Hybrid-Agent Architectures: TRIP-PAL & GenPlanX
Published:
Pillar: Hybrid-Agent Systems
Overview
Large Language Models excel at understanding unstructured natural language and capturing user preferences, but they fundamentally struggle with arithmetic reasoning, temporal scheduling, and formal constraint adherence.
In projects like TRIP-PAL (Travel Planning with Guarantees) and GenPlanX, we architected hybrid neuro-symbolic systems:
- LLM Frontend: Handles natural language extraction, conversational preference elicitation, and entity recognition.
- Formal Translation Layer: Maps extracted constraints and objectives into standard planning representations (PDDL/RDDL).
- Symbolic Solver: Classical planners (e.g. Metric-FF, Fast Downward, CPLEX) compute provably optimal and constraint-respecting plans.
- Explainable Feedback: Dispatches verified schedules back to the user with full mathematical correctness guarantees.