Examples of aima.core.search.online.LRTAStarAgent

aima.core.search.online.LRTAStarAgent

Artificial Intelligence A Modern Approach 3rdd Edition): Figure 4.24, page 152.

 function LRTA*-AGENT(s') returns an action inputs: s', a percept that identifies the current state persistent: result, a table, indexed by state and action, initially empty H, a table of cost estimates indexed by state, initially empty s, a, the previous state and action, initially null if GOAL-TEST(s') then return stop if s' is a new state (not in H) then H[s'] <- h(s') if s is not null result[s, a] <- s' H[s] <-        min LRTA*-COST(s, b, result[s, b], H) b (element of) ACTIONS(s) a <- an action b in ACTIONS(s') that minimizes LRTA*-COST(s', b, result[s', b], H) s <- s' return a function LRTA*-COST(s, a, s', H) returns a cost estimate if s' is undefined then return h(s) else return c(s, a, s') + H[s']

Figure 4.24 LRTA*-AGENT selects an action according to the value of neighboring states, which are updated as the agent moves about the state space.

Note: This algorithm fails to exit if the goal does not exist (e.g. A<->B Goal=X), this could be an issue with the implementation. Comments welcome. @author Ciaran O'Reilly @author Mike Stampone

      break;
    case 1:
      Problem p = new BidirectionalMapProblem(map, null, locs[1]);
      OnlineSearchProblem osp = new OnlineSearchProblem
      (p.getActionsFunction(), p.getGoalTest(), p.getStepCostFunction());
      agent = new LRTAStarAgent
      (osp, MapFunctionFactory.getPerceptToStateFunction(), heuristic);
      break;
    }
    env.addAgent(agent, locs[0]);
  }

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        break;
      case 1:
        Problem p = new BidirectionalMapProblem(map, null, locs[1]);
        OnlineSearchProblem osp = new OnlineSearchProblem
        (p.getActionsFunction(), p.getGoalTest(), p.getStepCostFunction());
        agent = new LRTAStarAgent
        (osp, MapFunctionFactory.getPerceptToStateFunction(), heuristic);
        break;
      }
      env.addAgent(agent, locs[0]);
    }

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  }


  @Test
  public void testAlreadyAtGoal() {
    MapEnvironment me = new MapEnvironment(aMap);
    LRTAStarAgent agent = new LRTAStarAgent(new OnlineSearchProblem(
        MapFunctionFactory.getActionsFunction(aMap),
        new DefaultGoalTest("A"), new MapStepCostFunction(aMap)),
        MapFunctionFactory.getPerceptToStateFunction(), hf);
    me.addAgent(agent, "A");
    me.addEnvironmentView(new TestEnvironmentView());

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  }


  @Test
  public void testNormalSearch() {
    MapEnvironment me = new MapEnvironment(aMap);
    LRTAStarAgent agent = new LRTAStarAgent(new OnlineSearchProblem(
        MapFunctionFactory.getActionsFunction(aMap),
        new DefaultGoalTest("F"), new MapStepCostFunction(aMap)),
        MapFunctionFactory.getPerceptToStateFunction(), hf);
    me.addAgent(agent, "A");
    me.addEnvironmentView(new TestEnvironmentView());

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  }


  @Test
  public void testNoPath() {
    MapEnvironment me = new MapEnvironment(aMap);
    LRTAStarAgent agent = new LRTAStarAgent(new OnlineSearchProblem(
        MapFunctionFactory.getActionsFunction(aMap),
        new DefaultGoalTest("G"), new MapStepCostFunction(aMap)),
        MapFunctionFactory.getPerceptToStateFunction(), hf);
    me.addAgent(agent, "A");
    me.addEnvironmentView(new TestEnvironmentView());

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