# Heirarchical Task Networks (HTN) ## What is HTN? Inspired by earlier planning systems like STRIPS ((Stanford Research Institute Problem Solver) which itself originated in 1971 as part of a research effort at the Stanford Research Institute (SRI)), Hierarchical Task Networks (HTNs) also appeared during the 1970s and expanded the concept by incorporating domain-specific knowledge and abstract goals. This made them particularly useful for addressing real-world problems that required breaking down large, complicated tasks into smaller, manageable steps. As time has passed, HTNs have proven invaluable across different domains, including: * **Robotics** * **Game Development** (providing NPCs with structured, realistic behaviors, in games such as *The Sims)* * **Operations and Logistics** * **Simulation and Training**: (for fields like disaster response, military training, and healthcare) ### **HTNs and AI Agent Swarms** Agent swarms benefit significantly from HTNs, as they bring a structured framework to the natural adaptability of swarms, helping align individual agent actions with broader objectives. *HTN Integration Overview:* 1. **Task Breakdown** HTNs decompose high-level goals into smaller subtasks. 1. **Agent Allocation** Subtasks are assigned to individual agents based on their specific roles, resources, or proximity. 1. **Real-Time Adjustment** Agents adapt to changing conditions using local feedback, ensuring flexibility and resilience. ### **The Role of HTNs in Web3 and AI** HTNs offer expansive possibilities for web3 AI and agentic solutions being built currently, especially those that rely on agent swarms. * **DAOs**: With a recent explosion in AI powered investment DAOs, HTN-driven swarms can manage resource allocation, treasury, governance, and decision-making. * **Gaming**: Massive virtual metaverse worlds and web3 games can use HTNs to manage NPCs, quests, and user-generated content. * **DeFi:** HTN powered agent swarms can handle tasks like liquidity management. ### HTN Example: **Optimizing Liquidity Management in DeFi** **Top-Level Goal:** “Optimize protocol liquidity to maximize yield and stability.” **Decomposed Subtasks via HTNs:** 1. **Monitor Market Conditions**: * Agents track on-chain data, price feeds from oracles, and lending rates to assess liquidity needs. 2. **Rebalance Liquidity Pools**: * Swarm agents shift liquidity between pools to optimize yields while maintaining risk thresholds. 3. **Manage User Incentives**: * Agents analyze user behavior and recommend adjustments to token rewards or staking rates to attract or retain liquidity. 4. **Flag and Mitigate Risks**: * Agents identify risky positions (e.g., low collateralization or high volatility) and execute preemptive measures, such as rebalancing or liquidation. **Swarm Execution:** * Each agent in the swarm is specialized for a particular task. * Some agents focus on “Monitor Market Conditions” by aggregating data from on-chain oracles. * Others handle "Rebalance Liquidity Pools" by submitting transactions to move assets between pools, governed by smart contracts. * Risk-focused agents continuously monitor user positions and flag critical scenarios for intervention. **Dynamic Adaptation:** If sudden market changes (like price volatility) occur, the agent swarm autonomously adjusts its strategy. For instance: * Liquidity may be shifted to stabilize underperforming pools. * Token incentives could be recalibrated to attract users to deposit more assets. * On-chain alerts are generated to inform governance token holders of potential risks. *Web3 systems become more autonomous, efficient, and capable of addressing complex, large-scale challenges with minimal human oversight by combining HTNs with the power of decentralized agent swarms, with the potential to drive the next wave of innovation in web3 AI.* ## HTN in Shadō Network