Making simple decision (chapter 15 and 25) (aimacode#1108)

bruceyoungsysu · delaray · commit 92fa85861fed · 2019-09-01T19:24:05.000+02:00
* add chapter 15 and 25

* add comments
diff --git a/making_simple_decision4e.py b/making_simple_decision4e.py
@@ -0,0 +1,170 @@
+from utils4e import (
+    argmax, element_wise_product, matrix_multiplication,
+    vector_to_diagonal, vector_add, scalar_vector_product, inverse_matrix,
+    weighted_sample_with_replacement, probability, normalize
+)
+from agents import Agent
+from probability import BayesNet
+import random
+
+# Making Simple Decisions (Chapter 15)
+
+
+class DecisionNetwork(BayesNet):
+    """An abstract class for a decision network as a wrapper for a BayesNet.
+    Represents an agent's current state, its possible actions, reachable states
+    and utilities of those states."""
+
+    def __init__(self, action, infer):
+        """action: a single action node
+        infer: the preferred method to carry out inference on the given BayesNet"""
+        super(DecisionNetwork, self).__init__()
+        self.action = action
+        self.infer = infer
+
+    def best_action(self):
+        """Return the best action in the network"""
+        return self.action
+
+    def get_utility(self, action, state):
+        """Return the utility for a particular action and state in the network"""
+        raise NotImplementedError
+
+    def get_expected_utility(self, action, evidence):
+        """Compute the expected utility given an action and evidence"""
+        u = 0.0
+        prob_dist = self.infer(action, evidence, self).prob
+        for item, _ in prob_dist.items():
+            u += prob_dist[item] * self.get_utility(action, item)
+
+        return u
+
+
+class InformationGatheringAgent(Agent):
+    """A simple information gathering agent. The agent works by repeatedly selecting
+    the observation with the highest information value, until the cost of the next
+    observation is greater than its expected benefit. [Figure 16.9]"""
+
+    def __init__(self, decnet, infer, initial_evidence=None):
+        """decnet: a decision network
+        infer: the preferred method to carry out inference on the given decision network
+        initial_evidence: initial evidence"""
+        self.decnet = decnet
+        self.infer = infer
+        self.observation = initial_evidence or []
+        self.variables = self.decnet.nodes
+
+    def integrate_percept(self, percept):
+        """Integrate the given percept into the decision network"""
+        raise NotImplementedError
+
+    def execute(self, percept):
+        """Execute the information gathering algorithm"""
+        self.observation = self.integrate_percept(percept)
+        vpis = self.vpi_cost_ratio(self.variables)
+        j = argmax(vpis)
+        variable = self.variables[j]
+
+        if self.vpi(variable) > self.cost(variable):
+            return self.request(variable)
+
+        return self.decnet.best_action()
+
+    def request(self, variable):
+        """Return the value of the given random variable as the next percept"""
+        raise NotImplementedError
+
+    def cost(self, var):
+        """Return the cost of obtaining evidence through tests, consultants or questions"""
+        raise NotImplementedError
+
+    def vpi_cost_ratio(self, variables):
+        """Return the VPI to cost ratio for the given variables"""
+        v_by_c = []
+        for var in variables:
+            v_by_c.append(self.vpi(var) / self.cost(var))
+        return v_by_c
+
+    def vpi(self, variable):
+        """Return VPI for a given variable"""
+        vpi = 0.0
+        prob_dist = self.infer(variable, self.observation, self.decnet).prob
+        for item, _ in prob_dist.items():
+            post_prob = prob_dist[item]
+            new_observation = list(self.observation)
+            new_observation.append(item)
+            expected_utility = self.decnet.get_expected_utility(variable, new_observation)
+            vpi += post_prob * expected_utility
+
+        vpi -= self.decnet.get_expected_utility(variable, self.observation)
+        return vpi
+
+
+# _________________________________________________________________________
+# chapter 25 Robotics
+# TODO: Implement continuous map for MonteCarlo similar to Fig25.10 from the book
+
+
+class MCLmap:
+    """Map which provides probability distributions and sensor readings.
+    Consists of discrete cells which are either an obstacle or empty"""
+    def __init__(self, m):
+        self.m = m
+        self.nrows = len(m)
+        self.ncols = len(m[0])
+        # list of empty spaces in the map
+        self.empty = [(i, j) for i in range(self.nrows) for j in range(self.ncols) if not m[i][j]]
+
+    def sample(self):
+        """Returns a random kinematic state possible in the map"""
+        pos = random.choice(self.empty)
+        # 0N 1E 2S 3W
+        orient = random.choice(range(4))
+        kin_state = pos + (orient,)
+        return kin_state
+
+    def ray_cast(self, sensor_num, kin_state):
+        """Returns distace to nearest obstacle or map boundary in the direction of sensor"""
+        pos = kin_state[:2]
+        orient = kin_state[2]
+        # sensor layout when orientation is 0 (towards North)
+        #  0
+        # 3R1
+        #  2
+        delta = ((sensor_num % 2 == 0)*(sensor_num - 1), (sensor_num % 2 == 1)*(2 - sensor_num))
+        # sensor direction changes based on orientation
+        for _ in range(orient):
+            delta = (delta[1], -delta[0])
+        range_count = 0
+        while (0 <= pos[0] < self.nrows) and (0 <= pos[1] < self.nrows) and (not self.m[pos[0]][pos[1]]):
+            pos = vector_add(pos, delta)
+            range_count += 1
+        return range_count
+
+
+def monte_carlo_localization(a, z, N, P_motion_sample, P_sensor, m, S=None):
+    """Monte Carlo localization algorithm from Fig 25.9"""
+
+    def ray_cast(sensor_num, kin_state, m):
+        return m.ray_cast(sensor_num, kin_state)
+
+    M = len(z)
+    W = [0]*N
+    S_ = [0]*N
+    W_ = [0]*N
+    v = a['v']
+    w = a['w']
+
+    if S is None:
+        S = [m.sample() for _ in range(N)]
+
+    for i in range(N):
+        S_[i] = P_motion_sample(S[i], v, w)
+        W_[i] = 1
+        for j in range(M):
+            z_ = ray_cast(j, S_[i], m)
+            W_[i] = W_[i] * P_sensor(z[j], z_)
+
+    S = weighted_sample_with_replacement(N, S_, W_)
+    return S
+
