Introducing Distributed Artificial Intelligence Trends
Distributed Artificial Intelligence (AI) is a subfield of AI devoted to researching and developing distributed solutions. It emerged in the 1990s as a subfield of AI that deals with the interaction of intelligent and distributed agents. However, it has received much attention in recent times because of its decentralized and distributed nature.
To make Artificial Intelligence more reachable and scalable, it needs to be distributed and decentralized. Recently, distributed AI systems have collaborated with Machine learning and Deep Learning Technologies, giving them a new dimension to explore. Today, we will discuss the latest trends in Distributed Artificial intelligence.
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Swarm Intelligence
Nature inspires swarm intelligence. We have seen in our environment how bees distribute their work among themselves. Similarly, it amazes one how a flock of birds communicates while flying in more than a hundred groups. A similar pattern is also visible in the colonies of ants. These all show the wisdom of crowd intelligence.
Swarm or collective intelligence consists of multiple decentralized agents(autonomous entities performing the task) capable of self-organization. Bloom(1995) first coined the term due to research on complex adaptive systems.
How does Swarm Intelligence Work?
Naturally, swarm intelligence happens through a process called stigmergy. It is a kind of natural mechanism for the coordination among the agents. The agent intentionally leaves a trace when a task is performed in the environment. The leftover trance then triggers another event. In this manner, the whole series of tasks are performed until the defined goal is achieved.
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Trending Swarm Optimization Technologies
This section will discuss some trending optimizing algorithms in swarm intelligence; these optimization strategies have made it an area of interest for researchers. Let’s see what amazes them:
Particle Swarm Optimization
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Inspiration of the technology: It is inspired by the social foraging behaviour of some organisms, such as birds' flocking behaviour and fishes' schooling behaviour. The way these organisms pass information while moving in the right directions in these swarms gives researchers opportunities to develop such systems artificially.
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Methodology: The main objective of this algorithm is to use all agents to locate the optima in a multidimensional space. This optimum is initially assigned any random position and velocity in the space, but as time elapses with exploration and exploitation, the optima are found. For a greater understanding, see the image below.
- Applications: The applications of Swarm Optimization are listed below.
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Dimensionality reduction in machine learning uses swarm-based dimensionality reduction, which uses the vectorized implementation of the PSO.
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PSO has also been used as an optimisation technique to hyperparameter-tune deep learning algorithms.
Ant Colony System
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Inspiration of Technology: It is inspired by the communication of the ants, which is done by using a harmonic chemical known as a pheromone. The agent’s probability of choosing the path is a function of the chemical intensity and the distance between the locations. This phenomenon of using pheromones for communication is one of the types of stigmergy. Researchers have developed a similar artificial structure for this phenomenon.
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Methodology: This strategy aims to use historical information and construct the solution for the individual agent using a probabilistic step-wise approach. The probability of selecting any component for constructing a solution depends on that component’s heuristic contribution to the overall cost function. Once the cost function is calculated, the history related to that path is also updated.
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Applications: This Optimization technique is heavily used in a field called “Swarm Robotics,” which studies how a large number of simple robotic agents can be developed such that the desired goal can be achieved by the collective behaviour of these agents using Ant colony optimization techniques.
Artificial Bees Colony Algorithms
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Inspiration: It is inspired by the natural communication and distribution within the beehive. The scout bees are sent from the hive to locate the nectar. They return to the hive and give information about its location, fitness, and distance of food using a waggle dance.
- Applications:
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The Artificial Bee Colony Algorithm can be used as an alternative to traditional gradient descent algorithms as the optimisation function.
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The Modified Version of the Artificial Bee Colony (ABC) can solve the clustering problem as an alternative to traditional clustering approaches.
Leading Players in Swarm Intelligence
Some notable organizations in this field are Unanimous AI, Augur, Estimize, Almanis, and Ace. Some of the leading players believe in the crowd’s wisdom, i.e., swarm intelligence.
Multi-Agent System
To understand Multi-agent, let’s first understand what an agent is in the case of MAS. An agent is a computer system capable of taking independent(autonomous) actions to perform a specific task on behalf of its user.
Multiple agents are trying to optimize their own long-term rewards by interacting with the environment and with each other. Multi-agent reinforcement learning for an uncertain world
How do Multi-Agent Systems work?
A multi-agent system consists of several agents interacting with one another. Generally, agents will interact with other agents to fulfil their user's goals by cooperating, coordinating, and negotiating. The difference between the Multi-agent system and the swarm intelligence is that we have homogeneous agents in later ones, but we have heterogeneous agents in multi-agent systems.
What’s Trending in Multi-agent Systems?
Research in multi-agent systems has its roots in the development of distributed systems; with time, it has given rise to many subfields, like game theory and negotiation theory. However, the increase in Machine learning and deep learning technologies has allowed MAS to explore new dimensions. We will discuss these new trends.
Multi-agent Reinforcement Learning
Multi-agent Reinforcement Learning (MARL) aims to develop multiple reinforcement learning agents (these agents will use deep learning while performing the task). These agents learn by dynamically interacting with their environment. One can understand them as using enforcement learning (a Machine learning technique) in a multi-agent setup.
MARL allows multiple agents to learn, collaborate, and interact collectively.
Applications of Multi-agent Reinforcement Learning
MARL is a new field of research. Let’s see some promising and fascinating potential use cases of MARL
- It can be used to allocate online resources in a computing network.
- Research is going to develop applications of MARL in cellular Network optimization.
- Last but not least, in the future, smart traffic control systems can be developed using MARL.
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Agent-Based Modelling
ABM can be thought of as part of the multi-agent systems as both have distributed autonomous agents designed to interact within an environment. Still, one crucial difference is that ABM is generally designed to simulate the system’s behaviour. At the same time, MAS is used to solve a specific engineering problem.
Why to use Agent-Based Modelling?
Simulations model created Using ABM can be used for the following purposes:
- For a Depth Understanding of the system
- For testing the coherence and comprehensiveness of the system
- For Validation of theory against real data
- For Making predictions
ABM in Working- Modeling Big Data of Organizations
Today, every organization has large volumes of data, and organizations are eager to analyze this data to gain fruitful insights and make smart decisions.
CRM, ERP, HR, and other databases are the data sources for this valuable information. ABM can be used to put that information to work and use simulation models; the insights can be delivered from the data, which can be used for forecasting, decision-making, etc.
Federated Learning
Traditionally, machine learning approaches are based on centralizing the training data in one place(a machine or a data centre). But the idea of federated learning enables to allow the collaborative learning from distributed devices across the users.
How does Federated Learning work?
Federated Learning works like this: Each distributed device(say a smartphone) gets the current model. The model improves by learning the data on the device; it generalizes itself and updates its weights and biases. The weights and biases are sent to the cloud in an encrypted manner after this cloud is updated with the weights coming from all the distributed devices, which updates the shared model.
See the image below to understand the flow
Working with Federated learning
Smartphones update the weights locally according to personal data(A). Many users update it (B) Change is done on the cloud the shared model(C) the same is repeated again
Why Federated Learning?
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Federated Learning enables the device’s data to remain on the device, protecting the user’s privacy.
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Federated Learning provides a favourable environment for the collaborative learning of shared models rather than a generalized pre-trained model; the model learns with time and with the collaborative effort of all distributed devices as agents.
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It significantly reduces the latency as the model remains on the device, reducing the dependency on the central cluster.
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The hardware infrastructure dependency required to get the data in a single place is also reduced to a minimum level as the weights are sent that are the results of the training.
Conclusion
We have seen the latest trends in Distributed AI, though these trending technologies are in their infancy stage. At the pace with which work is going in these domains, technology will make distributed Artificial intelligence feasible and state-of-the-art solutions very shortly.
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