Learning Hierarchical Probabilistic World Models for Adaptive Reasoning Across Spatiotemporal Scales

Core Concepts

This thesis proposes novel probabilistic formalisms, namely Hidden-Parameter State Space Models (HiP-SSMs) and Multi-Time Scale State Space Models (MTS3), to develop scalable, adaptive hierarchical world models capable of representing nonstationary dynamics across multiple temporal abstractions and scales.

Abstract

The thesis identifies several limitations with the prevalent use of state space models (SSMs) as internal world models and proposes two new probabilistic formalisms to address these drawbacks.
The Hidden-Parameter SSM (HiP-SSM) formalism enables modeling of multitask dynamics using a singular, overarching model. It achieves this through a hierarchical latent task variable that parameterizes the latent dynamics. The structure of the graphical model facilitates scalable exact probabilistic inference using belief propagation, as well as end-to-end learning via backpropagation through time.
The Multi-Time Scale SSM (MTS3) formalism allows the development of hierarchical world models that can represent dynamics across multiple temporal abstractions and scales. It uses a multi-level hierarchical structure to capture fast and slow changing dynamics. The higher-level abstractions guide the lower-level predictions, enabling accurate long-term forecasting.
Both formalisms integrate the concept of uncertainty in world states, improving the system's capacity to emulate the stochastic nature of the real world and quantify the confidence in its predictions. Experiments on various real and simulated robots demonstrate that these formalisms can match and in many cases exceed the performance of contemporary transformer variants in making long-range future predictions.

Stats

Machines that can replicate human intelligence with type 2 reasoning capabilities should be able to reason at multiple levels of spatiotemporal abstractions and scales using internal world models.
State space models (SSMs) have limitations as internal world models, motivating the need for new probabilistic formalisms.
The proposed HiP-SSM and MTS3 formalisms enable scalable, adaptive hierarchical world models capable of representing nonstationary dynamics across multiple temporal abstractions and scales.
The graphical model structure of HiP-SSM and MTS3 facilitates scalable exact probabilistic inference and end-to-end learning.
Experiments show that HiP-SSM and MTS3 can match or exceed the performance of contemporary transformer models in long-range future predictions.

Quotes

"Machines that can replicate human intelligence with type 2 (Daniel 2017) reasoning capabilities should be able to reason at multiple levels of spatiotemporal abstractions and scales using internal world models."
"The structure of graphical models in both formalisms facilitates scalable exact probabilistic inference using belief propagation, as well as end-to-end learning via backpropagation through time."
"Our experiments on various real and simulated robots demonstrate that our formalisms can match and in many cases exceed the performance of contemporary transformer variants in making long-range future predictions."

Key Insights Distilled From

Learning World Models With Hierarchical Temporal Abstractions: A Probabilistic Perspective

by Vaisakh Shaj at arxiv.org 04-26-2024

https://arxiv.org/pdf/2404.16078.pdf

Deeper Inquiries

How can the proposed hierarchical world model formalisms be extended to incorporate other forms of reasoning, such as causal inference or common sense reasoning, to further enhance their capabilities?

Incorporating other forms of reasoning, such as causal inference or common sense reasoning, into the proposed hierarchical world model formalisms can significantly enhance their capabilities. One way to achieve this is by integrating causal inference mechanisms into the model architecture. By incorporating causal relationships between variables, the model can better understand the underlying mechanisms driving the observed data and make more accurate predictions. This can be done by explicitly modeling causal relationships between variables and incorporating causal reasoning algorithms into the inference process.
Additionally, integrating common sense reasoning into the model can further enhance its capabilities. Common sense reasoning involves leveraging background knowledge and general principles to make inferences about the world. By incorporating a knowledge base or ontology into the model and using it to guide the reasoning process, the model can make more informed decisions and predictions. This can help the model generalize better to unseen scenarios and improve its overall performance.
Overall, extending the hierarchical world model formalisms to incorporate causal inference and common sense reasoning can lead to more robust and intelligent systems that can reason more effectively in complex and uncertain environments.

What are the potential limitations or drawbacks of the current HiP-SSM and MTS3 models, and how could they be addressed in future research?

While the HiP-SSM and MTS3 models offer significant advancements in hierarchical world modeling, they also have potential limitations and drawbacks that need to be addressed in future research. Some of these limitations include:

Scalability: As the complexity of the models increases, scalability can become a challenge. Future research could focus on developing more efficient algorithms and architectures to handle larger and more complex datasets.

Interpretability: The black-box nature of some probabilistic models can hinder interpretability. Future research could explore methods to improve the interpretability of the models, making it easier for users to understand the reasoning behind the model's predictions.

Generalization: Ensuring that the models can generalize well to unseen data and adapt to new environments is crucial. Future research could investigate techniques to improve the generalization capabilities of the models, such as transfer learning or domain adaptation.

Incorporating Uncertainty: While the models integrate uncertainty in world states, there may be room for improvement in quantifying and utilizing uncertainty more effectively. Future research could focus on refining the uncertainty estimation methods to enhance the model's predictive capabilities.

Addressing these limitations could involve exploring novel algorithmic approaches, leveraging advancements in deep learning and probabilistic modeling, and conducting empirical studies to validate the effectiveness of proposed solutions.

How might the insights and approaches developed in this thesis contribute to the broader understanding of intelligence and cognition, beyond just the domain of machine learning?

The insights and approaches developed in this thesis have the potential to contribute significantly to the broader understanding of intelligence and cognition beyond the domain of machine learning. By focusing on hierarchical world models and probabilistic reasoning, the thesis sheds light on how intelligent systems can reason at multiple levels of abstraction and scales, mimicking human cognitive processes.

Cognitive Science: The thesis's emphasis on internal world models and probabilistic reasoning aligns with theories in cognitive science, such as predictive processing and Bayesian brain hypothesis. These insights can provide valuable perspectives on how the human brain processes information and makes decisions.

Artificial General Intelligence (AGI): The research on hierarchical world models can inform the development of more advanced AI systems capable of generalizing across tasks and adapting to new environments. This can contribute to the ongoing efforts to achieve AGI by incorporating principles of hierarchical abstraction and probabilistic reasoning.

Neuroscience: The thesis's exploration of hierarchical temporal abstractions and causal hierarchies resonates with research in neuroscience on how the brain represents and processes information. By drawing parallels between machine learning models and neural processes, the thesis can offer insights into the neural mechanisms underlying intelligence and cognition.

Overall, the insights and approaches developed in this thesis have the potential to bridge the gap between machine learning, cognitive science, and neuroscience, leading to a deeper understanding of intelligence and cognition in both artificial and biological systems.