The study of AI agents reveals diverse levels of intelligence and functionality, from simple reflex responses to sophisticated multi-agent systems working towards shared goals.
Overview of Agents in AI #
Agents in AI are entities capable of perceiving their environment and taking action to achieve specific objectives. They vary significantly in complexity and intelligence, ranging from basic reflex mechanisms to advanced systems that plan and learn.
Classification Based on Perceived Intelligence and Capability #
Simple Reflex Agents #
Simple reflex agents operate based on condition-action rules. These agents function effectively in fully observable environments but face several limitations:
– Limited Intelligence: Only react to current percepts without considering the overall situation.
– Lack of Knowledge: Cannot account for elements not directly perceived.
– Large Rule Storage: Requires significant memory for storing numerous rules.
– Frequent Rule Updates: Must frequently update rules as the environment changes.
Next, we examine model-based reflex agents which build on this foundation by maintaining an internal state.
Model-Based Reflex Agents #
Model-based reflex agents enhance functionality by incorporating an internal model to track the world’s state, making them suitable for partially observable environments.
– Use rules matched to the current situation for decision-making.
– Maintain an internal state to reflect unobserved aspects.
– Update the state based on how the world evolves independent of the agent.
– Account for the effects of the agent’s actions on the environment.
Now, let’s move on to goal-based agents which introduce decision-making driven by objectives.
Goal-Based Agents #
Goal-based agents make decisions by evaluating how close they are to reaching their goals and selecting actions that minimize this distance.
– Choose actions to move closer to desired outcomes.
– Flexibly adapt behavior by modifying decision-support knowledge.
– Require search and planning capabilities.
– Easily adjust behavior to align with different goals.
Following this, utility-based agents add another layer by weighing multiple potential actions based on their utility.
Utility-Based Agents #
Utility-based agents enhance decision-making by evaluating the utility of different states, aiming to maximize overall satisfaction or happiness.
– Develop actions based on end uses.
– Weigh multiple alternatives before acting.
– Assign preferences (utility) to each possible state.
– Use a utility function to map states to levels of happiness.
The next section covers learning agents, which continuously improve by learning from past experiences.
Learning Agents #
Finally, we explore multi-agent systems where agents interact for shared objectives and hierarchical agents which organize tasks through a structured approach.
Multi-Agent Systems #
Multi-agent systems involve multiple agents working together toward common goals, requiring sophisticated coordination and communication.
– Homogeneous vs. Heterogeneous Agents: Differ in their designs and functionalities.
– Cooperative vs. Competitive Behavior: Vary in their interactions, either working together or competing.
– Implementation Techniques: Include game theory, machine learning, and agent-based modeling.
Hierarchical Agents #
Hierarchical agents are organized into multiple levels, with higher-level agents setting goals and constraints while lower-level agents execute specific tasks.
– Organize actions in a hierarchical structure for efficiency.
– Allow resource allocation according to task suitability.
– Improve decision-making through a multi-level approach.
– Suitable for complex environments requiring structured operations.