Advancing the Search Frontier with AI Agents
Ryen W. White

TL;DR
This paper discusses how AI agents, especially generative AI, are transforming search systems to better support complex tasks and improve information interaction, with significant implications for future search design.
Contribution
It provides an analysis of recent AI agent developments and their potential to enhance search capabilities for complex tasks, highlighting future research directions.
Findings
AI agents improve support for complex search tasks
Generative AI enhances information interaction
Future search systems will be more intelligent and task-aware
Abstract
As many of us in the information retrieval (IR) research community know and appreciate, search is far from being a solved problem. Millions of people struggle with tasks on search engines every day. Often, their struggles relate to the intrinsic complexity of their task and the failure of search systems to fully understand the task and serve relevant results. The task motivates the search, creating the gap/problematic situation that searchers attempt to bridge/resolve and drives search behavior as they work through different task facets. Complex search tasks require more than support for rudimentary fact finding or re-finding. Research on methods to support complex tasks includes work on generating query and website suggestions, personalizing and contextualizing search, and developing new search experiences, including those that span time and space. The recent emergence of generative…
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Taxonomy
TopicsAI-based Problem Solving and Planning · Artificial Intelligence in Games
MethodsFocus
