Glossary: Key Terms and Concepts

Swarm Consensus A decentralized decision-making process where agents collaborate to propose, vote on, and finalize tasks or actions. Decisions are made based on a predetermined consensus threshold, ensuring autonomy, fault tolerance, and the absence of central authority in agent-based systems.

Reinforcement Learning (RL) A machine learning paradigm where agents learn optimal behaviors by interacting with their environment. Through feedback in the form of rewards or penalties, agents refine their decision-making strategies to maximize cumulative rewards in dynamic and complex scenarios.

IPFS (InterPlanetary File System) A decentralized file storage and retrieval system that ensures content integrity and availability. Files are stored as immutable objects identified by unique hashes, enabling distributed sharing and retrieval without centralized control.

Blockchain Integration The incorporation of blockchain technologies, such as Ethereum and Solana, to enable secure, transparent, and tamper-proof operations. This integration facilitates use cases like task logging, decentralized voting, and trustless coordination among distributed agents.

Task Scheduler A system component responsible for dynamic task allocation, prioritization, and distribution among agents. By optimizing resource utilization, the scheduler ensures tasks are executed efficiently while balancing workloads across the swarm.

Knowledge Graph A structured representation of information that connects entities (concepts) through defined relationships. By capturing attributes and interconnections, knowledge graphs enable agents to perform advanced reasoning, querying, and decision-making.

Multi-Modal Capabilities The ability of agents to process and synthesize data from multiple formats—such as text, images, and audio—enhancing decision-making with richer contextual understanding and more sophisticated problem-solving abilities.

Redis An in-memory key-value store employed for high-speed data operations in the Axion Framework. Redis supports task queues, voting mechanisms, and swarm behavior coordination, with features like atomic operations and fast access times, making it ideal for real-time distributed systems.

Federated Learning A collaborative machine learning approach where agents train models collectively without sharing raw data. Model updates are exchanged instead, preserving data privacy while leveraging the collective intelligence of the swarm.

Lua Scripts A lightweight scripting language used within Redis to perform atomic operations directly on the server. This minimizes network overhead and ensures high-concurrency performance, optimizing distributed system tasks such as task queuing and voting.

Agent Collaboration A feature enabling agents to work together by sharing knowledge, delegating tasks, and communicating in decentralized networks. This fosters coordination, mutual benefit, and increased efficiency in distributed systems, supporting teamwork and complex problem-solving at scale.