The complexity of automated negotiation research calls for dedicated, user-friendly research frameworks that facilitate advanced analytics, comprehensive loggers, visualization tools, and auto-generated domains and preference profiles. This paper introduces NegoLog, a platform that provides advanced and customizable analysis modules to agent developers for exhaustive performance evaluation. NegoLog introduces an automated scenario and tournament generation tool in its Web-based user interface so that the agent developers can adjust the competitiveness and complexity of the negotiations. One of the key novelties of the NegoLog is an individual assessment of preference estimation models independent of the strategies.

This work presents BIDS (Bidding using Diversified Search), an algorithm that can be used by negotiating agents to search very large outcome spaces. BIDS provides a balance between being rapid, accurate, diverse, and scalable search, allowing agents to search spaces with as many as 10 ²⁵⁰ possible outcomes on very run-of-the-mill hardware. We show that our algorithm can be used to respond to the three most common search queries employed by 87% of all agents from the Automated Negotiating Agents Competition. Furthermore, we validate one of our techniques by integrating it into negotiation platform GeniusWeb, to enable existing state-of-the-art agents (and future agents) to scale their use to very large outcome spaces.

This work presents the Autonomous Bidding Coordinated Acceptance framework (ABCA): An agent-Team design that allows general bilateral agents to engage in oneto-many negotiations in a setting where (possibly overlapping) deals with multiple opponents are desirable. We propose also a coordinated acceptance strategy that uses the estimated outcomes of its bilateral negotiations while deciding to accept a deal.

We present the results of the 2^nd Annual Human-Agent League of the Automated Negotiating Agent Competition. Building on the success of the previous year's results, a new challenge was issued that focused exploring the likeability-success tradeoff in negotiations. By examining a series of repeated negotiations, actions may affect the relationship between automated negotiating agents and their human competitors over time. The results presented herein support a more complex view of human-agent negotiation and capture of integrative potential (win-win solutions). We show that, although likeability is generally seen as a tradeoff to winning, agents are able to remain well-liked while winning if integrative potential is not discovered in a given negotiation. The results indicate that the top-performing agent in this competition took advantage of this loophole by engaging in favor exchange across negotiations (cross-game logrolling). These exploratory results provide information about the effects of different submitted 'black-box' agents in human-agent negotiation and provide a state-of-the-art benchmark for human-agent design.