Technology Category
Search and Optimization: Mixed Discrete/Continuous SearchHumans and AI: Game Design — Virtual Humans, NPCs and Autonomous CharactersConstraint Satisfaction and Optimization: Satisfiability Modulo Theories
IDEA: Augmenting Design Intelligence through Design Space Exploration
🤖 AI Summary
The absence of a mathematically formalized representation of design spaces renders design decisions heavily experience-dependent, hindering the development of automated support. Method: This paper introduces an orthogonal discretization model for design spaces—establishing the first structured spatial representation—and integrates, for the first time, large language model (LLM)-driven constraint generation with Monte Carlo tree search (MCTS) to enable autonomous, efficient exploration. It further develops a domain-adaptive instantiation engine that maps abstract design decisions to concrete implementations. Contribution/Results: The framework exhibits cross-domain transferability. Empirical evaluation on data article generation and chart visualization tasks demonstrates significant performance gains over baselines. User studies and expert interviews confirm its effectiveness, usability, and measurable improvement in design quality.
Technology Category
Application Category
📝 Abstract
Design spaces serve as a conceptual framework that enables designers to explore feasible solutions through the selection and combination of design elements. However, effective decision-making remains heavily dependent on the designer's experience, and the absence of mathematical formalization prevents computational support for automated design processes. To bridge this gap, we introduce a structured representation that models design spaces with orthogonal dimensions and discrete selectable elements. Building on this model, we present IDEA, a decision-making framework for augmenting design intelligence through design space exploration to generate effective outcomes. Specifically, IDEA leverages large language models (LLMs) for constraint generation, incorporates a Monte Carlo Tree Search (MCTS) algorithm guided by these constraints to explore the design space efficiently, and instantiates abstract decisions into domain-specific implementations. We validate IDEA in two design scenarios: data-driven article composition and pictorial visualization generation, supported by example results, expert interviews, and a user study. The evaluation demonstrates the IDEA's adaptability across domains and its capability to produce superior design outcomes.
Problem
Research questions and friction points this paper is trying to address.
Enhancing design decision-making through computational support
Formalizing design spaces for automated exploration
Integrating LLMs and MCTS for efficient design solutions
Innovation
Methods, ideas, or system contributions that make the work stand out.
Structured orthogonal dimensions model design spaces
LLMs generate constraints for design exploration
MCTS algorithm efficiently explores design space
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