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Outline

Title
Abstract
FAQs
The Mediation Protocol
Basic Operation of the Negotiation Protocol
Experimental Evaluation
Categories and Subject Descriptors
Experimental Settings
Conclusions and Future Work
Finding Agreements
Experimental Results
Method of Determining Interdependency Graph
Conclusion and Future Works
Protocol
Metric Related to the Graph
Metric Related to the Utilities
Metric Related to Beliefs
Experimental Analysis
Regression Analysis
Experimental Setting
Results and Analysis
Detailed Experimental Setup
Hypotheses and Experimental Results
Conclusion and Future Work
Background: CP-Nets & Ucp-Nets
Discussion
Preliminary Discussions 2.1 Incomplete Information
Result of Experiment
Mediator Design
Experimental Methodology
Experiment Results
Collaborative Park Design Sup-Port System
Method of Eliciting the Utility Spaces
Results of Experiments
Related Works
Related Work
Conclusion
Experiments and Results
The Experimental Setup
The Practical Protocol Against Qea
Introduction
Data Acquisition
Conclusions
References

Consensus Policy Based Multi-agent Negotiation

Profile image of Enrique de la HozEnrique de la Hoz

2011, Lecture Notes in Computer Science

https://doi.org/10.1007/978-3-642-25044-6_14
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description

98 pages

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1 file

Abstract

Complex Automated Negotiations have been widely studied and are becoming an important, emerging area in the field of Autonomous Agents and Multi-Agent Systems. In general, automated negotiations can be complex, since there are a lot of factors that characterize such negotiations. These factors include the number of issues, dependency between issues, representation of utility, negotiation protocol, negotiation form (bilateral or multi-party), time constraints, etc. Software agents can support automation or simulation of such complex negotiations on the behalf of their owners, and can provide them with adequate bargaining strategies. In many multi-issue bargaining settings, negotiation becomes more than a zero-sum game, so bargaining agents have an incentive to cooperate in order to achieve efficient win-win agreements. Also, in a complex negotiation, there could be multiple issues that are interdependent.

FAQs

sparkles

AI

What key challenges exist in automated negotiation with incomplete information?add

Automated negotiation often struggles with agents unwilling to reveal private information, complicating optimal decision-making. This leads to issues in accurately estimating opponents' preferences and constraints during negotiations.

How does AniMed improve over existing automated mediators?add

AniMed utilizes animated and interactive avatars to facilitate negotiations, enhancing user engagement and outcomes. Its unique strategy allows proposing partial solutions based on current negotiation states, increasing the likelihood of agreements.

What empirical evidence supports AniMed's effectiveness in negotiations?add

Experiments showed significant increases in individual utility scores and social welfare when AniMed mediated, yielding better outcomes compared to interactions with human negotiators and simpler automated mediators.

How are user preferences elicited in the collaborative park design system?add

Users provide evaluations on generated park designs, which are processed to estimate their utility functions through sampling techniques based on the feedback received.

What metrics are employed to evaluate agent behavior in the Social Ultimatum Game?add

Metrics include offer distribution, target-recipient distribution, rejection probabilities, and reciprocity over time, providing a comprehensive analysis of agent behavior against human behavior patterns.

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