Hypertext environments such as the Web are rich with both word and link cues that can be exploited by autonomous agents performing distributed tasks on behalf of the user. This paper characterizes such environments and identifies the features that are most useful and readily available. We describe the adaptive representation of an ecology of retrieval agents who attempt to capture important features of their surroundings, and base their behaviors upon them. We discuss how such a representation allows the agents to interact with the environments where they are situated.
Agents can internalize words that are locally correlated with fitness, based on user feedback.
They are shown to outperform non-adaptive search by an order of magnitude. Furthermore, each agent learns new strategies at local time and space scales, while the population evolves at a global scale.