Use Cases of Intelligent Agents

Intelligent Agents play a pivotal role in various real-world applications, leveraging their autonomous capabilities to enhance efficiency, decision-making, and user experience across multiple domains. Below are detailed use cases for each type of intelligent agent, supported by relevant references.

• Simple Reflex Agents

Use Case: Automated Traffic Light Control

Description: Simple reflex agents can be used in automated traffic light control systems where the agent changes the light based on the current state of traffic detected by sensors. Each sensor input triggers a specific action, such as switching the light from green to red.

Reference: Wiering, M. (2000). “Multi-Agent Reinforcement Learning for Traffic Light Control.” Proceedings of the International Conference on Machine Learning (ICML).

• Model-Based Reflex Agents

Use Case: Smart Home Systems

Description: Model-based reflex agents in smart home systems can maintain an internal model of the home environment. These agents can control heating, lighting, and security systems by predicting future states based on current and past data.

Reference: Cook, D. J., & Das, S. K. (2007). “How Smart are Our Environments? An Updated Look at the State of the Art.” Pervasive and Mobile Computing.

• Goal-Based Agents

Use Case: Autonomous Drones for Delivery

Description: Goal-based agents can be employed in autonomous drones for delivery services. These agents determine the optimal path and actions needed to reach the delivery destination while avoiding obstacles and minimizing travel time.

Reference: Dorling, K., Heinrichs, J., Messier, G. G., & Magierowski, S. (2017). “Vehicle Routing Problems for Drone Delivery.” IEEE Transactions on Systems, Man, and Cybernetics: Systems.

• Utility-Based Agents

Use Case: Personalized Healthcare Management

Description: Utility-based agents can be used in personalized healthcare management systems to optimize treatment plans based on the patient’s health data. These agents evaluate the utility of different treatments considering factors like efficacy, side effects, and patient preferences.

Reference: Wang, F., et al. (2018). “A Personalized Healthcare Monitoring System for Elderly People.” IEEE Transactions on Mobile Computing.

• Learning Agents

Use Case: Autonomous Vehicles

Description: Learning agents in autonomous vehicles can improve driving performance over time by learning from experiences. These agents utilize reinforcement learning to make decisions in complex traffic scenarios, enhancing safety and efficiency.

Reference: Kiran, B. R., et al. (2021). “Deep Reinforcement Learning for Autonomous Driving: A Survey.” IEEE Transactions on Intelligent Transportation Systems.

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Published By: Aize Perez