Browsing by Author "Gratch, J."

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    Challenges and main results of the automated negotiating agents competition (ANAC) 2019
    (Springer, 2020) Aydoğan, Reyhan; Baarslag, T.; Fujita, K.; Mell, J.; Gratch, J.; de Jonge, D.; Mohammad, Y.; Nakadai, S.; Morinaga, S.; Osawa, H.; Aranha, C.; Jonker, C. M.; Computer Science; Bassiliades, N.; Chalkiadakis, G.; de Jonge, D.; AYDOĞAN, Reyhan
    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.
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    The likeability-success tradeoff: results of the 2nd annual human-agent automated negotiating agents competition
    (IEEE, 2019) Mell, J.; Gratch, J.; Aydoğan, Reyhan; Baarslag, T.; Jonker, C. M.; Computer Science; AYDOĞAN, Reyhan
    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 humanagent negotiation and provide a state-of-the-art benchmark for human-agent design.
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    Results of the first annual human-agent league of the automated negotiating agents competition
    (The ACM Digital Library, 2018) Mell, J.; Gratch, J.; Baarslag, T.; Aydoğan, Reyhan; Jonker, C. M.; Computer Science; AYDOĞAN, Reyhan
    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.