001 package aima.search.online;
002
003 import java.util.ArrayList;
004 import java.util.Hashtable;
005 import java.util.List;
006
007 import aima.basic.Agent;
008 import aima.basic.Percept;
009
010 import aima.search.framework.Problem;
011 import aima.search.framework.Successor;
012
013 /**
014 * Artificial Intelligence A Modern Approach (2nd Edition): Figure 4.20, page 126.
015 * <code>
016 * function ONLINE-DFS-AGENT(s') returns an action
017 * inputs: s', a percept that identifies the current state
018 * static: result, a table, indexed by action and state, initially empty
019 * unexplored, a table that lists, for each visited state, the actions not yet tried
020 * unbacktracked, a table that lists, for each visited state, the backtracks not yet tried
021 * s, a, the previous state and action, initially null
022 *
023 * if GOAL-TEST(s') then return stop
024 * if s' is a new state then unexplored[s'] <- ACTIONS(s')
025 * if s is not null then do
026 * result[a, s] <- s'
027 * add s to the front of the unbacktracked[s']
028 * if unexplored[s'] is empty then
029 * if unbacktracked[s'] is empty then return stop
030 * else a <- an action b such that result[b, s'] = POP(unbacktracked[s'])
031 * else a <- POP(unexplored[s'])
032 * s <- s'
033 * return a
034 * </code>
035 * Figure 4.20 An online search agent that uses depth-first exploration. The agent is
036 * applicable only in bidirectional search spaces.<br>
037 * <br>
038 * Note: This algorithm fails to exit if the goal does not exist (e.g. A<->B Goal=X),
039 * this could be an issue with the implementation. Comments welcome.
040 */
041
042 /**
043 * @author Ciaran O'Reilly
044 *
045 */
046 public class OnlineDFSAgent extends Agent {
047
048 private Problem problem;
049 // static: result, a table, indexed by action and state, initially empty
050 private final Hashtable<ActionState, Percept> result = new Hashtable<ActionState, Percept>();
051 // unexplored, a table that lists, for each visited state, the actions not
052 // yet tried
053 private final Hashtable<Percept, List<Object>> unexplored = new Hashtable<Percept, List<Object>>();
054 // unbacktracked, a table that lists,
055 // for each visited state, the backtracks not yet tried
056 private final Hashtable<Percept, List<Percept>> unbacktracked = new Hashtable<Percept, List<Percept>>();
057 // s, a, the previous state and action, initially null
058 private Percept s = null;
059 private Object a = null;
060
061 public OnlineDFSAgent(Problem problem) {
062 setProblem(problem);
063 }
064
065 public Problem getProblem() {
066 return problem;
067 }
068
069 public void setProblem(Problem problem) {
070 this.problem = problem;
071 init();
072 }
073
074 // function ONLINE-DFS-AGENT(s') returns an action
075 // inputs: s', a percept that identifies the current state
076 @Override
077 public String execute(Percept sComma) {
078 // if GOAL-TEST(s') then return stop
079 if (!goalTest(sComma)) {
080 // if s' is a new state then unexplored[s'] <- ACTIONS(s')
081 if (!unexplored.containsKey(sComma)) {
082 unexplored.put(sComma, actions(sComma));
083 }
084
085 // if s is not null then do
086 if (null != s) {
087 // result[a, s] <- s'
088 result.put(new ActionState(a, s), sComma);
089
090 // Ensure the unbacktracked always has a list for s'
091 if (!unbacktracked.containsKey(sComma)) {
092 unbacktracked.put(sComma, new ArrayList<Percept>());
093 }
094
095 // add s to the front of the unbacktracked[s']
096 unbacktracked.get(sComma).add(s);
097 }
098 // if unexplored[s'] is empty then
099 if (unexplored.get(sComma).size() == 0) {
100 // if unbacktracked[s'] is empty then return stop
101 if (unbacktracked.get(sComma).size() == 0) {
102 a = Agent.NO_OP;
103 } else {
104 // else a <- an action b such that result[b, s'] =
105 // POP(unbacktracked[s'])
106 Percept popped = unbacktracked.get(sComma).remove(
107 unbacktracked.get(sComma).size() - 1);
108 for (ActionState as : result.keySet()) {
109 if (as.getState().equals(sComma)
110 && result.get(as).equals(popped)) {
111 a = as.getAction();
112 break;
113 }
114 }
115 }
116 } else {
117 // else a <- POP(unexplored[s'])
118 a = unexplored.get(sComma).remove(
119 unexplored.get(sComma).size() - 1);
120 }
121 } else {
122 a = Agent.NO_OP;
123 }
124
125 if (Agent.NO_OP.equals(a)) {
126 // I'm either at the Goal or can't get to it,
127 // which in either case I'm finished so just die.
128 die();
129 }
130
131 // s <- s'
132 s = sComma;
133 // return a
134 return a.toString();
135 }
136
137 //
138 // PRIVATE METHODS
139 //
140
141 private void init() {
142 live();
143 result.clear();
144 unexplored.clear();
145 unbacktracked.clear();
146 s = null;
147 a = null;
148 }
149
150 private boolean goalTest(Percept state) {
151 return getProblem().isGoalState(state);
152 }
153
154 private List<Object> actions(Percept state) {
155 List<Object> actions = new ArrayList<Object>();
156
157 List<Successor> successors = getProblem().getSuccessorFunction()
158 .getSuccessors(state);
159
160 for (Successor s : successors) {
161 actions.add(s.getAction());
162 }
163
164 return actions;
165 }
166 }