Agent Planning & Reasoning

How agents think before they act — classical planning (STRIPS/PDDL), hierarchical task networks, goal decomposition, and chain-of-thought reasoning for autonomous agents.

Overview

Planning turns goals into action sequences. Reasoning allows agents to handle uncertainty, incomplete information, and adapt plans when the world changes.

    ┌─────────────────────────────────────────────────┐
    │                 Planning Pipeline                 │
    │                                                   │
    │  Goal ──> Decompose ──> Search ──> Plan ──> Act  │
    │                          │                        │
    │                    ┌─────┴──────┐                │
    │                    │  Operators  │                │
    │                    │ (actions +  │                │
    │                    │ preconditions│               │
    │                    │ + effects)  │                │
    │                    └────────────┘                 │
    │                                                   │
    │  Reasoning Layer:                                │
    │    Beliefs ──> Inference ──> Updated Beliefs     │
    │    Evidence ──> Rules ──> Conclusions            │
    └─────────────────────────────────────────────────┘

Key Concepts

Concept	Description
STRIPS Planning	Classical planning with preconditions and effects
HTN (Hierarchical Task Network)	Decompose abstract tasks into primitive actions
Forward/Backward Chaining	Search from initial state forward or goal backward
Goal Decomposition	Break complex goals into manageable sub-goals
Plan Repair	Fix broken plans instead of replanning from scratch
Chain-of-Thought	Step-by-step reasoning for complex decisions

Examples

File	Description
01_strips_planner.py	Classical STRIPS-style planner for a blocks world
02_htn_planner.py	Hierarchical task network for a cooking recipe domain
03_chain_of_thought.py	Chain-of-thought reasoning agent that shows its work

Best Practices

1. Define clear state representations — planning quality depends on good abstractions
2. Keep operator libraries modular — each action should have well-defined pre/post conditions
3. Use hierarchical decomposition for complex domains — flat planning doesn't scale
4. Implement plan monitoring — detect when a plan fails and trigger replanning
5. Combine planning with learning — learn which plans work and reuse them
6. Add reasoning traces — make agent thinking transparent for debugging

References

• Significant-Gravitas/AutoGPT — Autonomous planning agent
• yoheinakajima/babyagi — Task-driven autonomous agent
• microsoft/autogen — Multi-agent conversation with planning
• AIMA Python — AI textbook planning algorithms
• pyperplan — Lightweight PDDL planner

Author

Dhiraj Singh

Usage Notice

This repository is shared publicly for learning and reference. It is made available for everyone through VAIU Research Lab. For reuse, redistribution, adaptation, or collaboration, contact Dhiraj Singh / VAIU Research Lab.