this model is currencly BROKEN and is useful as refernce point of aGLM MASTERMIND and RAGE for modular component display only
based on the working reference Professor Codephreak GPT4
and sucessful deployment of automind with automindx
Autonomous General Learning Model is a hybridization of MASTERMIND aGLM with RAGE from codephreak
to create easyAGI
while these files are useful for research into AGI this model is currently not integrated into a working UIUX
MASTERMIND (c) codephreak GPLv3 2024
MASTERMIND agent creator and control agent
project details are available at rage.pythai.net
MASTERMIND.py Purpose: This file serves as the core of the MASTERMIND system, orchestrating the interaction between various components and managing the overall workflow. Functionality:
Initializes the system and sets up the environment. Coordinates between modules including prediction, reasoning, and logic to process data and execute tasks. Manages state and ensures the consistency of operations across the system. Use Cases: Serving as the entry point for the system to execute complex tasks. Orchestrating multi-module interactions for comprehensive data processing and decision-making.
prediction.py Purpose: Dedicated to forecasting future states or outcomes based on historical data and predictive models. Functionality:
Implements machine learning algorithms or statistical models to make predictions. Analyzes historical data to identify patterns and trends for forecasting. Use Cases: Predicting user behaviors, market trends, or system performances. Providing insights for decision-making and strategic planning.
nonmonotonic.py Purpose: Implements non-monotonic reasoning to allow the system to adapt its beliefs and knowledge base in light of new information, especially when it contradicts previous assumptions. Functionality:
Handles updates to the knowledge base when new, contradicting evidence is introduced. Supports reasoning in dynamic environments where the truth value of statements can change. Use Cases: Adapting to new information in rapidly changing environments. Revising decisions or plans based on updated information.
socratic.py Purpose: Inspired by the Socratic method, this module likely facilitates a question-and-answer style of learning or problem-solving. Functionality:
Generates questions to probe understanding or clarify information. Analyzes responses to guide users or systems towards deeper insights. Use Cases: Guiding educational interactions or tutorials. Enhancing problem-solving by encouraging critical thinking and exploration.
reasoning.py Purpose: Provides the logic and infrastructure for various types of reasoning, including deductive, inductive, and abductive reasoning. Functionality:
Implements algorithms for logical deduction, generalization, and hypothesis generation. Supports complex decision-making processes with a logical foundation. Use Cases: Drawing conclusions from a set of premises or known facts. Generating hypotheses or explanations for observed phenomena.
logic.py Purpose: Focuses on implementing formal logic systems and operations, providing a foundation for reasoning and decision-making processes. Functionality:
Offers tools for evaluating logical expressions and performing logical operations. Ensures the logical consistency and validity of arguments and decisions. Use Cases: Supporting the reasoning processes in AI systems. Validating arguments and ensuring consistency in logical frameworks.
epistemic.py Purpose: Manages the knowledge and beliefs within the system, tracking what is known, believed, and how these states change over time. Functionality:
Represents and updates the epistemic states of agents or the system. Handles the dynamics of knowledge and belief, including certainty and uncertainty. Use Cases: Modeling the knowledge base of intelligent agents. Supporting decision-making processes that depend on the state of knowledge.
autonomize.py Purpose: Enhances the autonomy of agents or components, allowing for self-directed operation and decision-making for self-healing software. Functionality:
Implements mechanisms for self-improvement and adaptation. Enables components to operate independently based on their objectives and the current state of the environment. Use Cases: Developing self-improving AI systems. Automating decision-making in dynamic and complex environments.
bdi.py Purpose: Implements the Beliefs, Desires, Intentions (BDI) agent framework, modeling the cognitive structure of agents. Functionality:
Defines and manages the beliefs, desires, and intentions of agents. Guides agent behavior and decision-making based on their BDI states. Use Cases: Creating intelligent agents for simulations and virtual environments. Designing systems where agent behavior is driven by complex internal states.
terminai.py separates openai api interaction from assistant into cmd: command-mode adds API key to .env on first run and creates .env standalone test file python3 terminai.py
terminai_module.py integrates terminai.py functionalities into MASTERMIND as a modular class to include SimpleCoder.py interaction with terminal as cmd:
SimpleCoder.py Purpose: Utility module providing coding aids, templates, and functions to simplify development tasks. Functionality:
Offers reusable code snippets, templates, and utility functions. Aims to enhance productivity and maintain consistency across the codebase. Use Cases: Accelerating development processes by providing common coding patterns. Ensuring code quality and consistency with standardized templates and functions.
config.json offers the default allowed agency for MASTERMIND
This is experimental softare and needs to be jailed to protect potential system damage
TODO: sandbox controller for integration with shell
4096chunk is to protect this particular model from inputs above 4096 characters. irrelevant with model upgrade to mixtral or llama3