“Just in Time” World Modeling Supports Human Planning and Reasoning
An overview of a state-of-the-art study, uncovering simulation-based reasoning, a "just-in-time" framework and how it helps improve predictions in the context of supporting human planning and reasoning.
Image by Editor
# Understanding Just-in-Time World Modeling
This article provides an overview and summary of the recently published paper titled "Just in Time" World Modeling Supports Human Planning and Reasoning, which is fully available to read at arXiv.
Using a gentler and more accessible tone for a wider audience, we will cover what simulation-based reasoning is, describe the overall just-in-time (JIT) framework presented in the article with a focus on the orchestration of mechanisms it uses, and summarize how it behaves and helps improve predictions in the context of supporting human planning and reasoning.
# Understanding Simulation-Based Reasoning
Imagine you are in the most remote corner of a dark, messy room full of obstacles and want to determine the exact path to reach the door without colliding. In parallel, suppose you are about to hit a pool ball and visualize the exact trajectory you expect the ball to follow. In these two situations, there is one thing in common: the ability to project a future situation in our mind without conducting any action. This is known as simulation-based reasoning, and sophisticated AI agents need this skill in a variety of situations.
Simulation-based reasoning is a cognitive tool we humans constantly use for decision-making, route planning, and predicting what will happen next in our environment. Yet the real world is absurdly complex and full of nuance and detail. Trying to exhaustively calculate all the possible eventualities and their effects may quickly exhaust our mental resources in a matter of milliseconds. To avoid this, in biological terms, what we do is not create a near-perfect photographic copy of reality, but generate a simplified representation that retains truly relevant information only.
The scientific community is still trying to answer a major question: How does our brain decide so quickly and efficiently which details to include and which ones to omit in that mental simulation? That question motivates the JIT framework presented in the target study.
# Exploring the Underlying Mechanisms
To answer the previously formulated question, the researchers in the study present an innovative JIT framework that, unlike traditional theories that assume full environment observability before planning, proposes building a mental map on the fly, gathering information only when it is really necessary.
JIT framework proposed in the paper and applied to a navigation problem | Source: here
The biggest achievement in this model is how it defines the combination and intertwining between three key mechanisms:
- Simulation: It is based on the principle that our mind begins drafting in advance the course of action or route we will follow.
- Visual search: As the mental simulation progresses toward the unknown, it sends our eyes (or percepts, in the case of AI agents or systems) a signal to inspect that specific part of the physical (or digital) environment.
- Representation modification: When an object that may interfere with our plan is detected, e.g. an obstacle, the mind immediately "encodes" that object and adds it to its mental model to take it into account.
In practice, this is a quick and fluent cycle: The brain simulates to a humble degree, then "eyes" search for obstacles, the mind updates the information, and the simulation continues — all in a finely orchestrated way.
# Framework Behavior and Its Impact on Decision Making
What is the most fascinating aspect of the JIT model presented in the paper? It is arguably stunningly efficient. The authors tested it by comparing human behavior with computational simulations in two experiments: navigation in a maze and physical prediction trials, such as guessing where a ball will bounce.
Results showed that the JIT system stores in memory a significantly smaller number of objects than systems trying to exhaustively process the full environment from the outset. However, despite working based on a fragmented mental image that only includes a small portion of the full reality, the framework is capable of making high-quality, informed decisions. This offers a profound takeaway: Our mind improves its performance and response speed not by processing more data, but by being incredibly selective, achieving reliable predictions without overspending cognitive efforts.
# Considering Future Directions
While the JIT framework presented in the study offers a brilliant explanation of how humans plan (with potential implications for pushing the boundaries of AI systems), there are some horizons still to be explored. The trials conducted in the study only considered largely static environments. Therefore, expanding this model should also consider highly dynamic and even chaotic scenarios. Understanding how relevant information is selected when multiple non-static objects coexist around us might be the next big challenge to further progress in this fascinating human planning and reasoning theory and — who knows! — translating it to the AI world.
Iván Palomares Carrascosa is a leader, writer, speaker, and adviser in AI, machine learning, deep learning & LLMs. He trains and guides others in harnessing AI in the real world.