GPTales-InteractiveNPCs is an innovative RPG Maker project that harnesses the power of GPT technology and the advanced Cardinal System to breathe life into the game's non-playable characters (NPCs). By integrating advanced language models and the Cardinal System, an AI-driven overseer that bridges the game engine and NPCs, this project aims to create an immersive, dynamic, and interactive world where NPCs exhibit diverse personalities, react intelligently to player interactions, and contribute to the richness of the game's narrative.
The Cardinal System observes the game state and issues contextually relevant commands to the NPCs, managing their schedules, contextual decision-making, and interactions with the player character. Players will experience a more engaging and responsive environment, as the NPCs dynamically adapt their dialogue, behaviors, and stories, shaping the game world according to each player's choices and actions. GPTales-InteractiveNPCs redefines the boundaries of RPG Maker games by offering a uniquely lifelike and captivating experience for both seasoned players and newcomers alike.
- Smart NPC interactions using OpenAI's GPT
- Dynamic NPC socializing
- Realistic NPC schedules
- In-game economy driven by NPCs and player choices
- Cardinal System for context-aware NPC guidance and decision-making
- Detachable and universally applicable Cardinal System with translation layers for integration into any game engine
- Implement GPT into NPC logic
- Enable dynamic NPC social interactions
- Introduce daily NPC schedules
- Establish in-game economy
- Integrate Cardinal System for context-aware NPC behavior
- Create translation layers for Cardinal System compatibility with other game engines
- Node.js v14 or later
- RPG Maker MV or MZ
- Clone the repository:
git clone https://github.com/YourUsername/GPTales-InteractiveNPCs.git- Install the necessary dependencies:
cd GPTales-InteractiveNPCs
npm install- Set up your OpenAI API key:
Create a .env file in the project root directory and add the following line, replacing your_openai_api_key with your actual API key:
OPENAI_API_KEY=your_openai_api_key
- Follow the additional instructions provided for integrating the Cardinal System with your chosen game engine.
Title: An Overview of the Theoretical AI Project: A Cognitive Model Mimicking Human Mind Traits with Phase Disposition
The present paper delves into the development and functioning of a theoretical artificial intelligence system designed to emulate the various traits of the human mind. The primary goal of this project is to create an AI that exhibits a comprehensive understanding of cognitive processes, emotional responses, and decision-making capabilities akin to human beings. By examining the intricacies of the project, this paper sheds light on the AI system's unique features, such as Phase Disposition, and potential applications. The paper also explores the significance of hypervectors and the concept of cognition and thought as colliding clusters in extra dimensions within the AI model. Furthermore, the integration of sensory inputs and temporal events is discussed as a means to enhance the AI's understanding of the environment and the sense of self.
The field of artificial intelligence has witnessed significant advancements over the past few decades, paving the way for numerous applications across various domains. While AI systems have demonstrated remarkable proficiency in specific tasks, developing a system that emulates the complexity and adaptability of the human mind remains an elusive goal. This paper presents an overview of a theoretical AI project that aims to bridge this gap, incorporating a wide range of cognitive, emotional, and decision-making traits characteristic of the human mind, including the innovative process of Phase Disposition, as well as the integration of sensory inputs and temporal events.
Phase Disposition is a core process within the theoretical AI project that evaluates actions through the mind to determine their disposition and execution. The process involves passing the action through each trait of the mind, with each phase calling upon the model with a phase-specific vector store. Some of the phases include Long-Term Memory (LTM), Personality, Mindset, Short-Term Memory (STM), and the Mastering phase. The final phase, known as the Mastering phase, represents the final disposition of the action and calls the model with the master vector store, which can be interpreted as destiny or fate.
The theoretical AI project allows for the incorporation of additional phases, such as the Asimov Phase, a disposition phase trained on Asimov AI safety concepts. The inclusion of these additional phases enhances the AI system's adaptability and alignment with human values.
The integration of hypervectors and extra-dimensional cognition in the theoretical AI project plays a crucial role in the Phase Disposition process. Hypervectors, or high-dimensional vectors, enable efficient storage and manipulation of complex, high-dimensional data. Extra-dimensional cognition, on the other hand, provides a novel perspective on thought and learning, considering them as colliding clusters in extra dimensions. The combination of these concepts within the AI model allows for the development of an AI system that mirrors the intricacies of the human mind.
The theoretical AI project also incorporates sensory inputs and temporal events to enhance its understanding of the environment and the sense of self. The sensory inputs, such as hearing, sight, touch, taste, and smell, can be assigned to digital, virtual, simulated, or real-life sensor inputs. These sensory inputs are processed through specialized classes, such as Hearing, Sight, Touch, Taste, and Smell, which extend from a base Sense class. By ingesting temporally parallel channels of data from the character's body, the AI system can process sensory information in a manner akin to human perception.
Temporal events play a crucial role in the AI system's memory management, allowing it to maintain a temporal presence of events and a sense of self. Events in Short-Term Memory (STM) gradually become harder to remember, mimicking the memory decay process in humans. These events transition to Long-Term Memory (LTM) after approximately an hour of existence. The TemporalEvent class is used to create instances of events with their respective timestamps, which are then stored and managed within the AI system's memory structure.
The theoretical AI project presented in this paper represents a bold endeavor to develop an artificial intelligence system that emulates the complexity and adaptability of the human mind. The introduction of the Phase Disposition process, coupled with the integration of hypervectors, extra-dimensional cognition, sensory inputs, and temporal events, demonstrates the potential of interdisciplinary collaboration in pushing the boundaries of AI research and development. As the field of artificial intelligence continues to evolve, it is crucial to explore and integrate diverse concepts and approaches to unlock the full potential of AI systems and their applications across various domains.