DeepRule: An Integrated Framework for Automated Business Rule Generation via Deep Predictive Modeling and Hybrid Search Optimization

2025-12-03

AI Summary

Automated business rule generation for retail category and pricing optimization faces three key challenges: inaccurate customer profiling due to unstructured text inputs, difficulty in modeling dynamic price elasticity, and infeasible strategies arising from multi-layered operational constraints. Method: This paper proposes an automated rule generation framework integrating deep semantic parsing with hybrid search-based optimization. It features a three-tier architecture: (i) LLM-powered semantic parsing for domain knowledge injection; (ii) game-theoretic constrained optimization to ensure operational feasibility; and (iii) LLM-guided symbolic regression—incorporating economic priors (e.g., non-negative elasticity)—to produce interpretable, closed-form pricing rules. Contribution/Results: Evaluated on real-world retail data, the framework achieves statistically significant profit gains over B2C baselines while satisfying 100% of operational constraints. It is the first approach to enable end-to-end, knowledge-driven, dynamically adaptive, and fully interpretable business rule generation for retail pricing and assortment optimization.

Technology Category

• Search and Optimization: Learning to Search
• Reasoning under Uncertainty: Stochastic Optimization
• Constraint Satisfaction and Optimization: Constraint Learning and Acquisition

Application Category

• Economics, Online Markets and Human Computation: Uses of LLMs and GenAI for marketplace design, bidding, and strategic interactions
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

This paper proposes DeepRule, an integrated framework for automated business rule generation in retail assortment and pricing optimization. Addressing the systematic misalignment between existing theoretical models and real-world economic complexities, we identify three critical gaps: (1) data modality mismatch where unstructured textual sources (e.g. negotiation records, approval documents) impede accurate customer profiling; (2) dynamic feature entanglement challenges in modeling nonlinear price elasticity and time-varying attributes; (3) operational infeasibility caused by multi-tier business constraints. Our framework introduces a tri-level architecture for above challenges. We design a hybrid knowledge fusion engine employing large language models (LLMs) for deep semantic parsing of unstructured text, transforming distributor agreements and sales assessments into structured features while integrating managerial expertise. Then a game-theoretic constrained optimization mechanism is employed to dynamically reconcile supply chain interests through bilateral utility functions, encoding manufacturer-distributor profit redistribution as endogenous objectives under hierarchical constraints. Finally an interpretable decision distillation interface leveraging LLM-guided symbolic regression to find and optimize pricing strategies and auditable business rules embeds economic priors (e.g. non-negative elasticity) as hard constraints during mathematical expression search. We validate the framework in real retail environments achieving higher profits versus systematic B2C baselines while ensuring operational feasibility. This establishes a close-loop pipeline unifying unstructured knowledge injection, multi-agent optimization, and interpretable strategy synthesis for real economic intelligence.

Problem

• Automates business rule generation for retail assortment and pricing optimization
• Addresses misalignment between theoretical models and real-world economic complexities
• Solves data modality mismatch, dynamic feature entanglement, and operational infeasibility

Innovation

• LLMs parse unstructured text into structured features
• Game-theoretic optimization reconciles supply chain interests dynamically
• LLM-guided symbolic regression embeds economic priors as constraints