Add ERC: Instinct-Based Automatic Transactions

ERCS/erc-0000.md

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+---
+eip: 0000
+title: Instinct-Based Automatic Transactions
+description: A blockchain framework for autonomous transaction execution based on predefined AI-driven instincts with temptation values.
+author: James Savechives (@jamesavechives) <[email protected]>
+discussions-to: https://ethereum-magicians.org/t/erc-0000-ai-driven-instinct-based-automatic-transactions/20725
+status: Draft
+type: Standards Track
+category: ERC
+created: 2024-08-19
+---
+
+## Abstract
+
+This ERC proposes a standard for AI-driven automatic transactions on the Ethereum blockchain, triggered by predefined or dynamic "instincts" with associated temptation values. AI agents or users autonomously evaluate and act upon these instincts, optimizing their strategies through a learning process. This standard enables the creation of a self-regulating, adaptive blockchain ecosystem where decisions and transactions are made based on calculated rewards and penalties.
+
+## Motivation
+
+As AI and blockchain technologies evolve, the need for autonomous systems that can make complex decisions and execute transactions without direct human intervention becomes more apparent. This standard aims to provide a framework for integrating AI-driven instincts into the blockchain, allowing for more sophisticated, adaptive, and autonomous transaction mechanisms that mimic natural decision-making processes.
+
+## Specification
+
+### Instinct Definition
+
+1. **Instinct Attributes**:
+   - `instinctID`: Unique identifier for the instinct.
+   - `temptationValue`: Numerical value representing the instinct's temptation level (positive for rewards, negative for penalties).
+   - `triggerConditions`: Conditions under which the instinct is activated.
+   - `static`: Boolean indicating whether the instinct is static or dynamic.
+   - `autoExecution`: Boolean indicating whether the transaction should be automatically executed when the conditions are met.
+
+2. **Mid-way Targets**:
+   - Mid-way targets are generated dynamically as intermediate steps toward achieving the final instinct.
+   - Each mid-way target has its own `temptationValue` and `triggerConditions`.
+   - These targets guide the AI agent through a series of decisions and actions leading to the final instinct.
+
+3. **Instinct Management**:
+   - `createInstinct(instinctID, temptationValue, triggerConditions, static)`: Creates a new instinct.
+   - `modifyInstinct(instinctID, temptationValue, triggerConditions)`: Modifies an existing instinct.
+   - `deleteInstinct(instinctID)`: Deletes an instinct.
+
+### AI-Agent Interaction
+
+1. **Evaluation and Action**:
+   - `evaluateInstincts(agentID, timeFrame)`: Evaluates all available instincts within a given time frame and returns the optimal action.
+   - `actOnInstinct(instinctID, agentID)`: Executes the action associated with a selected instinct.
+
+2. **Automatic Execution**:
+   - If `autoExecution` is enabled and the conditions for an instinct or mid-way target are met, the transaction is executed automatically without further input from the agent.
+
+3. **Learning Process**:
+   - `labelInstinct(instinctID, cost)`: Labels the cost of pursuing a particular instinct to inform future decision-making.
+   - `updateStrategy(agentID)`: Updates the agent's strategy based on past outcomes and cost analyses.
+
+### Dynamic Instincts
+
+1. **Instinct Updates**:
+   - `updateInstinct(instinctID, newTemptationValue)`: Updates the temptation value or conditions of a dynamic instinct based on real-time data or external conditions.
+
+## Rationale
+
+The proposed standard introduces a novel approach to blockchain transactions by leveraging AI-driven instincts that enable autonomous decision-making. By incorporating temptation values and learning processes, the system can dynamically adapt to changing environments, offering a more efficient and self-regulating ecosystem.
+
+## Test Cases
+
+1. **Instinct Creation and Modification**: Test the creation, modification, and deletion of instincts.
+2. **AI-Agent Decision-Making**: Test the evaluation and action process for AI agents based on available instincts.
+3. **Automatic Execution**: Test the automatic execution of transactions when conditions for an instinct or mid-way target are met.
+4. **Learning and Strategy Update**: Test the learning process and strategy updates based on labeled costs and past actions.
+5. **Dynamic Instinct Adjustment**: Test the updating mechanism for dynamic instincts based on external data or real-time conditions.
+
+## Security Considerations
+
+1. **Instinct Integrity**: Ensure that instincts and their attributes are immutable and protected against tampering.
+2. **AI-Agent Security**: Implement safeguards to prevent AI agents from being manipulated into making harmful or suboptimal decisions.
+3. **Data Privacy**: Ensure that the data used to trigger instincts and guide decision-making is securely handled and protected.
+
+## Copyright
+
+Copyright and related rights waived via [CC0](../LICENSE.md).
+
+---
+
+### About the Author
+
+James Savechives is a blockchain enthusiast and developer with a keen interest in digital assets and their innovative applications. With a background in software development, James is currently working on the Aizel Network, aiming to explore and create solutions that bridge the gap between technology and practical applications.