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(solution) Hi everyone, Please help me out for solving this AI documents and


Hi everyone, Please help me out for solving this AI documents and how these algorithms can be solved.


Dear students,

 

You are to expand and revise the previous model and implement procedures for not only the agent but

 

also the environment. Be sure to use lot of plots to prove how this model is better than the previous

 

model.

 

It is very important to note that in NetLogo, you MUST NOT use loops. Instead use a ?Forever? button

 

and set stop conditions at the start of your procedure. Look at the ?stop? command in NetLogo.

 

At the end of the lab you must submit a detailed overview of how the previous model and this one

 

compare with each other:

 

Model-based Reflex Agent

 

? function REFLEX-AGENT-WITH-STATE (percept) returns an action

 

? static: state, a description of the current world state

 

rules, a set of condition-action rules

 

action, the most recent action, initially none

 

? state ? UPDATE-STATE (state, action, percept)

 

? rule ? RULE-MATCH (state, rules)

 

? action ? RULE-ACTION [rule]

 

? state ? UPDATE-STATE (state, action)

 

? return action

 

The Environment Program

 

? The RUN-ENVIRONMENT procedure correctly exercises the agents in the environment.

 

? The function RUN-EVAL-ENVIRONMENT applies a performance measure to each agent and

 

returns a list of the resulting scores.

 

? The scores variable keeps track of each agent?s score.

 

? procedure RUN-ENVIRONMENT (state, UPDATE-FN, agents, termination)

 

? inputs: state, the initial state of the environment

 

UPDATE-FN, function to modify the environment

 

agents, a set of agents

 

termination, a predicate to test when we are done

 

? repeat

 

? for each agent in agents do

 

? PERCEPT [agent] ? GET-PERCEPT (agent, state)

 

? end

 

? for each agent in agents do

 

? ACTION [agent] ? PROGRAM [agent] (PERCEPT [agent])

 

? end

 

? state ? UPDATE-FN (actions, agents, state)

 

? until termination (state)

 

? Basic Environment Simulator

 

? Gives each agent its percept, gets an action from each agent, and then updates the

 

environment.

 

? procedure RUN-EVAL-ENVIRONMENT (state, UPDATE-FN, agents, termination, PERFORMANCEFN) returns scores ?

 

?

 

?

 

?

 

?

 

?

 

?

 

?

 

?

 

?

 

?

 

? local variables: scores, a vector the same size as agents, all 0

 

repeat

 

for each agent in agents do

 

PERCEPT [agent] ? GET-PERCEPT (agent, state)

 

end

 

for each agent in agents do

 

ACTION [agent] ? PROGRAM [agent] (PERCEPT [agent])

 

end

 

state ? UPDATE-FN (actions, agents, state)

 

scores ? PERFORMANCE-FN (scores, agents, state)

 

until termination (state)

 

return scores

 


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