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Found 15 papers, 7 papers with code
Experience-driven discovery of planning strategies
In this work, we propose that new planning strategies are discovered through metacognitive reinforcement learning.
Leveraging automatic strategy discovery to teach people how to select better projects
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.
What are the mechanisms underlying metacognitive learning?
How is it that humans can solve complex planning tasks so efficiently despite limited cognitive resources?
An intelligent tutor for planning in large partially observable environments
Unlike these artificial tasks, the world is only partially observable.
Toward a normative theory of (self-)management by goal-setting
This creates an untapped opportunity to derive practical recommendations for which subgoals managers and individuals should set from cognitive models of bounded rationality.
Boosting human decision-making with AI-generated decision aids
Designing such decision aids used to be a tedious manual process.
Have I done enough planning or should I plan more?
People's decisions about how to allocate their limited computational resources are essential to human intelligence.
Automatic Discovery and Description of Human Planning Strategies
Our method utilizes a new algorithm, called Human-Interpret, that performs imitation learning to describe sequences of planning operations in terms of a procedural formula and then translates that formula to natural language.
Optimal To-Do List Gamification for Long Term Planning
We test the accuracy of the incentivised to-do list by comparing the performance of the strategy with the points computed exactly using Value Iteration for a variety of case studies.
Improving Human Decision-Making by Discovering Efficient Strategies for Hierarchical Planning
Future work can leverage our method to develop decision support systems that improve human decision making in the real world.
Optimal to-do list gamification
Optimal gamification strives to help people overcome these problems by incentivizing each task by a number of points that communicates how valuable it is in the long-run.
Automatic Discovery of Interpretable Planning Strategies
Our algorithm combines recent advances in imitation learning and program induction with a new clustering method for identifying a large subset of demonstrations that can be accurately described by a simple, high-performing decision rule.
Learning to select computations
The efficient use of limited computational resources is an essential ingredient of intelligence.
Algorithm selection by rational metareasoning as a model of human strategy selection
Rational metareasoning appears to be a promising framework for reverse-engineering how people choose among cognitive strategies and translating the results into better solutions to the algorithm selection problem.
Burn-in, bias, and the rationality of anchoring
Therefore minds and machines have to approximate Bayesian inference.
