Leveraging automatic strategy discovery to teach people how to select better projects
- URL: http://arxiv.org/abs/2406.04082v1
- Date: Thu, 6 Jun 2024 13:51:44 GMT
- Title: Leveraging automatic strategy discovery to teach people how to select better projects
- Authors: Lovis Heindrich, Falk Lieder,
- Abstract summary: 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.
- Score: 0.9821874476902969
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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 that take people's constraints into account. So far, this line of research has been limited to simplified decision problems. This article is the first to extend this approach to a real-world decision problem, namely project selection. We develop a computational method (MGPS) that automatically discovers project selection strategies that are optimized for real people and develop an intelligent tutor that teaches the discovered strategies. We evaluated MGPS on a computational benchmark and tested the intelligent tutor in a training experiment with two control conditions. MGPS outperformed a state-of-the-art method and was more computationally efficient. Moreover, the intelligent tutor significantly improved people's decision strategies. Our results indicate that our method can improve human decision-making in naturalistic settings similar to real-world project selection, a first step towards applying strategy discovery to the real world.
Related papers
- Barbarians at the Gate: How AI is Upending Systems Research [58.95406995634148]
We argue that systems research, long focused on designing and evaluating new performance-oriented algorithms, is particularly well-suited for AI-driven solution discovery.<n>We term this approach as AI-Driven Research for Systems ( ADRS), which iteratively generates, evaluates, and refines solutions.<n>Our results highlight both the disruptive potential and the urgent need to adapt systems research practices in the age of AI.
arXiv Detail & Related papers (2025-10-07T17:49:24Z) - Evolving Deeper LLM Thinking [61.61227021098086]
The proposed approach, Mind Evolution, uses a language model to generate, recombine and refine candidate responses.
Mind Evolution significantly outperforms other inference strategies such as Best-of-N and Sequential Revision in natural language planning tasks.
arXiv Detail & Related papers (2025-01-17T00:41:44Z) - SMART: Self-learning Meta-strategy Agent for Reasoning Tasks [44.45037694899524]
We introduce SMART (Self-learning Meta-strategy Agent for Reasoning Tasks), a novel framework that enables LMs to learn and select the most effective strategies for various reasoning tasks.
We model the strategy selection process as a Markov Decision Process and leverage reinforcement learning-driven continuous self-improvement.
Our experiments demonstrate that SMART significantly enhances the ability of models to choose optimal strategies without external guidance.
arXiv Detail & Related papers (2024-10-21T15:55:04Z) - Combining AI Control Systems and Human Decision Support via Robustness and Criticality [53.10194953873209]
We extend a methodology for adversarial explanations (AE) to state-of-the-art reinforcement learning frameworks.
We show that the learned AI control system demonstrates robustness against adversarial tampering.
In a training / learning framework, this technology can improve both the AI's decisions and explanations through human interaction.
arXiv Detail & Related papers (2024-07-03T15:38:57Z) - 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) - Training Towards Critical Use: Learning to Situate AI Predictions
Relative to Human Knowledge [22.21959942886099]
We introduce a process-oriented notion of appropriate reliance called critical use that centers the human's ability to situate AI predictions against knowledge that is uniquely available to them but unavailable to the AI model.
We conduct a randomized online experiment in a complex social decision-making setting: child maltreatment screening.
We find that, by providing participants with accelerated, low-stakes opportunities to practice AI-assisted decision-making, novices came to exhibit patterns of disagreement with AI that resemble those of experienced workers.
arXiv Detail & Related papers (2023-08-30T01:54:31Z) - A Reinforcement Learning-assisted Genetic Programming Algorithm for Team
Formation Problem Considering Person-Job Matching [70.28786574064694]
A reinforcement learning-assisted genetic programming algorithm (RL-GP) is proposed to enhance the quality of solutions.
The hyper-heuristic rules obtained through efficient learning can be utilized as decision-making aids when forming project teams.
arXiv Detail & Related papers (2023-04-08T14:32:12Z) - 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.<n>Compared to previous intelligent tutors for teaching planning strategies, this novel intelligent tutor combines two innovations.<n>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) - On solving decision and risk management problems subject to uncertainty [91.3755431537592]
Uncertainty is a pervasive challenge in decision and risk management.
This paper develops a systematic understanding of such strategies, determine their range of application, and develop a framework to better employ them.
arXiv Detail & Related papers (2023-01-18T19:16:23Z) - 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) - Self-directed Machine Learning [86.3709575146414]
In education science, self-directed learning has been shown to be more effective than passive teacher-guided learning.
We introduce the principal concept of Self-directed Machine Learning (SDML) and propose a framework for SDML.
Our proposed SDML process benefits from self task selection, self data selection, self model selection, self optimization strategy selection and self evaluation metric selection.
arXiv Detail & Related papers (2022-01-04T18:32:06Z) - 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) - The MineRL BASALT Competition on Learning from Human Feedback [58.17897225617566]
The MineRL BASALT competition aims to spur forward research on this important class of techniques.
We design a suite of four tasks in Minecraft for which we expect it will be hard to write down hardcoded reward functions.
We provide a dataset of human demonstrations on each of the four tasks, as well as an imitation learning baseline.
arXiv Detail & Related papers (2021-07-05T12:18:17Z) - Decision Rule Elicitation for Domain Adaptation [93.02675868486932]
Human-in-the-loop machine learning is widely used in artificial intelligence (AI) to elicit labels from experts.
In this work, we allow experts to additionally produce decision rules describing their decision-making.
We show that decision rule elicitation improves domain adaptation of the algorithm and helps to propagate expert's knowledge to the AI model.
arXiv Detail & Related papers (2021-02-23T08:07:22Z) - Improving Human Decision-Making by Discovering Efficient Strategies for
Hierarchical Planning [0.6882042556551609]
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.
arXiv Detail & Related papers (2021-01-31T19:46:00Z) - Leveraging Expert Consistency to Improve Algorithmic Decision Support [62.61153549123407]
We explore the use of historical expert decisions as a rich source of information that can be combined with observed outcomes to narrow the construct gap.
We propose an influence function-based methodology to estimate expert consistency indirectly when each case in the data is assessed by a single expert.
Our empirical evaluation, using simulations in a clinical setting and real-world data from the child welfare domain, indicates that the proposed approach successfully narrows the construct gap.
arXiv Detail & Related papers (2021-01-24T05:40:29Z) - 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.