AI agents framework

Tags

AI agents can be classified based on their purpose, behavior, and the techniques used to implement them. Here’s an overview of the most popular types of AI agents:

1. Reactive Agents

  • Description: These agents make decisions based on the current state of their environment. They lack memory and do not learn or adapt over time.
  • Examples:
    • Game bots: Agents in chess or tic-tac-toe games using simple algorithms.
    • Recommendation agents: Systems suggesting items based on the current session only.
  • Techniques:
    • State-action mappings (e.g., decision trees, finite state machines).

2. Deliberative Agents

  • Description: These agents have a goal-oriented architecture and use planning to achieve their objectives. They maintain an internal model of the environment.
  • Examples:
    • Autonomous robots: Path planning in robotic vacuum cleaners or delivery drones.
    • Virtual assistants: Systems like Siri or Alexa planning multi-step actions.
  • Techniques:
    • Search algorithms (e.g., A* algorithm).
    • Planning frameworks (e.g., PDDL - Planning Domain Definition Language).

3. Learning Agents

  • Description: These agents improve their performance over time by learning from experience. They can adapt to new environments or tasks.
  • Examples:
    • Recommendation systems: Netflix or Amazon learning user preferences over time.
    • Chatbots: Adaptive conversational agents like ChatGPT that use reinforcement learning from feedback.
  • Techniques:
    • Supervised learning (e.g., decision trees, neural networks).
    • Reinforcement learning (e.g., Q-learning, policy gradients).

4. Collaborative Agents

  • Description: These agents work alongside humans or other agents to complete tasks.
  • Examples:
    • Co-pilots: Tools like GitHub Copilot assisting developers.
    • Human-AI teams: Agents in customer support chat systems handing over complex queries to human operators.
  • Techniques:
    • Multi-agent systems.
    • Dialogue management and natural language understanding.

5. Proactive Agents

  • Description: These agents anticipate the needs or future states and act in advance to optimize outcomes.
  • Examples:
    • Predictive maintenance systems: Identifying potential failures in machinery before they occur.
    • Smart home systems: Adjusting thermostats based on anticipated user behavior.
  • Techniques:
    • Predictive modeling using machine learning.
    • Probabilistic reasoning (e.g., Bayesian networks).

6. Autonomous Agents

  • Description: Fully self-governing agents capable of making decisions and taking actions without human intervention.
  • Examples:
    • Self-driving cars: Navigating traffic and making decisions in real-time.
    • Financial trading bots: Operating in stock markets with minimal oversight.
  • Techniques:
    • Deep reinforcement learning (e.g., DDPG, PPO).
    • Multi-modal AI systems combining vision, NLP, and decision-making.

7. Social Agents

  • Description: Agents designed to interact with humans or other agents in socially intelligent ways.
  • Examples:
    • Customer service bots: Chatbots like Zendesk.
    • Companion robots: Robots like Pepper designed for social interaction.
  • Techniques:
    • Emotion recognition and sentiment analysis.
    • Natural language processing (e.g., transformer-based models).

8. Multi-Agent Systems (MAS)

  • Description: Systems involving multiple agents that interact, collaborate, or compete within an environment.
  • Examples:
    • Swarm robotics: Coordinated drones or robots in manufacturing.
    • Online multiplayer games: Game AI working collaboratively or competitively with other agents.
  • Techniques:
    • Game theory (e.g., Nash equilibrium).
    • Distributed reinforcement learning.

9. Embodied Agents

  • Description: Agents integrated into physical systems or robots that interact with the real world.
  • Examples:
    • Humanoid robots: Like Sophia by Hanson Robotics.
    • Industrial robots: Machines performing manufacturing tasks.
  • Techniques:
    • Robotics frameworks (e.g., ROS - Robot Operating System).
    • SLAM (Simultaneous Localization and Mapping).

10. Conversational Agents

  • Description: Agents designed for natural language communication, often in text or speech.
  • Examples:
    • Chatbots: Like ChatGPT, Bard, or Bing Chat.
    • Voice assistants: Amazon Alexa, Google Assistant.
  • Techniques:
    • NLP models (e.g., GPT, BERT).
    • Dialogue systems (task-oriented or open-domain).

11. Ethical Agents

  • Description: Agents that make decisions while considering ethical guidelines or societal norms.
  • Examples:
    • Healthcare bots: Ensuring patient data privacy and ethical recommendations.
    • Autonomous cars: Making ethically sound decisions in split-second scenarios.
  • Techniques:
    • Moral decision-making frameworks.
    • Rule-based systems augmented with ethical reasoning.

12. Utility-Based Agents

  • Description: These agents select actions based on maximizing a utility function that quantifies their preferences.
  • Examples:
    • Autonomous delivery systems: Optimizing for speed, cost, and safety.
    • Economic trading agents: Maximizing profit in auctions or trading platforms.
  • Techniques:
    • Optimization algorithms.
    • Multi-objective reinforcement learning.

Choosing the Right Type

The choice of AI agent depends on the problem domain, the environment in which the agent operates, and the expected level of autonomy, intelligence, and interactivity.

LinkedIn Reddit