SPIDER is a graph‑driven geotechnical intelligence engine that fuses symbolic inference with cost‑aware planning. Heterogeneous inputs are parsed with NLP and mapped onto a domain symbol set, then reasoned over a knowledge graph using uniform‑cost search. A PostgreSQL‑backed dynamic memory enables human–AI collaboration and explainable workflows; computation blends Python/SymPy for exact algebra, fuzzy logic for uncertainty handling, and reinforcement learning for policy refinement.
Animation guide: Click nodes (Inputs, Parser/NLP, Dynamic Memory, Σ Pool, Registry, Reasoner, Output) to open deep‑dives. Pulsing wires indicate data flow; moving particles visualize token transfer. Use the × button or Esc to exit overlays.
Interactive walkthrough of symbolic planning, inference, and explainable reasoning across the system.
Tip: Nodes are clickable for deep‑dives • Press Esc to exit overlays.
SPIDER is a graph‑driven geotechnical intelligence engine that combines NLP‑based parsing, symbolic inference with SymPy, and cost‑aware planning over a knowledge graph via uniform‑cost search. A PostgreSQL‑backed dynamic memory supports human–AI collaboration and explainable AI, while fuzzy logic and reinforcement learning refine decisions under uncertainty.