Many people ask, what is the difference between Artificial Intelligence and distributed intelligence. And in this we will figure it out today. If you have a project or an interesting idea related to artificial intelligence that needs to be implemented, then ai development services will help you with distributed intelligence and AI-
What’s the Difference?
The answer is very simple. In fact, distributed artificial intelligence is part of artificial intelligence. Therefore, these are not opposite concepts. Now let’s learn the features of distributed intelligence among the riot police.
Distributed artificial intelligence is an area of AI dedicated to the study of the techniques and knowledge required to coordinate and distribute activities in a multi-agent environment.
There are two main directions of development:
- Collaborative Distributed Problem Solving (SCPD): Explore how certain sets of modules (or nodes) interact to share and share knowledge about a problem and to develop a solution.
- Multi-agent systems (SMA): the study of the coordination of intelligent behavior between a set of autonomous intelligent agents.
The main difference between both areas is the flexibility of coordination between agents. In SCPD, the interactions and tasks that each agent performs are predefined: there is a centralized plan for solving problems. There is usually a global oversight actor who centralizes partial results and data among the rest of the system. In contrast, in SMA, agents have a large degree of autonomy and can dynamically decide which interactions are appropriate, what tasks they should perform, who performs each task, and in addition, it is possible to maintain knowledge that is not globally consistent, even agents can support different global goals.
This definition makes it possible to distinguish between systems oriented towards global behavior, with fixed behavior of agents (SCPD), and systems oriented towards the behavior of individuals, which as a result received behavior from the system (SMA). From the point of view of society, it will be a choice between the state, which plans and regulates the behavior of people (which will be predictable), and in order to allow the system to act on the free initiative of the people.
The main problems that IAD studies and which are common to all systems:
- How to formulate, describe and assign tasks and synthesize results among a group of intelligent agents.
- How to train agents to communicate and interact: what languages or communication protocols to use, what and when they should bind, etc.
- How to ensure that agents act consistently when making decisions or taking actions, for example by adapting to the global consequences of local decisions and preventing unwanted interactions.
- How to teach agents to imagine and reason about the actions, plans and knowledge of other agents for coordination; how to talk about the status of your coordination process (start or end).
- How to recognize and reconcile conflicting points of view and intentions between multiple agents to coordinate their actions; how to synthesize points of view and results.
- How to use engineering techniques and design systems with IAD. How to develop SMA platforms and development methodologies with IAD tapeworms.
As you can see, distributed intelligence is directly related to artificial intelligence. And this is also a new section of technology that is just evolving. Therefore, in order not to face difficulties in this area, it is best to refer to https://unicsoft.com/blockchain-development/crypto-exchange-development-company/.