2020/02/13 by Mark K. Ho, Ho, Mark K., David Abel +7 · 13 citations
Computer Science · Decision Sciences · Engineering · Psychology · #AI-based Problem Solving and Planning #Abstraction #Artificial Intelligence (cs.AI) #Artificial intelligence #Bayesian Modeling and Causal Inference #Cognition #Complex Systems and Decision Making #Computer science #Control (management) #Engineering #FOS: Computer and information sciences #Function (biology) #Management science #Plan (archaeology) #Psychology #Task (project management) #cs.AI
paper · pdf · doi:10.48550/arxiv.2002.05769
published in arXiv (Cornell University) (Cornell University) · 13 pg (incl. supplemental materials); included in Proceedings of the 34th AAAI Conference on Artificial Intelligence
arxiv created 2020/02/13 · openalex publication_date 2020/02/13 · arxiv updated 2020/02/17 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Planning is useful. It lets people take actions that have desirable long-term consequences. But, planning is hard. It requires thinking about consequences, which consumes limited computational and cognitive resources. Thus, people should plan their actions, but they should also be smart about how they deploy resources used for planning their actions. Put another way, people should also "plan their plans". Here, we formulate this aspect of planning as a meta-reasoning problem and formalize it in terms of a recursive Bellman objective that incorporates both task rewards and information-theoretic planning costs. Our account makes quantitative predictions about how people should plan and meta-plan as a function of the overall structure of a task, which we test in two experiments with human participants. We find that people's reaction times reflect a planned use of information processing, consistent with our account. This formulation of planning to plan provides new insight into the function of hierarchical planning, state abstraction, and cognitive control in both humans and machines.