Overcoming Information Overload with Artificial Selective Agents: an Application to Travel Information Domain

Authors

Abstract

We describe an application of Macedo’s computational model of selective attention for overcoming the problem of information and interruption overload of intelligent agents in travel information systems. This computational model has been integrated into the architecture of a BDI artificial agent so that this can autonomously select relevant, interesting travel information, while ignoring other less relevant information, to communicate to its human owner so that her/his processing resources can be allocated more effectively. We illustrate and provide experimental results of this role of the artificial, selective attention mechanism in the travel domain.

Keywords

Information overload, Selective attention, Interest, Value of information, Surprise, Uncertainty, Resource-bounded agents, Personal agents

Related Project

iCIS - Intelligent Computing in the Internet of Services

Conference

13th International Conference on Autonomous Agents and Multiagent Systems –8th International Workshop on Agents in Traffic and Transportation, April 2014