Automated Negotiating Agents Competition (ANAC2020)

To be held at IJCAI-PRICAI2020 in Yokohama, Japan, in July 11-17 as part of the IJCAI competition challenge

Overview of ANAC2020

The Automated Negotiating Agent Competition (ANAC) is an international tournament that has been running since 2010 to bring together researchers from the negotiation community. ANAC provides a unique benchmark for evaluating practical negotiation strategies in multi-issue domains and has the following aims:

  • to provide an incentive for the development of effective and efficient negotiation protocols and strategies for bidding, accepting and opponent modeling for different negotiation scenarios;
  • to collect and develop a benchmark of negotiation scenarios, protocols and strategies;
  • to develop a common set of tools and criteria for the evaluation and exploration of new protocols and new strategies against benchmark scenarios, protocols and strategies;
  • to set the research agenda for automated negotiation.
The previous competitions have spawned novel research in AI in the field of autonomous agent design which are available to the wider research community.

This year, we introduce five different negotiation research challenges:

We expect innovative and novel agent strategies will be developed, and the submitted ANAC 2020 agents will serve as a negotiating agent repository to the negotiation community. The researchers can develop novel negotiating agents and evaluate their agents by comparing their performance with the performance of the ANAC 2020 agents.

Leagues in ANAC2020

Automated Negotiation League

Representing users in a negotiation: developing an agent that can negotiate while performing preference elicitation.

Human-Agent League

Explore the strategies, nuances, and difficulties in creating realistic and efficient agents whose primary purpose is to ‚Äčnegotiate with humans.‚Äč

Supply Chain Management League

Design and build an autonomous agent that negotiates on behalf of a factory manager situated in a supply chain management simulation.

Werewolf Game League

Build an agent that is able to build consensus with other actors (who are potentially deceptive), identify agents of the opposing team and coordinate to vote them out of the game.


Render as avatars on a display, and the human interacts with them by speaking (in English) and looking at the one with whom they wish to negotiate.



NEC - AIST AI Cooperative Research Laboratory



ANAC Board Members

League Organizers

Sponsorship Chair in ANAC2020

Scientific Advisory Board



For any questions of ANAC2020, the main contact is Dr. Reyhan Aydogan reyhan.aydogan[at]