KEPLER: A Method for Planning Solution Paths in Complex Problems

Complex problems often resist resolution not only because they are intrinsically difficult, but because the path from a promising local insight to a portable, defensible conclusion is left implicit. A claim may work inside the field in which it was first generated, yet fail when it crosses into a new regime of evidence, language, scale, cost, institutional constraint, or operational context. This paper introduces KEPLER — Knowledge Engine for Portable, Layered, Evidence-based Resolution — a method for planning solution paths in complex problems by treating solutions as governed journeys rather than isolated terminal answers. In KEPLER, knowledge denotes structured understanding that can be examined and transferred; engine denotes an explicit procedural discipline rather than an informal metaphor; portable denotes the requirement that a solution survive movement across contexts; layered denotes that such movement occurs across fields, regimes, scales, and boundary conditions; evidence-based denotes that promotion must remain grounded in auditable support; and resolution denotes justified arrival under declared constraints, rather than the mere appearance of an answer. The method models a solution path as a structured traversal across fields, layers, and border crossings, requiring explicit invariants, contracts, admissible transformations, receipts, and fail-closed promotion gates. The aim is not to replace proof, experiment, or domain expertise, but to add a disciplined layer for governing how partial truths travel. KEPLER is designed to reduce drift, overclaim, and false portability while improving auditability, replayability, and the cumulative value of intermediate progress. The method is intended for high-complexity settings in science, engineering, AI, architecture, and policy, especially where legal, ethical, or safety constraints require conservative promotion and explicit accountability.