What are model-based reflex agents?

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intelligence (AI) agents. They combine immediate reactions to stimuli with contextual awareness derived from an internal state of the environment.

These agents excel in scenarios that require dynamic decision-making, especially in fields such as natural language processing (NLP), where understanding context and adapting to new information is critical.

Unlike simple reflex agents (machine learning), which base their decisions on current inputs, model-based reflex agents use stored information about past states to make more informed decisions.

This approach allows them to adapt to changing or partially observable environments, often complementing hierarchical agents in complex systems to manage multi-level decision making.

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Key components of model-based reflex agents
Model-based reflex agents rely on multiple components to work together, execute actions, and enable adaptive decision making.

These components include:

Internal model of the environment: A representation of the external world that provides past states and current conditions
Condition-Action Rules : A set of predefined rules or correlations that guide the agent's actions based on specific conditions.
State Updater: Mechanisms that update the internal model as the environment changes
Sensors and actuators: Components that interact with the external environment to collect data and execute actions.
Utility Function : In specific scenarios, model-based reflex agents use a utility function to evaluate and rank possible actions based on their expected outcomes, allowing them to choose the most optimal response.
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