In this paper, we present AutoMed, an automated mediator for multi-issue bilateral negotiation under time constraints. AutoMed elicits the negotiators preferences and analyzes them. It monitors the negotiations and proposes possible solutions for resolving the conflict. We conducted experiments in a simulated environment. The results show that negotiations mediated by AutoMed are concluded significantly faster than non-mediated ones and without any of the negotiators opting out. Furthermore, the subjects in the mediated negotiations are more satisfied with the resolutions than the subjects in the non-mediated negotiations.
The ability to reliably represent and replicate choices people make is crucial for building accurate models of day-today situations. The fact that people are inherently rationally-and computationally-bounded increases the difficulties in designing such simulations. This paper builds on the use of peer-designed agents (PDAs)-computer agents developed by people-to * Preliminary results of this research were published in the proceedings of AAMAS 2008.
Today, sensors and/or anomaly detection algorithms (ADAs) are used to collect data in a wide variety of applications(e.g. Cyber security systems, sensor networks, etc.). Today, every sensor or ADA in its applied system participates in the collection of data throughout the entire system. The data collected from all of the sensors or ADAs are then integrated into one significant conclusion or decision, a process known as data fusion. However, the reliability, or reputation, of a single sensor or ADA may change over time, or may not be known at all. Since this reputation is taken into account when determining the final conclusion post data classification, one must be able to predict their reputations. We propose a new machine learning prediction technique (MLPT) to predict the reputation of each sensor or ADA. This technique is based on the existing 'Decision Tree Certainty Level' technique, or DTCL, which is the creation of many random decision trees (forests) with high certainty levels [Dolev et al. (2009)]. In particular, it was shown that the DTCL enhances the classification capabilities of CARTs (Classification and Regression Trees) [Briman et al. (1984)]. After applying the DTCL technique to the reputation data, we then apply a new evolutionary process on those decision trees to reduce the overall number of trees by merging only the most accurate trees and then using only these new trees to generate the reputation values. Thus, we combine DTCL and evolution techniques to enable the determination of sensor or ADA reputations by using only the most accurate trees. Finally, we demonstrate how to improve the data fusion process by identifying the most reliable portions of the collected data to reach more accurate conclusions.
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