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Introduction: What Are AI Agents
Artificial intelligence (AI) is rapidly impacting lives and livelihoods, manifesting in the form of self-driving cars and facial recognition softwares. One of the most exciting areas of AI research is the development of AI agents.
AI agents are computer programs that can autonomously perform tasks on behalf of humans. They can be used to automate a wide range of tasks, from simple ones like scheduling appointments to complex ones like controlling robots.
There are many different types of AI agents, each with its own strengths and weaknesses. In this article, we will discuss the most common types of AI agents and their potential applications.
Types of AI Agents
1. Reactive Agents
Reactive agents are the simplest type of AI agent. They operate on a stimulus-response basis, meaning they only react to the current situation. Reactive agents do not have any memory of past events and cannot learn from their experiences.
An example of a reactive agent is a spam filter. A spam filter is designed to identify and block spam emails. It does this by looking for certain keywords and patterns in the email; if the email matches the criteria for spam, it is blocked.
How to employ reactive AI agents in blockchain: A great example of integrating AI with Web3 technologies is by using reactive agents to provide instant decision-making processes. For example, a reactive agent could respond to blockchain-based smart contracts to execute real-time actions like automated trading or supply chain tracking.
2. Model-Based Agents
Model-based agents are more sophisticated than reactive agents. They make decisions using smaller machine-learning models like Bayesian networks or Markov Decision Processes (MDPs), predicting outcomes based on a simplified model of the environment.
An example of a model-based agent is a chess-playing program. A chess-playing program uses its internal model of the chessboard to evaluate different moves and make the best decision.
How to employ model-based AI agents in blockchain: Model-based agents can use Web3 for enhanced data integrity and secure, decentralised data storage. For example, a model-based agent could rely on blockchain with AI to validate the accuracy of its internal model by cross-referencing with a decentralised oracle.
3. Goal-Based Agents
As the name suggests, goal-based agents have specific goals to achieve. The agent uses its internal model of the world to plan a sequence of actions that will lead to the goal.
Goal-based agents often use reinforcement learning (RL), where they learn optimal actions to achieve specific goals through trial and error. Depending on the complexity of the tasks, medium-to large-sized models can be employed.
An example of a goal-based agent is a robot cleaner. The robot uses its internal model of the room to plan a path that will allow it to clean every nook and cranny.
How to employ goal-based AI agents in blockchain: Goal-based agents can leverage Web3 to autonomously execute smart contracts once a goal is achieved. For instance, in decentralised autonomous organisations (DAOs), goal-based agents might be used to manage resources or execute voting mechanisms autonomously.
4. Utility-Based Agents
Utility-based agents may use multi-objective optimisation algorithms or advanced reinforcement learning models to maximise their utility functions. The utility function assigns a value to each goal, and the agent tries to achieve the goal with the highest value.
Take for instance, a financial trading system. The system uses its utility function to evaluate different trades and make the trade that is most likely to be profitable.
How to employ utility-based AI agents in blockchain: Utility-based agents could use Web3 with AI to interact with decentralised finance (DeFi) platforms, optimising trading strategies or investment portfolios based on real-time blockchain data. For instance, they could adjust investment strategies dynamically based on market conditions reflected in a blockchain ledger.
5. Learning Agents
Learning agents are the most sophisticated type of AI agent. They can learn from their experiences and improve their performance over time.
Learning agents often use complex, large models such as deep learning, large language models (LLMs), or generative adversarial networks (GANs) to improve their performance continuously.
An example of a learning agent is a self-driving car, which uses its sensors to collect data about its environment. This data is then used to train the car's AI algorithms. As the car collects more data, it becomes better at driving.
How to employ AI learning agents in blockchain: Learning agents can leverage Web3 with AI for secure and transparent training data sourcing, utilising decentralised oracles to obtain real-world data without relying on a central authority. This is crucial in fields like DeFi, where the accuracy of the learning model impacts financial decisions.
6. Collaborative Agents
Collaborative agents are AI agents that can work together to achieve a common goal.
They might use federated learning, where multiple agents learn collaboratively across decentralised datasets without sharing the actual data. This often involves medium to large models, especially when dealing with diverse datasets.
An example of a collaborative agent is a team of robots working together to build a house. Each robot has its own task to complete, but they all work together to achieve the overall goal of finishing the house.
How to employ collaborative AI agents in blockchain: Web3 enhances AI collaborative agents by providing decentralised platforms where multiple agents can share insights, vote on decisions, and execute actions in a trustless environment.
7. Autonomous Agents
Autonomous agents are AI agents that can operate without human intervention. They use large models, such as deep reinforcement learning, to navigate complex, dynamic environments without human intervention. They may also use hybrid models combining different learning techniques.
Similar to learning agents, autonomous agents can also be employed in the training of self-driving cars.
How to employ autonomous AI agents in blockchain: AI in Web3 can enable autonomous agents to operate securely in decentralised environments, such as autonomous vehicles using blockchain for secure data exchange, or drones using smart contracts for automated delivery tasks.
8. Intelligent Agents
Intelligent agents are the most advanced type of AI agent. They are capable of general intelligence, meaning they can learn and perform any task that a human can.
Intelligent agents would use very large, highly complex models, potentially combining various forms of deep learning, natural language processing (NLP), and reinforcement learning to achieve general intelligence.
There are no real-world examples of intelligent agents yet, but they are a major goal of AI research.
How intelligent AI agents can be employed in blockchain: Web3 can provide AI agents with the decentralised infrastructure necessary for the secure and transparent deployment of AGI systems, ensuring that their decision-making processes are transparent and verifiable. For example, an AGI might autonomously govern a DAO, ensuring decisions are made in the best interest of all stakeholders.
9. Social Agents
Social agents are AI agents that can interact with humans in a natural way. They often integrate large models like LLMs for NLP tasks, and smaller models for emotion recognition or user interaction optimisation.
An example of a social agent is a chatbot. Chatbots can be used to provide customer service or to answer questions.
How to employ social AI agents in blockchain: Social agents can weave in Web3 AI elements to ensure secure and private interactions, especially in decentralised social networks. For instance, a social agent could engage in privacy-preserving conversations on a blockchain-based social platform, where user data is protected by cryptographic techniques.
AI Agents Summary: Compare and Contrast
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In Closing
AI agents are a powerful tool that can be used to automate a wide range of tasks. Each type of AI agent, from reactive agents to social agents, has its own unique capabilities. Choosing the right type of agent for a particular task is essential.
AI agents are still a relatively new technology and there is still much that we do not know about them, for Web3 AI integrations.
It is important to consider the ethical implications of using AI agents. For example, it is important to ensure that AI agents are not used to discriminate against certain groups of people.
By considering these factors, we can help to ensure that AI agents are used not just efficiently, but for the good of society as a whole.
aelf is a trailblazing multi-chain blockchain framework designed to address the limitations of traditional blockchain systems using AI. Its unique architecture, featuring a mainchain and dAppChains, upgrades network performance. aelf 2.0 further elevates this by integrating AI for 'smarter' contract execution, gas usage prediction, and intelligent resource allocation on its AI blockchain.
*Disclaimer: The information provided on this blog does not constitute investment advice, financial advice, trading advice, or any other form of professional advice. aelf makes no guarantees or warranties about the accuracy, completeness, or timeliness of the information on this blog. You should not make any investment decisions based solely on the information provided on this blog. You should always consult with a qualified financial or legal advisor before making any investment decisions.
About aelf
aelf, an AI-enhanced Layer 1 blockchain network, leverages the robust C# programming language for efficiency and scalability across its sophisticated multi-layered architecture. Founded in 2017 with its global hub in Singapore, aelf is a pioneer in the industry, leading Asia in evolving blockchain with state-of-the-art AI integration to ensure an efficient, low-cost, and highly secure platform that is both developer and end-user friendly. Aligned with its progressive vision, aelf is committed to fostering innovation within its ecosystem and advancing Web3 and AI technology adoption.
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