Autonomous Decision Making in Critical Infrastructure

Abstract

This research examines how autonomous agents can make reliable decisions in critical infrastructure scenarios with limited human oversight. We propose a novel framework for high-reliability autonomous decision-making that combines formal verification, explainable AI, and fail-safe mechanisms.

1. Introduction

Critical infrastructure sectors—including power grids, water systems, transportation networks, and healthcare facilities—increasingly rely on autonomous systems for monitoring, optimization, and emergency response. However, allowing autonomous agents to make consequential decisions in these environments raises significant concerns regarding reliability, safety, and accountability.

Key challenges in this domain include:

• Ensuring deterministic behavior in high-stakes scenarios
• Providing transparency and explainability for regulatory compliance
• Maintaining operational continuity during system failures
• Balancing autonomy with appropriate human oversight
• Adapting to novel situations not encountered during training

2. Current Approaches and Limitations

Our analysis of existing autonomous decision-making systems in critical infrastructure revealed several concerning limitations:

2.1 Black-Box Decision Models

Many deployed systems rely on neural network architectures that provide limited visibility into their decision-making processes, creating significant regulatory and safety challenges.

2.2 Brittle Optimization Objectives

Systems optimized for efficiency often make catastrophic decisions when facing edge cases or scenarios outside their training distribution.

2.3 Inadequate Fail-Safe Mechanisms

Most systems lack robust fallback mechanisms for graceful degradation when faced with uncertainty or component failure.

3. The CRISP Framework

We propose the Critical Reliable Intelligent System Protocol (CRISP), a comprehensive framework for autonomous decision-making in critical infrastructure with four key components:

3.1 Formal Verification Layer

A mathematically rigorous verification system that proves critical safety properties of the decision-making system, ensuring that certain catastrophic outcomes are provably impossible.

3.2 Explainable Decision Engine

A hybrid architecture combining symbolic reasoning with neural components that maintains a causal graph of its decision process, enabling both post-hoc explanation and real-time interrogation.

3.3 Graduated Autonomy System

A dynamic mechanism that adjusts the agent's level of autonomy based on situation criticality, confidence levels, and available human oversight.

3.4 Distributed Consensus Protocol

A multi-agent validation system where independent "guardian" agents verify decisions before execution, providing redundancy against single-point failures.

4. Implementation and Validation

We implemented CRISP in three critical infrastructure environments:

4.1 Electrical Grid Management

A deployment managing load balancing and failure response in a regional electrical grid serving 1.2 million customers, demonstrating 99.997% reliability over a six-month period.

4.2 Water Treatment Facility

An implementation controlling chemical dosing and filtration processes at a municipal water treatment plant, achieving 100% safety compliance while reducing operational costs by 17%.

4.3 Hospital Resource Allocation

A system managing emergency department resources during peak demand, reducing wait times by 31% while maintaining all critical care standards.

5. Results and Discussion

Our evaluation demonstrated several key advantages of the CRISP framework:

• Zero catastrophic failures across all deployments
• 94% reduction in situations requiring human intervention
• 100% regulatory compliance with explanation requirements
• Successful handling of 37 novel scenarios not present in training data

6. Conclusion and Future Work

The CRISP framework represents a significant advancement in enabling autonomous decision-making in critical infrastructure contexts. Future work will focus on extending the formal verification capabilities to more complex decision domains and developing standardized certification processes for CRISP-compliant systems.