Abstract-Rideshare systems allow a substantial number of people to mutually benefit from using less cars in a specific region. This would rationalize energy consumption, save money, and decrease traffic jams and pollution. However, accessibility issues have prevented these architectures from being widely spread. In this paper, we present an agent-based Rideshare system that is accessible via lightweight devices. We use auction mechanism as a method of negotiation among autonomous and proactive agents, by this we aim at accelerating agents' interactions while resolving end-user composite tasks.
People tend to form social networks within geographical areas. This can be explained by the fact that generally geographical localities correspond to common interests (e.g. students located in a university could be interested to buy or sell textbooks adopted for a specific course, to share notes, or just to meet together to play basketball). Cellular phones and more in general mobile devices are currently widely used and represent a big opportunity to support social communities. In this paper, we present a general architecture for multi-agent systems accessible via mobile devices (cellular phones and PDAs), where Bluetooth technology has been adopted to reflect users locality. We illustrate ToothAgent, an implemented prototype of the proposed architecture, and discuss the opportunities offered by the system.
Abstract. Independence, intelligence and interactiveness are making software agents strongly approach the development of advanced service applications for both, pocket and fixed computing devices. In this paper we present an interactions protocol that is used by intelligent agents operating in a dynamic environment. In particular, we focus our research on the situation where a multi-agent system is serving lightweight devices through advanced communication methods (e.g., Bluetooth). Like similar contributions, our interactions protocol provides agents with a monetary system and a mechanism for feedback calculation. The goal of our research was to accelerate efficient agents interactions while resolving end-user composite tasks.
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