An entity that perceives its environment through sensors and acts on it through effectors. Like Robot. The sensors of a robot are ultrasonic, infrared video finder etc. The effectors of a robot are motors, manipulators and etc.
Types of agent
There are four types of agents in Artificial Intelligence
- Single reflex agent
- Model based agent
- Goal based agent
- Utility based agent
Simple Reflex Agent :
It is the most basic of the artificial out of there. and It performs actions based on the current situation. It is a very limited intelligence agent. When something happens in the environment of a simple reflex agent, the agent quickly scans its knowledge base for how to respond to the situation at hand based on predetermined rules. Simple reflex agents do not have the memory of past world states or percepts. So, actions depend solely on current perception. Action becomes a “reflex.” Uses condition-action rules.
If we have an agent and we set the if-then rule on it Condition = if the temp is more than 45, switch on AC. When agents sense the environment’s current situation is more than 45, it’ll take action, the agent must have all the information about the room.
If we make it more complex then we’ll need a more advanced agent or another reflex agent will be used to sense if there is anyone in the room. like, if the room is not empty, then switch on AC.
From our real life, if we consider a vacuum cleaner- it observes a place, detects dirt and cleans it. It’s quite straightforward. But, if there is an important piece of paper mixed with the dirt then the vacuum cleaner can’t detect the important documents. Here, it needs to be more advanced.
Schematic diagram of single reflex agent
In this example, Agent precepts from the environment through sensors got to know current situation imposes an if-then rule on current situation performs action basis of this rule it’ll change the environment through the actuator. **actuators perform the actions [ Like- if anyone throws an object towards us, we try to catch that object or want to bypass it. It is our reflex action, we are agents here. We perform catch action using our hand, this is the actuator here. Here, our sensor is our eyes.]
Model based agent :
The agent which performs actions based on current input, as well as the previous input, is called a Model-Based Reflex Agent. A model-based reflex agent is one that uses its percept history and its internal memory to make decisions about an internal ”model” of the world around it. It is a knowledge base agent. Decision made by history. What is a precept in the past, stored as knowledge and taking decisions based on this knowledge It is the partial observable moment?
This agent can think, and store the previous history.
Let’s say, you’ve been admitted to a new school. There, you know no one that much, just a primary knowledge that they are your classmates. You know you can be a good friend but you don’t know about them how good they can be as a friend. So, if you want to understand the environment
first then you will think about how to start a conversation with them. You will think, your action won’t be immediate.] For example – a self-driving car knows about itself, about its sensor, brakes, acceleration. but at present how many cars are on the road / when other cars will break, doesn’t know.
Schematic diagram of Model Based Agent
Goal based agent :
Expansion of model-based reflex agent. It has a model-based basic purpose and has knowledge of the past. extra is, it knows what should be the output. It knows what the goal state looks like. searching and planning is its main purpose. Goal-based agent focuses only on reaching the goal set and hence the decision taken by the agent is based on how far it is currently from the goal state.
This agent also can think, it has a specific goal and it gives the priority of the goal. You can say this agent works combined with a model-based agent.
Schematic diagram of Goal based agent:
Utility based agent :
A utility-based agent is an agent that acts based not only on what the goal is but the best way to reach that goal. To sum up, this agent gives the priority of happiness.