Simple Reflex Agent

-SIMPLE REFLEX AGENT  In artificial intelligence, an intelligent agent (IA) refers to an autonomous entity which acts, directing its activity towards achieving goals (i.e. it is an agent), upon an environment using observation through sensors-Input and consequent actuators- output/perform/activities ?  What is Simple Reflex Agent ?: It is the most basic of the intelligent agents out there. It performs actions based on a current situation. 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 memory of past world states or percepts. So, actions depend solely on current percept. Action becomes a “reflex.” Uses condition-action rules. ? percept ?A  ction becomes a “reflex ? Properties : - Reflex agent's properties based on reflex. - Spontaneously perform action is called reflex. - What it senses in current situation, current perception is the base of it's action - Ignore percept history. because, it needs calculation and calculation needs time to take action. Like, when we sneeze, we just sneezed, no thinking is performed,this is reflex. Work process :  Figure: 1 ? Here, Agent precepts from the environment฀ through sensors฀ got to know current situation฀impose if-then rule on current situation฀ perform 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 agent here. We perform catch action using our hand, this is the actuator here. Here, our sensor is our eyes.] Rules of Simple Reflex Agent : Based on if-then rules, it's a condition statement. Like, - if this…..then this. if we give any condition, it’ll will work - otherwise not. Agent will work only then when it has full information. Like, Game tic tac toe. it has to have all the information to play the game. Partial-observable, like- self driving car. We don’t know how many cars are on the road, don't know when other cars will give a break. so, some information is hidden. It needs a more advanced agent. Pseudocode : ● function SIMPLE-REFLEX-AGENT(percept) returns action [ a ? how does percept works in the above function ● function named SIMPLE_REFLEX_AGENT has been called using a parameter named percept, whuch takes percept of the agent and returns the action based on that percept] ● static: rules, a set of condition-action rules ● state ฀ INTERPRET-INPUT (percept) ● rule ฀ RULE-MATCH (state,rules) ● action ฀ RULE-ACTION [rule] return action  Example : If we have an agent and we set if then rule on it; Condition = if temp is more 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 that 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 clean it. It’s quite straight forward command and action. But, if there is any 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. Limitations : This only works if the current percept is sufficient for making the correct decision. We can’t apply if the current situation isn’t straight forward and demanding an advanced understanding agent. To overcome this limitation there are some advanced agents like model based reflex agent, utility based agent, goal based agent and so on.