which benefits some other specific agent. In particular, the following behaviors are strictly prohibited:
Designing an agent in such a way that it benefits some specific other agent.
Communicating with the agent during the competition.
Altering the agent during the competition.
The participants can use up to 2 GBytes of memory of their agent, if they use beyond that amount and the system cannot cope, their agent will be taken out of the competition.