Complex System Exploration with Interactive Human Guidance

2025-10-01

AI Summary

Exploring complex systems faces the “constrained diversity” challenge—arising from vast parameter spaces, highly nonlinear parameter-to-pattern mappings, and users’ prior expectations about specific emergent patterns. Method: We propose a “constrained diversity” framework that integrates user-defined explicit constraints with interactive human-in-the-loop collaboration to maximize pattern diversity efficiently within user-specified regions while preserving global coverage. Our approach innovatively incorporates human guidance into active sampling, jointly modeling nonlinear parameter–pattern mappings and enforcing constraint satisfaction through iterative feedback. Contribution/Results: The framework supports system-agnostic constraint specification and sample-efficient exploration. Experiments across diverse complex systems demonstrate significant improvements in both diversity and efficiency of pattern discovery within target regions, effectively balancing localized focus with global exploration.

Technology Category

• Search and Optimization: Sampling/Simulation-based Search
• Constraint Satisfaction and Optimization: Constraint Learning and Acquisition
• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval

Application Category

• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Economics, Online Markets and Human Computation: Fairness, privacy, and diversity in economic environments
• Search and Retrieval-Augmented AI: Assisted, interactive, and conversational search

Abstract

The diversity of patterns that emerge from complex systems motivates their use for scientific or artistic purposes. When exploring these systems, the challenges faced are the size of the parameter space and the strongly non-linear mapping between parameters and emerging patterns. In addition, artists and scientists who explore complex systems do so with an expectation of particular patterns. Taking these expectations into account adds a new set of challenges, which the exploration process must address. We provide design choices and their implementation to address these challenges; enabling the maximization of the diversity of patterns discovered in the user's region of interest -- which we call the constrained diversity -- in a sample-efficient manner. The region of interest is expressed in the form of explicit constraints. These constraints are formulated by the user in a system-agnostic way, and their addition enables interactive system exploration leading to constrained diversity, while maintaining global diversity.

Problem

• Addressing large parameter spaces in complex systems exploration
• Managing non-linear parameter-pattern mappings with user expectations
• Maximizing constrained pattern diversity through interactive guidance

Innovation

• Interactive human guidance for complex system exploration
• Constrained diversity maximization in user interest regions
• System-agnostic explicit constraints for pattern discovery