A distributed intelligent agent approach to context in information retrieval

Abstract

Information retrieval across disadvantaged networks requires intelligent agents that can make decisions about what to transmit in such a way as to minimize network performance impact while maximizing utility and quality of information (QOI). Specialized agents at the source need to process unstructured, ad-hoc queries, identifying both the context and the intent to determine the implied task. Knowing the task will allow the distributed agents that service the requests to filter, summarize, or transcode data prior to responding, lessening the network impact. This paper describes an approach that uses natural language processing (NLP) techniques, multi-valued logic based inferencing, distributed intelligent agents, and task-relevant metrics for information retrieval.