Give one example each for each agent below. Simple Reflex Agent Model Based Reflex Agent Goal …
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Technifi Expert’s Answer:
It is the mixture of architecture and program. It is something that perceives the environment through the sensor sand acts accordingly as a response through the actuators.
Simple reflex agent:
It is the basic form of agent with limited intelligence. It acts in the form of condition statement:
For example if a simple reflex agent is used it’s response is purely based on the condition and it doesnt take any consideration of past history.
for example if we design an agent to collect a sample, then when the agent comes across the sample it collects it. Then again if the agent comes across an another sample it simply collects it without calculating the earlier experience.
Model based reflex agent:
In this type of agent the actions are derived directly from an internal model of the current world state that is updated over time.
All the actions are based on current state and it has a knowledge of previous state as well.
in the earlier example of agent picking a sample it ignores the past event and picks another, but if we use model reflex agent then the agent simply ignores the second sample.
Goal Based Agent:
This type of agent is goal driven one and it simply doesnt follow the IF-THEN model but has power to take appropriate decisions based on its goal statements.
Goal provide a reason for the agent to prefer one way over another.
For example, let us consider automated car guiding system. if the user types the destination then the agent calculates the minimum distance and selects the optimal way to reach the destination.
Utility based agent:
Agents we have seen till now have clear and easy goal or condition, but utility based agent concentrates more on ultility(measure of goodness) rather than on single goal.
If the set of goals are there, and the agent should use any of the goals present to obtain maximum utility.
example: automatic car,
goals to consider: drive safe,obey law,manimize travel journey,save fuel etc
The agent’s behavior increases over experience and time. so the learning agent monitors the agents performance over time and learn by feedback.
Many actions or problems are simulated and the environment is generated for the agent to gather more experience and learn optimization in work.
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