package aima.core.agent.impl.aprog;
import java.util.Set;
import aima.core.agent.Action;
import aima.core.agent.AgentProgram;
import aima.core.agent.Percept;
import aima.core.agent.impl.DynamicPercept;
import aima.core.agent.impl.NoOpAction;
import aima.core.agent.impl.ObjectWithDynamicAttributes;
import aima.core.agent.impl.aprog.simplerule.Rule;
/**
* Artificial Intelligence A Modern Approach (3rd Edition): Figure 2.10, page
* 49.<br>
* <br>
*
* <pre>
* function SIMPLE-RELEX-AGENT(percept) returns an action
* persistent: rules, a set of condition-action rules
*
* state <- INTERPRET-INPUT(percept);
* rule <- RULE-MATCH(state, rules);
* action <- rule.ACTION;
* return action
* </pre>
*
* Figure 2.10 A simple reflex agent. It acts according to a rule whose
* condition matches the current state, as defined by the percept.
*
* @author Ciaran O'Reilly
* @author Mike Stampone
*
*/
public class SimpleReflexAgentProgram implements AgentProgram {
//
// persistent: rules, a set of condition-action rules
private Set<Rule> rules;
/**
* Constructs a SimpleReflexAgentProgram with a set of condition-action
* rules.
*
* @param ruleSet
* a set of condition-action rules
*/
public SimpleReflexAgentProgram(Set<Rule> ruleSet) {
rules = ruleSet;
}
//
// START-AgentProgram
// function SIMPLE-RELEX-AGENT(percept) returns an action
@Override
public Action execute(Percept percept) {
// state <- INTERPRET-INPUT(percept);
ObjectWithDynamicAttributes state = interpretInput(percept);
// rule <- RULE-MATCH(state, rules);
Rule rule = ruleMatch(state, rules);
// action <- rule.ACTION;
// return action
return ruleAction(rule);
}
// END-AgentProgram
//
//
// PROTECTED METHODS
//
protected ObjectWithDynamicAttributes interpretInput(Percept p) {
return (DynamicPercept) p;
}
protected Rule ruleMatch(ObjectWithDynamicAttributes state,
Set<Rule> rulesSet) {
for (Rule r : rulesSet) {
if (r.evaluate(state)) {
return r;
}
}
return null;
}
protected Action ruleAction(Rule r) {
return null == r ? NoOpAction.NO_OP : r.getAction();
}
}