The First Automated Negotiating Agents Competition (ANAC 2010)

Studies in Computational Intelligence, 2013

In May 2011, we organized the Second International Automated Negotiating Agents Competition (ANAC2011) in conjunction with AAMAS 2011. ANAC is an international competition that challenges researchers to develop a successful automated negotiator for scenarios where there is incomplete information about the opponent. One of the goals of this competition is to help steer the research in the area of bilateral multi-issue negotiations, and to encourage the design of generic negotiating agents that are able to operate in a variety of scenarios. Eighteen teams from seven different institutes competed in ANAC2011. This chapter describes the participating agents and the setup of the tournament, including the different negotiation scenarios that were used in the competition. We report on the results of the qualifying and final round of the tournament.

Studies in Computational Intelligence, 2016

In May 2014, we organized the Fifth International Automated Negotiating Agents Competition (ANAC 2014) in conjunction with AAMAS 2014. ANAC is an international competition that challenges researchers to develop a successful automated negotiator for scenarios where there is incomplete information about the opponent. One of the goals of this competition is to help steer the research in the area of bilateral multi-issue negotiations, and to encourage the design of generic negotiating agents that are able to operate in a variety of scenarios. 21 teams from 13 different institutes competed in ANAC 2014. This chapter describes the participating agents and the setup of the tournament, including the different negotiation scenarios that were used in the competition. We report on the results of the qualifying and final round of the tournament.

ECMS 2008 Proceedings edited by: L. S. Louca, Y. Chrysanthou, Z. Oplatkova, K. Al-Begain, 2008

Autonomous agents with negotiation competence are becoming increasingly important and pervasive. This paper follows an interdisciplinary approach to build autonomous negotiating agents by considering both game-theoretic techniques and bargaining procedures from the social sciences. The paper presents a generic model that handles bilateral multi-issue negotiation, describes equilibrium strategies for the bargaining game of alternating offers, and formalizes important strategies used by human negotiators. Autonomous agents equipped with the model are able to negotiate under both complete and incomplete information, thereby making them very compelling for automated negotiation.

Multi-Agent Systems and Agreement Technologies, 2020

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 supplychain 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.

Computational Intelligence, 1995

The design of automated negotiators has been the focus of abundant research in recent years. However, due to difficulties involved in creating generalized agents that can negotiate in several domains and against human counterparts, many automated negotiators are domain specific and their behavior cannot be generalized for other domains. Some of these difficulties arise from the differences inherent within the domains, the need to understand and learn negotiators' diverse preferences concerning issues of the domain and the different strategies negotiators can undertake. In this paper we present a system that enables alleviation of the difficulties in the design process of general automated negotiators termed GENIUS, a General Environment for Negotiation with Intelligent multipurpose Usage Simulation. With the constant introduction of new domains, e-commerce and other applications, which require automated negotiations, generic automated negotiators encompass many benefits and advantages over agents that are designed for a specific domain. Based on experiments conducted with automated agents designed by human subjects using GENIUS we provide both quantitative and qualitative results to illustrate its efficacy. Finally, we also analyze a recent automated bilateral negotiators competition that was based on GENIUS. Our results show the advantages and underlying benefits of using GENIUS and how it can facilitate the design of general automated negotiators.

Studies in Computational Intelligence, 2010

The design of automated negotiators has been the focus of abundant research in recent years. However, due to difficulties involved in creating generalized agents that can negotiate in several domains and against human counterparts, many automated negotiators are domain specific and their behavior cannot be generalized for other domains. Some of these difficulties arise from the differences inherent within the domains, the need to understand and learn negotiators' diverse preferences concerning issues of the domain and the different strategies negotiators can undertake. In this paper we present a system that enables alleviation of the difficulties in the design process of general automated negotiators termed GENIUS, a General Environment for Negotiation with Intelligent multi-purpose Usage Simulation. With the constant introduction of new domains, e-commerce and other applications, which require automated negotiations, generic automated negotiators encompass many benefits and advantages over agents that are designed for a specific domain. Based on experiments conducted with automated agents designed by human subjects using GENIUS we provide both quantitative and qualitative results to illustrate its efficacy. Our results show the advantages and underlying benefits of using GENIUS for designing general automated negotiators.

Lecture Notes in Computer Science

Negotiation is an important and pervasive form of social interaction. The design of autonomous negotiating agents involves the consideration of insights from multiple relevant research areas to integrate different perspectives on negotiation. As a starting point for an interdisciplinary research effort, this paper presents a model that handles bilateral multi-issue negotiation, employs game-theoretic techniques to define equilibrium strategies for the bargaining game of alternating offers, and formalizes a set of negotiation strategies and tactics studied in the social sciences. Autonomous agents equipped with the model are currently being developed using the Jade framework. The agents are able to negotiate under both complete and incomplete information, thereby making the model in particular and the agents in general very compelling for automated negotiation.

Artificial Intelligence, 2013

This paper presents an in-depth analysis and the key insights gained from the Second International Automated Negotiating Agents Competition (ANAC 2011). ANAC is an international competition that challenges researchers to develop successful automated negotiation agents for scenarios where there is no information about the strategies and preferences of the opponents. The key objectives of this competition are to advance the state-of-the-art in the area of practical bilateral multi-issue negotiations, and to encourage the design of agents that are able to operate effectively across a variety of scenarios. Eighteen teams from seven different institutes competed. This paper describes these agents, the setup of the tournament, including the negotiation scenarios used, and the results of both the qualifying and final rounds of the tournament. We then go on to analyse the different strategies and techniques employed by the participants using two methods: (i) we classify the agents with respect to their concession behaviour against a set of standard benchmark strategies and (ii) we employ empirical game theory (EGT) to investigate the robustness of the strategies. Our analysis of the competition results allows us to highlight several interesting insights for the broader automated negotiation community. In particular, we show that the most adaptive negotiation strategies, while robust across different opponents, are not necessarily the ones that win the competition. Furthermore, our EGT analysis highlights the importance of considering metrics, in addition to utility maximisation (such as the size of the basin of attraction), in determining what makes a successful and robust negotiation agent for practical settings.

2009

With an increasing number of applications in the context of multi-agent systems, automated negotiation is a rapidly growing area. Written by top researchers in the field, this state-of-the-art treatment of the subject explores key issues involved in the design of negotiating agents, covering strategic, heuristic, and axiomatic approaches. The authors discuss the potential benefits of automated negotiation as well as the unique challenges it poses for computer scientists and for researchers in artificial intelligence. They also consider possible applications and give readers a feel for the types of domain where automated negotiation is already being deployed. This book is ideal for graduate students and researchers in computer science who are interested in multi-agent systems. It will also appeal to negotiation researchers from disciplines such as management and business studies, psychology, and economics.