Why Multi-Agent Systems will Make AI Better
We live in a time where pattern recognition AI is commonplace. All major websites on the Internet use this technology with Google using it for its search algorithm. However, pattern recognition is a basic application of AI which doesn’t justify what AI is supposed to be. However, we’ve got to a point in AI’s life cycle where it is feasible to create a learning AI that performs tasks according to everything it’s learned.
While learning AIs still seem like a futuristic concept, they are not. Many high-profile businesses have already started applying it to their businesses. In essence, learning AI is just patterned AI recording what it has predicted previously and applying it to a new problem. However, learning AI brings a new aspect to artificial intelligence that has never been seen before. That aspect is multi-agent systems.
What is a Multi-Agent System(MAS)?
A multi-agent system is simply is a form of M2M communication. To elaborate, a Multi-Agent System is a loose ecosystem of various communicating Artificial Intelligences. It is essentially the next iteration of agent-based systems.
Agent-Based systems(ABS) are communicating systems of distributed AI. They work by communicating with each other on set rules and constraints to solve a common problem. However, agent-based systems usually consist of one learning AI agent and other Pattern AIs or simply is an ecosystem of connected Pattern AIs.
Multiple learning Agent AIs can exist in an agent-based but they never directly communicate with each other. A multi-agent system, on the other hand, is an agent-based system that involves direct communication between two or more sets of learning AIs with minimum rules.
This form of M2M communication allows learning AI to actually ‘communicate’ and solve complex problems faster. There are various advantages of MAS over other forms of AI ecosystems.
Advantages of a Multi-Agent System
This is probably the greatest advantage of a multi-agent system. All agent-based systems are constrained by rules which make centralize them and free communication between agents and general Patter AI is usually not possible unless the learning agent AI has been designed to counteract this.
The centralized system means that even if one AI system fails, the entire ecosystem will crash. This is exactly why agent-based systems are designed to prevent direct communication between agents. A MAS can prevent this from happening by making all agents independent of each other and the failure of one agent doesn’t mean that the other will fail too. A failure in one agent would simply mean that the communication lines between the agents would break and the surviving agent would make decisions according to its algorithms.
Once multi-agent systems are successfully applied, they can even communicate with legacy systems and legacy systems could be connected to MAS ecosystems without major trouble. A simple MAS ‘skin’ would help them communicate saving a lot of time and resources.
ABS requires consistent human input to get the desired result whereas MAS systems can be completely autonomous while communicating and generating results.
Multi-Agent systems are relatively robust, reliable, efficient and provide better solutions than agent-based systems. There is simply no competition for this technology in AI.
General Feasibility of a Multi-agent System
The main lesson of thirty-five years of AI research is that the hard problems are easy and the easy problems are hard.
While multi-agent systems sound easy on paper since its just a communication between advanced learning AIs, the quote above pretty much sums the problem with a MAS. Currently, MAS is still being researched and except for scholarly projects, the industrial application of a multi-agent system is a few years away. However, companies are investing heavily in developing MAS and it is an investment bound to pay off. The current challenges faced by MAS are mentioned below.
The Limited Perception of Learning AI
All learning AI ‘remember’ things based on the problems they face and each AI has a different perception of solving a problem. This creates problems while communicating and the clash of perception leads to multiple solutions that break communication between two AIs. Working around this is tricky which is why creating a multi-agent system is difficult.
The ‘Gossip’ problem
Suppose the existence of n AI. Each AI knows something unique but when each AI communicates they end up ‘talking’ to each other which shares the information to all the AI. This increases the load on the algorithm making it more complicated since every AI’s node will have to be fully explored before a solution is found. This compromises the optimization of AI algorithms.
The good Ol’ ‘it’s not human’
This is the problem that has plagued AI from the beginning of its creation. What two AI must do in a multi-agent system is simple communication. What is easy for humans is always harder for machines.
MAS can only be feasible for all industries if special protocols are developed for it. It will be hard for MAS to work under current data-driven protocols. Heavy research is already underway for this technology.
Industries that will benefit from a Multi-Agent System:
Game engines use multi-agent systems especially for skilled bots. Bots are becoming more common in gameplay. A multi-agent system can improve graphics by communicating with different game mechanics of a game.
Travelling and logistics
Logistics could use multi-agent systems to improve efficiency and speed. A multi-agent system will also ensure better logistics and prevent discrepancies across systems by streamlining communication lines.
Networking & mobile technology
While networking is usually quite efficient, implementing a multi-agent system will allow the creation of automatic & dynamic load balancing, high scalability, and self-healing networks.
Along with IoT ecosystems, a MAS would ensure that all manufacturing units will have similar standards.
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