{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,14]],"date-time":"2026-03-14T20:30:22Z","timestamp":1773520222439,"version":"3.50.1"},"reference-count":0,"publisher":"Association for Computing Machinery (ACM)","issue":"2","content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["SIGIR Forum"],"published-print":{"date-parts":[[2025,12,1]]},"abstract":"<jats:p>In long-term life tasks, people often face challenges from uncertainty in tasks and information-seeking, which can create difficulties in decision-making and task completion. Recent advancements in Artificial Intelligence (AI), especially in Large Language Models (LLMs), offer transformative capabilities in domain-specific task planning and problem-solving. Despite these innovations, there is limited understanding of how such technologies can be applied to assist humans in long-term life tasks. This dissertation work seeks to address this gap by exploring how human-AI collaboration, mediated through LLM-based agents, can improve long-term life task planning and uncertainty management. To achieve this, this dissertation first proposes the long-term life task type and investigates how people may use AI tools to assist them in planning long-term life tasks and cope with uncertainty. Secondly, it proposes the Goal Oriented Long-term liFe planning (GOLF) framework that integrates LLMs to emulate human-AI collaboration in diverse long-term life task domains. The framework facilitates task decomposition and iterative planning while evaluating uncertainties throughout the process. Thirdly, the study introduces a GOLF benchmark to evaluate and generalize the effectiveness of LLMs in long-term planning scenarios. This study operates at the intersection of cognitive science, information science, and human-AI interaction, and makes multifaceted contributions. Theoretically, by introducing the concept of long-term life tasks and proposing the GOLF framework, it explores the potential of human-AI collaborations for long-term planning and uncertainty management. Methodologically, through a simulation-based approach and the LLM benchmark, it enables systematic evaluation of LLMs in long-term planning scenarios across domains. This work bridges the gap between human cognitive strategies and AI planning capabilities, providing a robust foundation for future advancements in AI-assisted task management and goal achievement.<\/jats:p>\n                  <jats:p>\n                    <jats:bold>Awarded by:<\/jats:bold>\n                    The University of Oklahoma, Norman, OK, USA on 4 December 2025.\n                  <\/jats:p>\n                  <jats:p>\n                    <jats:bold>Supervised by:<\/jats:bold>\n                    Jiqun Liu.\n                  <\/jats:p>","DOI":"10.1145\/3799914.3799938","type":"journal-article","created":{"date-parts":[[2026,3,4]],"date-time":"2026-03-04T18:19:03Z","timestamp":1772648343000},"page":"1-2","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["LLM-Based Long-Term Life Task Planning to Reduce Human Uncertainty"],"prefix":"10.1145","volume":"59","author":[{"given":"Ben","family":"Wang","sequence":"first","affiliation":[{"name":"Amazon, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2026,3,4]]},"container-title":["ACM SIGIR Forum"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3799914.3799938","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,14]],"date-time":"2026-03-14T17:19:31Z","timestamp":1773508771000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3799914.3799938"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12,1]]},"references-count":0,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2025,12,1]]}},"alternative-id":["10.1145\/3799914.3799938"],"URL":"https:\/\/doi.org\/10.1145\/3799914.3799938","relation":{},"ISSN":["0163-5840"],"issn-type":[{"value":"0163-5840","type":"print"}],"subject":[],"published":{"date-parts":[[2025,12,1]]},"assertion":[{"value":"2026-03-04","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}