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Distributed artificial intelligence systems were conceived as a group of intelligent entities, called agents, that interacted by cooperation, by coexistence or by competition. DAI is categorized into multi-agent systems and distributed problem solving. [3] In multi-agent systems the main focus is how agents coordinate their knowledge and ...
A multi-agent system (MAS or "self-organized system") is a computerized system composed of multiple interacting intelligent agents. [1] Multi-agent systems can solve problems that are difficult or impossible for an individual agent or a monolithic system to solve. [ 2 ]
Artificial intelligence engineering (or AI engineering) is a technical discipline that focuses on the design, development, and deployment of AI systems. AI engineering involves applying engineering principles and methodologies to create scalable, efficient, and reliable AI-based solutions.
In this domain, the problem of integrating various AI algorithms into a single intelligent system arises spontaneously, with blackboards providing a way for a collection of distributed, modular natural language processing algorithms to each annotate the data in a central space, without needing to coordinate their behavior.
Diagram of a Federated Learning protocol with smartphones training a global AI model. Federated learning (also known as collaborative learning) is a machine learning technique focusing on settings in which multiple entities (often referred to as clients) collaboratively train a model while ensuring that their data remains decentralized. [1]
In the field of finance, Parallel Intelligence has been employed for stock market prediction, fraud detection, and portfolio management. By integrating human financial expertise with AI algorithms that analyze vast amounts of data, Parallel Intelligence systems can enhance investment decision-making processes and mitigate risks. [3]
Federated learning is an adapted form of distributed artificial intelligence to training machine learning models that decentralizes the training process, allowing for users' privacy to be maintained by not needing to send their data to a centralized server. This also increases efficiency by decentralizing the training process to many devices.
In artificial intelligence, the distributed multi-agent reasoning system (dMARS) was a platform for intelligent software agents developed at the AAII that makes uses of the belief–desire–intention software model (BDI).