We describe the strategy used by our agent, IAMhaggler2011, which finished in third place in the 2011 Automated Negotiating Agent Competition. A key feature of this agent is the way in which it models the likely negotiation behaviour of its opponent. Specifically, it first uses a Gaussian process regression technique to estimate the future concession of its negotiation opponent. Its concession is then set as a best response to this prediction.@en

We describe the strategy used by our agent, IAMhaggler, which finished in third place in the 2010 Automated Negotiating Agent Competition. It uses a concession strategy to determine the utility level at which to make offers. This concession strategy uses a principled approach which considers the offers made by the opponent. It then uses a Pareto-search algorithm combined with Bayesian learning in order to generate a multi-issue offer with a specific utility as given by its concession strategy.@en

The third Automated Negotiating Agents Competition (ANAC 2012) was held at the 11th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2012, Valencia, Spain). ANAC is an international competition that aims to encourage research into bilateral, multi-issue negotiation, by providing a platform in which strategies developed independently by different research teams can be tried and compared against each other, in a real-time competition. In the 2012 edition, we received 17 entries from 9 different universities worldwide, out of which 8 were selected for the final round. This chapter aims to provide a broad description of the competition set-up (especially highlighting the changes from previous editions), the preference domains and the strategies submitted, as well as the results from both the qualifying and final rounds.@en

We propose a novel strategy to enable autonomous agents to negotiate concurrently with multiple, unknown opponents in realtime, over complex multi-issue domains. We formalise our strategy as an optimisation problem, in which decisions are based on probabilistic information about the opponents' strategies acquired during negotiation. In doing so, we develop the first principled approach that enables the coordination of multiple, concurrent negotiation threads for practical negotiation settings. Furthermore, we validate our strategy using the agents and domains developed for the International Automated Negotiating Agents Competition (ANAC), and we benchmark our strategy against the state-of-the-art. We find that our approach significantly outperforms existing approaches, and this difference improves even further as the number of available negotiation opponents and the complexity of the negotiation domain increases.@en

In multi-issue automated negotiation against unknown opponents, a key part of effective negotiation is the choice of concession strategy. In this paper, we develop a principled concession strategy, based on Gaussian processes predicting the opponent's future behaviour. We then use this to set the agent's concession rate dynamically during a single negotiation session. We analyse the performance of our strategy and show that it outperforms the state-of-the-art negotiating agents from the 2010 Automated Negotiating Agents Competition, in both a tournament setting and in self-play, across a variety of negotiation domains.@en