Extending Structural Causal Models for Autonomous Embodied Systems

2024-06-03

AI Summary

To address the challenges of adapting structural causal models (SCMs) to real-time perception, decision-making, and human–machine collaboration in dynamic physical environments—such as autonomous driving—this paper proposes a modular and encapsulated SCM extension framework. Methodologically, it introduces a temporal causal representation with constant-space complexity and, for the first time, formally integrates dynamically varying variable sets into the SCM theoretical framework while preserving causal stationarity. Two embodied agent interaction case studies are constructed based on this framework, empirically validating its effectiveness in safety-aware interpretability, counterfactual reasoning, and online dynamic adaptation. The work overcomes key modeling bottlenecks of SCMs in embodied intelligent systems, establishing a novel causal modeling paradigm for trustworthy autonomous systems—one that is verifiable, interpretable, and evolvable.

Technology Category

• Reasoning under Uncertainty: Causality
• Machine Learning: Causal Learning
• Knowledge Representation and Reasoning: Action, Change, and Causality

Application Category

• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

In this work we aim to bridge the divide between autonomous embodied systems and causal reasoning. Autonomous embodied systems have come to increasingly interact with humans, and in many cases may pose risks to the physical or mental well-being of those they interact with. Meanwhile causal models, despite their inherent transparency and ability to offer contrastive explanations, have found limited usage within such systems. As such, we first identify the challenges that have limited the integration of structural causal models within autonomous embodied systems. We then introduce a number of theoretical extensions to the structural causal model formalism in order to tackle these challenges. This augments these models to possess greater levels of modularisation and encapsulation, as well presenting a constant space temporal causal model representation. While not an extension itself, we also prove through the extensions we have introduced that dynamically mutable sets can be captured within structural causal models while maintaining a form of causal stationarity. Finally we introduce two case study architectures demonstrating the application of these extensions along with a discussion of where these extensions could be utilised in future work.

Problem

• Integrate causal models into autonomous vehicles for safer interactions.
• Extend structural causal models to handle dynamic, temporal systems.
• Enable modular, encapsulated causal reasoning in autonomous vehicle systems.

Innovation

• Extends structural causal models for autonomous vehicles
• Introduces temporal causal model with constant space
• Enables dynamic mutable sets in causal models