Intelligent agent in Artificial intelligence
Intelligent agents in AI are computer programs that can make decisions and take actions on their own. They are important because they can help solve problems and make tasks easier for humans. The idea of intelligent agents has been around for a long time, but it wasn’t until the development of computers that they could be created. Now, intelligent agents are used in many different industries and can be found in everyday things like smartphones and cars.
Characteristics of Intelligent Agent:
Intelligent agents have certain characteristics that make them unique. One of these is autonomy. This means that they can operate independently, without human supervision. Another characteristic is reactivity. This means that they can respond to changes in their environment quickly and effectively.
Autonomy: An intelligent agent operates independently and is capable of making decisions without human intervention.
Reactivity: An intelligent agent can perceive its environment and respond to changes in real time.
Proactiveness: An intelligent agent can actively seek out and pursue goals without being prompted.
Social ability: An intelligent agent can interact with other agents and humans in a social context. It can understand and respond to natural language, and can also take the initiative to communicate with others.
Structure of Intelligent Agent
Artificial intelligence agents adhere to the following equation:
Agent = Architecture + Agent Code
The following are the terms most commonly associated with agent structure:
The architecture is the gear or foundation that runs the agent.
The agent function, given by the subsequent formula, transfers a precept to the Action: f:P*-A
The agent function is implemented by the agent programme. By operating on the actual architecture, the agent programme generates function f.
The PEAS model is used in the construction of several AI Agents. The abbreviation PEAS stands for Performance Measure, Environment, Actuators, and Sensors. Take, for example, a hoover cleaner.
| Performance | Environment | Actuators | Sensors | |
| Vacuum Cleaner | Cleanness
Efficiency Battery life Security |
Room
Table Wood floor Carpet Various obstacles |
Wheels
Brushes Vacuum Extractor |
Camera
Dirt detection sensor Cliff sensor Bump Sensor Infrared Wall Sensor |
Here’s a diagram to illustrate the above explanation.
Working of Intelligent Agent
Sensors, actuators, and effectors are the three key elements that intelligent agents use. Gaining knowledge of these parts can help us comprehend more fully how intelligent agent humans function.
Sensors: Sensors are gadgets that sense modifications to their surroundings. This data is transmitted to other machines. Intelligent agents in artificial intelligence view their surroundings using sensors.
Actuators: These are devices that transform power into movement. They are responsible for regulating and manoeuvring a system. Rails, engines, and cogs are a few instances.
Effectors: Effectors have an impact on the surroundings. Legs, fingers, axles, an LCD screen, and limbs are among the examples. The intelligent agent receives input (percepts) from its surroundings via sensors. This agent uses artificial intelligence to make judgements based on the information it has gathered.
Types of Intelligent Agents
Simple reflex agents:
Simple reflex agents are the simplest type of intelligent agent, which respond to the current perception based on a set of predefined rules. These agents operate based on the “if-then” rule, where the agent’s behaviour is determined by the current percept and the corresponding rule. For example, a simple reflex agent designed to play chess would have a rule that says, “if the opponent’s pawn is in front of the king, then move the king to a safe location.”
Model-based agents:
Model-based agents are a step up from simple reflex agents, as they have the ability to maintain a model of the world. These agents use the model to reason about the effects of their actions and make decisions based on that information. For example, a model-based agent designed to play chess would have a model of the current board state, including the positions of all the pieces, and use that information to determine the best move to make.
Goal-based agents:
Goal-based agents are a type of intelligent agents that have a specific goal or objective they are trying to achieve. These agents are able to reason about the effects of their actions and use that information to make decisions that will help them achieve their goals. For example, a goal-based agent designed to play chess would have the goal of winning the game and would make decisions based on how they would help achieve that goal.
Utility-based agents:
Utility-based agents are a type of intelligent agents that make decisions based on the utility or value of a particular action. These agents use a utility function to determine the best action to take based on the current percept. For example, a utility-based agent designed to play chess would have a utility function that assigns a value to each possible move. This will be based on factors such as the safety of the pieces, the likelihood of winning, and so on. The agent would then make the move with the highest utility value.
Functions of Intelligent Agent
Artificial intelligence agents continually execute the following duties:
- Perception of dynamic environmental situations
- Taking action to influence environmental circumstances
- Reasoning is employed to interpret perceptions.
- Solving problems as they arise
- Making deductions
- Choosing acts and their results
Applications of Intelligent Agent
Robotics:
Intelligent agents are used in robotics to control the behavior of robots. These agents are designed to make decisions based on the information they receive from sensors, cameras, and other inputs. They can be used to navigate environments, manipulate objects, and interact with humans.
Gaming:
Intelligent agents are used in gaming to create non-player characters that can interact with players in a realistic way. They can be used to create complex, dynamic game environments and to generate characters that can adapt to the player’s actions. This makes the gaming experience more engaging and interactive.
Natural Language Processing:
Intelligent agents are used in natural language processing to understand and respond to spoken or written language. They can be used to create chatbots, virtual assistants, and language translators. These agents can understand and respond to a wide range of human languages, making them useful for a variety of applications.
Expert systems:
Intelligent agents are used in expert systems to simulate the decision-making processes of experts in a particular field. These agents are designed to analyze data, make predictions, and provide recommendations based on their knowledge and experience. They can be used in a wide range of fields, including medicine, finance, and engineering.
Challenges and Limitations of Intelligent Agent
The complexity of decision-making:
Decision-making is a complex process that requires understanding multiple factors and options. Intelligent agents may struggle to make decisions in scenarios where there are a large number of variables to consider or when the available data is incomplete or ambiguous.
Limited understanding of context:
Intelligent agents rely on data and algorithms to make decisions, and their performance can be affected by the context in which they operate. For example, an intelligent agent designed to recognize objects in images may struggle when presented with images taken in different lighting conditions or from different angles.
Lack of common sense:
Common sense is a set of knowledge and abilities that humans use to navigate the world around us. Intelligent agents lack the ability to understand and apply common sense reasoning. This can limit their ability to understand and respond to unexpected or novel situations.
Ethical considerations:
Intelligent agents are capable of making decisions and performing tasks that can have significant impacts on individuals and society. As such, there are important ethical considerations that need to be taken into account when designing and deploying intelligent agents. These include issues such as privacy, transparency, accountability, and bias.
Conclusion
In this article, we discussed the concept of intelligent agents in AI and their characteristics, types, and applications. We also highlighted some of the challenges and limitations of intelligent agents, including the complexity of decision-making, limited understanding of context, lack of common sense, and ethical considerations.
Intelligent agents have the potential to revolutionise various industries and improve our daily lives. However, it’s important to recognize the challenges and limitations of intelligent agents. We also need to look at ethical considerations when designing and deploying them. As we continue to advance in technology and data analysis, we can expect to see more and more applications of intelligent agents in our everyday lives.