3. METHODOLOGY: THE NEURO-SYMBOLIC BRIDGE Figure 1. The MSA Neuro-Symbolic Workflow. The architecture decouples semantic reasoning (The Brain) from haptic execution (The Body). The LLM translates natural language into a goal state, while the STRIPS planner queries the Knowledge Base to generate a deterministic sequence of actions. The Local Agent uses OpenCV to locate UI elements and injects hardware-level clicks, creating a closed-loop visual feedback system. The methodology unfolds in three stages: Semantic Parsing, Symbolic Planning, and Haptic Execution. 3.1 Stage 1: Semantic Parsing (LLM) The user provides a natural language instruction. An LLM (e.g., GPT-4 or a fine-tuned Llama 3) parses this intent and maps it to a specific Goal State defined in the Knowledge Base. For example, the command "Calculate the volume for Stope A" is translated to the goal state Volume_Report_Generated. The LLM acts strictly as a Semantic Parser. It is prompted with a Few-Shot learning technique containing the available "Goal States" from the Knowledge Base. When a user inputs: "Please update the block model and run the volume for Stope A", the LLM outputs a structured JSON payload: {"intent": "Calculate_Volume", "parameters": {"target": "Stope A"}}. Once this Goal State is identified, the LLM's job is done. The deterministic STRIPS planner takes over. By using a backward-chaining algorithm, STRIPS looks at the Goal (Volume_Calculated), checks its preconditions (Stope_Selected), and recursively searches for actions to satisfy them until it reaches the current Initial State of the software. This absolute separation guarantees zero hallucination during the execution phase. 3.2 Stage 2: Symbolic Planning (STRIPS) To prevent the "Hallucination Problem" common in GenAI, the execution logic is not generated by the LLM. Instead, the Orchestrator's planner performs a backward-chaining search: 1. It identifies an Action whose Effect matches the Goal. 2. It checks the Preconditions of that Action. 3. If preconditions are not met (e.g., "Topography not loaded"), those become new sub-goals. 4. This recursive process generates a guaranteed valid plan: Load_Topo -> Select_Stope -> Run_Calculation. 3.3 Stage 3: Visual Feedback Loop During execution, the Local Agent performs a "Closed-Loop" verification. Before clicking a button, it verifies the visual presence of the element with a confidence threshold (typically > 0.8). If the element is not found (e.g., the software is lagging), the agent waits or reports an error, preventing the "ghost clicking" issues typical of traditional macros. 4. CASE STUDY: LOGIC INJECTION AND EXECUTION VALIDATION While the ultimate target of the MSA architecture is complex mining packages (e.g., Vulcan, Leapfrog, Datamine), proprietary software licenses and End-User License Agreements (EULAs) often strictly prohibit unauthorized API hooking, reverse engineering, or automated scraping.
RkJQdWJsaXNoZXIy MTM0Mzk2