Improving Human Decision-Making by Discovering Efficient Strategies for Hierarchical Planning

• URL: http://arxiv.org/abs/2102.00521v1
• Date: Sun, 31 Jan 2021 19:46:00 GMT
• Title: Improving Human Decision-Making by Discovering Efficient Strategies for Hierarchical Planning
• Authors: Saksham Consul, Lovis Heindrich, Jugoslav Stojcheski, Falk Lieder
• Abstract summary: People need efficient planning strategies because their computational resources are limited. Our ability to compute those strategies used to be limited to very small and very simple planning tasks. We introduce a cognitively-inspired reinforcement learning method that can overcome this limitation.
• Score: 0.6882042556551609
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: To make good decisions in the real world people need efficient planning strategies because their computational resources are limited. Knowing which planning strategies would work best for people in different situations would be very useful for understanding and improving human decision-making. But our ability to compute those strategies used to be limited to very small and very simple planning tasks. To overcome this computational bottleneck, we introduce a cognitively-inspired reinforcement learning method that can overcome this limitation by exploiting the hierarchical structure of human behavior. The basic idea is to decompose sequential decision problems into two sub-problems: setting a goal and planning how to achieve it. This hierarchical decomposition enables us to discover optimal strategies for human planning in larger and more complex tasks than was previously possible. The discovered strategies outperform existing planning algorithms and achieve a super-human level of computational efficiency. We demonstrate that teaching people to use those strategies significantly improves their performance in sequential decision-making tasks that require planning up to eight steps ahead. By contrast, none of the previous approaches was able to improve human performance on these problems. These findings suggest that our cognitively-informed approach makes it possible to leverage reinforcement learning to improve human decision-making in complex sequential decision-problems. Future work can leverage our method to develop decision support systems that improve human decision making in the real world.

Related papers

• Learning to Assist Humans without Inferring Rewards [65.28156318196397]
  We build upon prior work that studies assistance through the lens of empowerment. An assistive agent aims to maximize the influence of the human's actions. We prove that these representations estimate a similar notion of empowerment to that studied by prior work.
  arXiv  Detail & Related papers  (2024-11-04T21:31:04Z)
• Leveraging automatic strategy discovery to teach people how to select better projects [0.9821874476902969]
  The decisions of individuals and organizations are often suboptimal because normative decision strategies are too demanding in the real world. Recent work suggests that some errors can be prevented by leveraging artificial intelligence to discover and teach prescriptive decision strategies. This article is the first to extend this approach to a real-world decision problem, namely project selection.
  arXiv  Detail & Related papers  (2024-06-06T13:51:44Z)
• Simple Hierarchical Planning with Diffusion [54.48129192534653]
  Diffusion-based generative methods have proven effective in modeling trajectories with offline datasets. We introduce the Hierarchical diffuser, a fast, yet surprisingly effective planning method combining the advantages of hierarchical and diffusion-based planning. Our model adopts a "jumpy" planning strategy at the higher level, which allows it to have a larger receptive field but at a lower computational cost.
  arXiv  Detail & Related papers  (2024-01-05T05:28:40Z)
• Optimising Human-AI Collaboration by Learning Convincing Explanations [62.81395661556852]
  We propose a method for a collaborative system that remains safe by having a human making decisions. Ardent enables efficient and effective decision-making by adapting to individual preferences for explanations.
  arXiv  Detail & Related papers  (2023-11-13T16:00:16Z)
• Risk-reducing design and operations toolkit: 90 strategies for managing risk and uncertainty in decision problems [65.268245109828]
  This paper develops a catalog of such strategies and develops a framework for them. It argues that they provide an efficient response to decision problems that are seemingly intractable due to high uncertainty. It then proposes a framework to incorporate them into decision theory using multi-objective optimization.
  arXiv  Detail & Related papers  (2023-09-06T16:14:32Z)
• An intelligent tutor for planning in large partially observable environments [0.8739101659113157]
  We develop and evaluate the first intelligent tutor for planning in partially observable environments. Compared to previous intelligent tutors for teaching planning strategies, this novel intelligent tutor combines two innovations. A preregistered experiment with 330 participants demonstrated that the new intelligent tutor is highly effective at improving people's ability to make good decisions in partially observable environments.
  arXiv  Detail & Related papers  (2023-02-06T13:57:08Z)
• Achieving mouse-level strategic evasion performance using real-time computational planning [59.60094442546867]
  Planning is an extraordinary ability in which the brain imagines and then enacts evaluated possible futures. We develop a more efficient biologically-inspired planning algorithm, TLPPO, based on work on how the ecology of an animal governs the value of spatial planning. We compare the performance of a real-time agent using TLPPO against the performance of live mice, all tasked with evading a robot predator.
  arXiv  Detail & Related papers  (2022-11-04T18:34:36Z)
• Boosting human decision-making with AI-generated decision aids [8.373151777137792]
  We developed an algorithm for translating the output of our previous method into procedural instructions. Experiments showed that these automatically generated decision-aids significantly improved people's performance in planning a road trip and choosing a mortgage. These findings suggest that AI-powered boosting might have potential for improving human decision-making in the real world.
  arXiv  Detail & Related papers  (2022-03-05T15:57:20Z)
• Procedure Planning in Instructional Videosvia Contextual Modeling and Model-based Policy Learning [114.1830997893756]
  This work focuses on learning a model to plan goal-directed actions in real-life videos. We propose novel algorithms to model human behaviors through Bayesian Inference and model-based Imitation Learning.
  arXiv  Detail & Related papers  (2021-10-05T01:06:53Z)
• Improving Human Sequential Decision-Making with Reinforcement Learning [29.334511328067777]
  We design a novel machine learning algorithm that is capable of extracting "best practices" from trace data. Our algorithm selects the tip that best bridges the gap between the actions taken by human workers and those taken by the optimal policy. Experiments show that the tips generated by our algorithm can significantly improve human performance.
  arXiv  Detail & Related papers  (2021-08-19T02:57:58Z)
• Automatic Discovery of Interpretable Planning Strategies [9.410583483182657]
  We introduce AI-Interpret, a method for transforming idiosyncratic policies into simple and interpretable descriptions. We show that prividing the decision rules generated by AI-Interpret as flowcharts significantly improved people's planning strategies and decisions.
  arXiv  Detail & Related papers  (2020-05-24T12:24:52Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.