Artificial Intelligence (AI) has transformed the way machines engage with the world, and at the core of this transformation are “intelligent agents.” These agents are systems that observe their environment, process information, and take actions to achieve specific goals. But how do they make decisions?
An intelligent agent is a self-guided system that uses sensors to observe its environment, processes the information, and takes action with actuators to achieve its goals. Essentially, it acts as a decision-making engine driven by inputs and objectives.
For example, a thermostat operates as an intelligent agent by using sensors to detect the current room temperature. It processes this data and compares it to the desired temperature set by the user. Depending on this comparison, it decides whether to activate the heating or cooling system to reach the target temperature. Additionally, some advanced thermostats can learn user preferences over time and automatically adjust settings to optimize comfort and energy efficiency.
A self-driving car is a more complex intelligent agent. It relies on a network of cameras, radar, lidar, and other sensors to perceive its surroundings, including other vehicles, pedestrians, traffic signs, and road conditions. The car’s onboard computer processes this data in real-time, making decisions about when to accelerate, brake, or steer. Its ultimate goal is to navigate safely and efficiently to a destination while avoiding obstacles and following traffic rules.
How Do Intelligent Agents Make Decisions?
The decision-making process of intelligent agents varies based on their design and purpose. It begins with perception, where the agent gathers information from its environment using sensors. Next, during the processing stage, the collected data is analyzed to interpret the current situation. After the analysis is complete, the decision-making phase takes place, during which the agent determines the most appropriate action based on predefined rules, goals, or learned experiences. Finally, in the action stage, the agent executes the decision using actuators to interact with or modify its environment. This continuous cycle of perception, processing, decision-making, and action allows intelligent agents to operate autonomously and effectively to achieve their objectives.
Types of Intelligent Agents
Intelligent agents serve as smart assistants, adapting their decision-making processes according to their specific types. Some agents react immediately to changes, while others plan for goals, optimize outcomes, or learn to improve over time. Each type is designed to handle varying levels of complexity, ranging from simple tasks to more advanced problem-solving.
Simple reflex agents respond instantly to changes in their environment without considering future consequences. For example, a light sensor activates a lamp when it detects darkness. These agents operate based on predefined rules and do not think ahead; they merely react to the immediate situation.
In contrast, model-based agents keep track of how things function within their environment, even when certain elements are not directly visible. They rely on an internal model to make informed decisions despite missing information. A good example is a vacuum robot that remembers the placement of furniture, allowing it to navigate around obstacles even when it cannot see them at that moment.
Goal-based agents focus on achieving specific targets by planning actions that lead to desired outcomes. For instance, a delivery robot determines the shortest and most efficient route to reach your house and deliver a package. Unlike simple reflex agents, goal-based agents consider future consequences and select actions that bring them closer to their objectives.
Utility-based agents take decision-making a step further by aiming for the best possible outcome. They evaluate different options based on a utility function, which measures success. For example, a stock-trading bot analyzes market trends to decide when to buy or sell stocks, maximizing profits while balancing risks and rewards.
Learning agents continuously improve by learning from past experiences. They refine their decision-making processes over time, adapting to new situations. A good example is a chatbot that becomes more accurate and efficient in answering questions as it interacts with users. Through learning, these agents enhance their performance and deliver better results over time.
Real-World Applications
Intelligent agents enhance many everyday technologies, making them more efficient and responsive. They are commonly used in virtual assistants, self-driving cars, healthcare, and gaming.
Virtual assistants such as Alexa, Siri, and Google Assistant use intelligent agents to understand and respond to user queries, provide helpful information, and complete various tasks. In self-driving cars, these agents process real-time data from sensors to “see” the road, navigate through traffic, and make safe driving decisions. In the healthcare sector, AI agents assist doctors in diagnosing illnesses and recommending appropriate treatments, ultimately improving patient care. Additionally, intelligent agents control in-game characters, making their actions more realistic and responsive, enhancing the overall gaming experience.
Intelligent agents make decisions by observing their environment through sensors, processing the collected data, and applying predefined rules, algorithms, or learning models to identify the best course of action. Whether it’s a simple thermostat adjusting the temperature or a complex self-driving car navigating tricky roads, these agents operate through a continuous cycle of perception, analysis, and action to achieve their objectives. Their decision-making capabilities are central to how AI systems interact with and adapt to the world around them.
by: Assoc. Prof. Ts. Dr. Maslin Masrom & Ts. Dr. Normaisharah Mamat, Faculty of Artificial Intelligence