Designing the market game for a trading agent competition

Wellman, Michael P.; Wurman, Peter R.; O'Malley, Kevin; Bangera, R.; Lin, Shou-De; Reeves, Daniel M.; Walsh, William E.

doi:10.1109/4236.914647

Cited by 106 publications

(42 citation statements)

References 6 publications

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“…ANAC is a negotiation competition aiming to facilitate and coordinate the research into proficient negotiation strategies for bilateral multi-issue negotiation, similar to what the Trading Agent Competition (TAC) has achieved for the trading agent problem [20]. The seven agents that participated in ANAC 2010 have been implemented by various international research groups of negotiation experts.…”

Section: Detailed Experimental Setup Agentsmentioning

confidence: 99%

Towards a Quantitative Concession-Based Classification Method of Negotiation Strategies

Baarslag

Hindriks

Jonker

2011

Lecture Notes in Computer Science

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Abstract. In order to successfully reach an agreement in a negotiation, both parties rely on each other to make concessions. The willingness to concede also depends in large part on the opponent. A concession by the opponent may be reciprocated, but the negotiation process may also be frustrated if the opponent does not concede at all.This process of concession making is a central theme in many of the classic and current automated negotiation strategies. In this paper, we present a quantitative classification method of negotiation strategies that measures the willingness of an agent to concede against different types of opponents. The method is then applied to classify some well-known negotiating strategies, including the agents of ANAC 2010. It is shown that the technique makes it easy to identify the main characteristics of negotiation agents, and can be used to group negotiation strategies into categories with common negotiation characteristics. We also observe, among other things, that different kinds of opponents call for a different approach in making concessions.

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Section: Detailed Experimental Setup Agentsmentioning

confidence: 99%

Towards a Quantitative Concession-Based Classification Method of Negotiation Strategies

Baarslag

Hindriks

Jonker

2011

Lecture Notes in Computer Science

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Section: Agentsmentioning

confidence: 99%

Acceptance Conditions in Automated Negotiation

Baarslag

Hindriks

Jonker

2013

Studies in Computational Intelligence

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Abstract. In every negotiation with a deadline, one of the negotiating parties has to accept an offer to avoid a break off. A break off is usually an undesirable outcome for both parties, therefore it is important that a negotiator employs a proficient mechanism to decide under which conditions to accept. When designing such conditions one is faced with the acceptance dilemma: accepting the current offer may be suboptimal, as better offers may still be presented. On the other hand, accepting too late may prevent an agreement from being reached, resulting in a break off with no gain for either party. Motivated by the challenges of bilateral negotiations between automated agents and by the results and insights of the automated negotiating agents competition (ANAC), we classify and compare state-of-the-art generic acceptance conditions. We focus on decoupled acceptance conditions, i.e. conditions that do not depend on the bidding strategy that is used. We performed extensive experiments to compare the performance of acceptance conditions in combination with a broad range of bidding strategies and negotiation domains. Furthermore we propose new acceptance conditions and we demonstrate that they outperform the other conditions that we study. In particular, it is shown that they outperform the standard acceptance condition of comparing the current offer with the offer the agent is ready to send out. We also provide insight in to why some conditions work better than others and investigate correlations between the properties of the negotiation environment and the efficacy of acceptance conditions.

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“…Second, there is an extensive literature on both all-human experiments [dating back to Vernon Smith's original work in 1962 (5)] and all-agent experiments [dating back at least as far as the Santa Fe Institute Double Auction Tournament in the early 1990s (6)], and ranging up to the present-day Trading Agent Competition conducted by Michael Wellman and his team at the University of Michigan (7,8).…”

Section: The First Economic Software Agentsmentioning

confidence: 99%

Software agents and the route to the information economy

Kephart

2002

Proc. Natl. Acad. Sci. U.S.A.

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Humans are on the verge of losing their status as the sole economic species on the planet. In private laboratories and in the Internet laboratory, researchers and developers are creating a variety of autonomous economically motivated software agents endowed with algorithms for maximizing profit or utility. Many economic software agents will function as miniature businesses, purchasing information inputs from other agents, combining and refining them into information goods and services, and selling them to humans or other agents. Their mutual interactions will form the information economy: a complex economic web of information goods and services that will adapt to the ever-changing needs of people and agents. The information economy will be the largest multiagent system ever conceived and an integral part of the world's economy. I discuss a possible route toward this vision, beginning with present-day Internet trends suggesting that agents will charge one another for information goods and services. Then, to establish that agents can be competent price setters, I describe some laboratory experiments pitting software bidding agents against human bidders. The agents' superior performance suggests they will be used on a broad scale, which in turn suggests that interactions among agents will become frequent and significant. How will this affect macroscopic economic behavior? I describe some interesting phenomena that my colleagues and I have observed in simulations of large populations of automated buyers and sellers, such as price war cycles. I conclude by discussing fundamental scientific challenges that remain to be addressed as we journey toward the information economy.

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Designing the market game for a trading agent competition

Cited by 106 publications

References 6 publications

Towards a Quantitative Concession-Based Classification Method of Negotiation Strategies

Towards a Quantitative Concession-Based Classification Method of Negotiation Strategies

Acceptance Conditions in Automated Negotiation

Software agents and the route to the information economy

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