# Glossary **Key Terms and Concepts** * **Swarm Consensus**: A process where agents collaborate to make decentralized decisions. Tasks are proposed, voted on, and finalized based on a consensus threshold. * **Reinforcement Learning (RL)**: A machine learning technique where agents learn optimal behaviors by performing actions and receiving rewards or penalties. * **IPFS (InterPlanetary File System)**: A decentralized storage protocol that allows for sharing and retrieving immutable files across distributed networks. * **Blockchain Integration**: The use of blockchain networks, such as Ethereum and Solana, for secure, on-chain task logging, voting, and decentralized coordination. * **Task Scheduler**: A component that dynamically assigns and prioritizes tasks among agents, ensuring efficient resource utilization. * **Knowledge Graph**: A structured database that represents entities (concepts) and their relationships, enabling advanced reasoning and querying. * **Multi-Modal Capabilities**: The ability of agents to process and integrate data from multiple modalities, such as text, images, and audio, to enhance decision-making. * **Redis**: A fast, in-memory data store used in Aether for task queues, voting mechanisms, and swarm coordination. * **Federated Learning**: A collaborative machine learning approach where agents train models locally and share only the updates, preserving data privacy. * **Lua Scripts**: Lightweight scripts used in Redis to execute tasks atomically and optimize performance in high-concurrency scenarios. * **Agent Collaboration**: A feature enabling agents to share knowledge, delegate tasks, and communicate in decentralized networks. --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://aether-framework.gitbook.io/aetherframework/glossary.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.