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    • Schemas
  • ⚙️JS SDK Reference
    • Installation
    • Get Methods
      • Verify a User's Altura Guard Code
      • Get User
      • Get Many Users
      • Get a User's Items
      • Get Item
      • Get Items
      • Get an Item's Holders
      • Get an Item's History
      • Get Collection
      • Get Collections
      • Get a User's Item Balance
      • Get a User's ERC20 Balance
      • Get a User's Native Token Balance
    • Developer Wallet Methods
      • Transfer ERC1155
      • Bulk Transfer ERC1155
      • Mint Additional Supply of ERC1155
      • Transfer ERC721
      • Transfer ERC20
      • Consume an Item (ERC1155)
    • Smart NFT Endpoints
      • Update an Item's Property
      • Add new Properties
      • Remove Properties
      • Bulk Update an Item's Properties
      • Switch an Item's Primary Image
      • Add a New Image
      • Update an Item's Name & Description
      • Update a Collections Metadata
    • Altura Guard II
      • Connect to a user's wallet
      • Revoke Connection
      • Check Connection
      • Transaction Requests
        • Submitting a signature request
        • Submitting a native transaction request
        • Submitting a contract transaction request
    • Connect to Web3 Wallet
    • Schemas
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On this page
  • Overview
  • Model Type & Architecture
  • Underlying Tech
  1. Altura Intelligence

NPC Engine

Overview

The NPC Engine employs advanced large language models (LLMs) combined with fine-tuned reinforcement learning agents to create adaptive, context-aware non-playable characters. These NPCs interact naturally with players, dynamically update their behaviors, and evolve their decision-making strategies based on player input and environmental stimuli.

Model Type & Architecture

  • Core LLM: At the heart is a state-of-the-art LLM, such as a transformer-based model, pre-trained on a diverse dataset of dialogues, game narratives, character interactions, and behavior scripts.

  • Reinforcement Learning (RL) Layer: NPC behaviors are refined through RL by simulating in-game scenarios. Rewards are defined by metrics like player engagement, quest completion rates, and narrative satisfaction. The RL layer helps NPCs adapt to new conditions and improve over time.

  • Memory Storage (Vector Databases): The NPC Engine uses vector databases to store NPC “memories” — embeddings of previous conversations, decisions, and player interactions. This enables long-term recall and context continuity. The engine retrieves these memories as needed, maintaining coherent character arcs.

  • Retrieval-Augmented Generation: When generating dialogues or actions, the engine looks up relevant context from the vector database. It then fuses the retrieved context with the core LLM’s output, ensuring each NPC’s behavior and personality feel consistent over time.

Underlying Tech

  • Language Modeling: Transformer-based LLMs (GPT-like architecture), trained on diverse in-game dialogues and lore.

  • Reinforcement Learning Training: RL from Human Feedback (RLHF), plus RL with simulation environments to align NPC behaviors with desired outcomes.

  • Vector Database: High-performance vector stores for fast similarity search, enabling retrieval of past interactions and learned behaviors.

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Last updated 3 months ago

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