We present the results of the 2nd 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.@en

Computers that negotiate on our behalf hold great promise for the future and will even become indispensable in emerging application domains such as the smart grid and the Internet of Things. Much research has thus been expended to create agents that are able to negotiate in an abundance of circumstances. However, up until now, truly autonomous negotiators have rarely been deployed in real-world applications. This paper sizes up current negotiating agents and explores a number of technological, societal and ethical challenges that autonomous negotiation systems have brought about. The questions we address are: in what sense are these systems autonomous, what has been holding back their further proliferation, and is their spread something we should encourage? We relate the automated negotiation research agenda to dimensions of autonomy and distill three major themes that we believe will propel autonomous negotiation forward: accurate representation, long-term perspective, and user trust. We argue these orthogonal research directions need to be aligned and advanced in unison to sustain tangible progress in the field. @en

We present the results of the first annual Human-Agent League of ANAC. By introducing a new human-agent negotiating platform to the research community at large, we facilitated new advancements in human-aware agents. This has succeeded in pushing the envelope in agent design, and creating a corpus of useful human-agent interaction data. Our results indicate a variety of agents were submitted, and that their varying strategies had distinct outcomes on many measures of the negotiation. These agents approach the problems endemic to human negotiation, including user modeling, bidding strategy, rapport techniques, and strategic bargaining. Some agents employed advanced tactics in information gathering or emotional displays and gained more points than their opponents, while others were considered more "likeable" by their partners.@en

To improve a negotiator's ability to recognise bidding strategies, we pro-actively provide explanations that are based on the opponent's bids and the negotiator's guesses about the opponent's strategy. We introduce an aberration detection mechanism for recognising strategies and the notion of an explanation matrix. The aberration detection mechanism identifies when a bid falls outside the range of expected behaviour for a specific strategy. The explanation matrix is used to decide when to provide what explanations. We evaluated our work experimentally in a task in which participants are asked to identify their opponent's strategy in the environment of a negotiation support system, namely the Pocket Negotiator (PN). We implemented our explanation mechanism in the PN and experimented with different explanation matrices. As the number of correct guesses increases with explanations, indirectly, these experiments show the effectiveness of our aberration detection mechanism. Our experiments with over 100 participants show that suggesting consistent strategies is more effective than explaining why observed behaviour is inconsistent.@en

Effective use of negotiation support systems depends on the systems capability of explaining itself to the user. This paper introduces the notion of an explanation matrix and an aberration detection mechanism for bidding strategies. The aberration detection is a mechanism that detects if one of the negotiating parties deviates from their expected behaviour, i.e. when a bid falls outside the range of expected behaviour for a specific strategy. The explanation matrix is used when to explain which aberrations to the user. The idea is that the user, when understanding the aberration, can take effective action to deal with the aberration. We implemented our aberration detection and our explanation mechanisms in the Pocket Negotiator (PN). We evaluated our work experimentally in a task in which participants are asked to identify their opponent’s bidding strategy, under different explanation conditions. As the number of correct guesses increases with explanations, indirectly, these experiments show the effectiveness of our aberration detection mechanism. Our experiments with over 100 participants show that suggesting consistent strategies is more effective than explaining why observed behaviour is inconsistent. An extended abstract of this article can be found in [15].@en

The Automated Negotiating Agents Competition (ANAC) is a yearly-organized international contest in which participants from all over the world develop intelligent negotiating agents for a variety of negotiation problems. To facilitate the research on agent-based negotiation, the organizers introduce new research challenges every year. ANAC 2019 posed five negotiation challenges: automated negotiation with partial preferences, repeated human-agent negotiation, negotiation in supply-chain management, negotiating in the strategic game of Diplomacy, and in the Werewolf game. This paper introduces the challenges and discusses the main findings and lessons learnt per league.@en