The Sixth Automated Negotiating Agents Competition (ANAC 2015)

Fujita, Katsuhide; Aydoğan, Reyhan; Baarslag, Tim; Hindriks, Koen V.; Ito, Takayuki; Jonker, Catholijn M.

doi:10.1007/978-3-319-51563-2_9

Cited by 25 publications

(11 citation statements)

References 8 publications

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“…Matsune and Fujita combines multiple existing opponent modeling algorithms by using boosting based on the least square method and nonlinear programming in multilateral negotiation settings [9]. In that sense, our work is complementary.…”

Section: Related Workmentioning

confidence: 99%

“…We evaluate the performance of our strategies against a variety of negotiation scenarios (i.e., different domains, different opponents) in General Environment for Negotiation with Intelligent multi-purpose Usage, [16] GENIUS 6 , a testbed also used in International Competition Automated Negotiating Agents Competition (ANAC) [13,9]. The benchmark provided by the competition has been used in our tests.…”

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confidence: 99%

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Collective Voice of Experts in Multilateral Negotiation

Güneş

Arditi

Aydoğan

2017

PRIMA 2017: Principles and Practice of Multi-Agent Systems

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Inspired from the ideas such as "algorithm portfolio", "mixture of experts", and "genetic algorithm", this paper presents two novel negotiation strategies, which combine multiple negotiation experts to decide what to bid and what to accept during the negotiation. In the first approach namely incremental portfolio, a bid is constructed by asking each negotiation agent's opinion in the portfolio and picking one of the suggestions stochastically considering the expertise levels of the agents. In the second approach namely crossover strategy, each expert agent makes a bid suggestion and a majority voting is used on each issue value to decide the bid content. The proposed approaches have been evaluated empirically and our experimental results showed that the crossover strategy outperformed the top five finalists of the ANAC 2016 Negotiation Competition in terms of the obtained average individual utility.

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Section: Related Workmentioning

confidence: 99%

mentioning

confidence: 99%

Collective Voice of Experts in Multilateral Negotiation

Güneş

Arditi

Aydoğan

2017

PRIMA 2017: Principles and Practice of Multi-Agent Systems

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“…Automated negotiation is a broad subfield of AI, with many recent works centering on the Automated Negotiating Agents Competition (Fujita et al 2016;Aydogan et al 2020). However, much of the work in this space either makes too many simplifying assumptions about the negotiation space to be applied directly to game negotiations or is highly domain specific (like the Diplomacy agents referenced in the introduction).…”

Section: Automated Negotiationmentioning

confidence: 99%

Exploring Monte Carlo Negotiation Search with Nontrivial Agreements

Malaby

Licato

2021

FLAIRS

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The application of automated negotiations to general game playing is a research area with far-reaching implications. Non-zero sum games can be used to model a wide variety of real-world scenarios and automated negotiation provides a framework for more realistically modeling the behavior of agents in these scenarios. A particular recent development in this space is the Monte Carlo Negotiation Search (MCNS) algorithm, which can negotiate to find valuable cooperative strategies for a wide array of games (such as those of the Game Description Language). However, MCNS only proposes agreements corresponding to individual sequences of moves without any higher-level notions of conditional or stateful strategy. Our work attempts to lift this restriction. We present two contributions: extensions to the MCNS algorithm to support more complex agreements and an agreement language for GDL games suitable for use with our algorithm. We also present the results of a preliminary experiment in which we use our algorithm to search for an optimal agreement for the iterated prisoners dilemma. We demonstrate significant improvement of our algorithm over random agreement sampling, although further work is required to more consistently produce optimal agreements.

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“…ANAC2015 introduced a qualitative index that is referred to as Cooperativeness [27]. A cooperative domain includes bids through which all agents can obtain high utilities, and a competitive domain includes bids where the variances of the utilities of each agent are high even though the social welfare of the bids is high.…”

Section: Experimental Settingsmentioning

confidence: 99%

Weighting Estimation Methods for Opponents' Utility Functions Using Boosting in Multi-Time Negotiations

Matsune

Fujita

2018

IEICE Trans. Inf. & Syst.

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Recently, multi-issue closed negotiations have attracted attention in multi-agent systems. In particular, multi-time and multilateral negotiation strategies are important topics in multi-issue closed negotiations. In multi-issue closed negotiations, an automated negotiating agent needs to have strategies for estimating an opponent's utility function by learning the opponent's behaviors since the opponent's utility information is not open to others. However, it is difficult to estimate an opponent's utility function for the following reasons: (1) Training datasets for estimating opponents' utility functions cannot be obtained. (2) It is difficult to apply the learned model to different negotiation domains and opponents. In this paper, we propose a novel method of estimating the opponents' utility functions using boosting based on the least-squares method and nonlinear programming. Our proposed method weights each utility function estimated by several existing utility function estimation methods and outputs improved utility function by summing each weighted function. The existing methods using boosting are based on the frequency-based method, which counts the number of values offered, considering the time elapsed when they offered. Our experimental results demonstrate that the accuracy of estimating opponents' utility functions is significantly improved under various conditions compared with the existing utility function estimation methods without boosting.

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The Sixth Automated Negotiating Agents Competition (ANAC 2015)

Cited by 25 publications

References 8 publications

Collective Voice of Experts in Multilateral Negotiation

Collective Voice of Experts in Multilateral Negotiation

Exploring Monte Carlo Negotiation Search with Nontrivial Agreements

Weighting Estimation Methods for Opponents' Utility Functions Using Boosting in Multi-Time Negotiations

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