Automatic Discovery and Description of Human Planning Strategies
Julian Skirzynski, Yash Raj Jain, Falk Lieder

TL;DR
This paper introduces Human-Interpret, an AI algorithm that automatically discovers and describes human planning strategies, matching human descriptions in clarity and coverage, thus accelerating scientific discovery in psychology and beyond.
Contribution
The work automates the process of identifying and verbalizing human planning strategies using imitation learning, reducing manual effort and enabling rapid analysis across various domains.
Findings
Automatically generated descriptions are as understandable as human ones.
The method covers a substantial proportion of known planning strategies.
It saves time and effort in scientific analysis.
Abstract
Scientific discovery concerns finding patterns in data and creating insightful hypotheses that explain these patterns. Traditionally, this process required human ingenuity, but with the galloping advances in artificial intelligence (AI) it becomes feasible to automate some parts of scientific discovery. In this work we leverage AI for strategy discovery for understanding human planning. In the state-of-the-art methods data about the process of human planning is often used to group similar behaviors together and formulate verbal descriptions of the strategies which might underlie those groups. Here, we automate these two steps. 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. We test our method on a benchmark data…
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Taxonomy
TopicsAdvanced Text Analysis Techniques · Complex Systems and Decision Making
MethodsTest
