added fol_fc_ask to logic.py ; update README.md (aimacode#415)

* Update utils.py

in pseudo code the sequence of arguments is   " WEIGHTED-SAMPLE-WITH-REPLACEMENT(N, S, W)"  

* Update utils.py

in pseudo code the sequence of arguments is " WEIGHTED-SAMPLE-WITH-REPLACEMENT(N, S, W)"  same must follow in function "particle_filtering" in the file probability.py

* Update learning.py

weight_sample_with_replacement sequence of args

* Update probability.py

 weighted_sample_with_replacement sequence of args

* Update search.py

* Update planning.py

added double_tennis_problem from chapter 11 , figure 11.10

* Update utils.py

added missing space after comma

* Update learning.py

added missing space after comma

* Update probability.py

added missing space after comma

* Update search.py

added missing space after comma

* Update planning.py

* Update planning.py

* Update planning.py

* Update planning.py

* Update planning.py

* Update planning.py

* Update test_agents.py

pep8 changes, showed flake8 errors

* Update test_agents.py

* Update test_agents.py

* Update test_agents.py

* Update test_text.py

added missing whitespace after comma

* Update utils.py

added space after comma

* Update search.py

added space after comma

* Update probability.py

added space after comma

* Update learning.py

added space after comma

* Update planning.py

added double_tennis_problem

* Update rl.py

In the pseudocode figure 21.8, the first 'if' starts with argument 's', which is the previous state, not s1(i.e, the current state).

* Update search.py

the 'uniform_cost_search' in notebook 'search-4e.ipynb' resembles more to the pseudocode in book.

* Update search.py

* Update search.py

* Update search.py

* Update README.md

* Update README.md

* Update README.md

* Update planning.py

* Update planning.py

added spaces after comma

* Update planning.py

* Update test_planning.py

added double_tennis_problem test

* Update test_planning.py

* Update logic.py

added fol_fc_ask from fig 9.3

* Update logic.py

* Update test_planning.py

Author: sofmonk

README.md

@@ -76,16 +76,16 @@ Here is a table of algorithms, the figure, name of the code in the book and in t
 | 9.3     | FOL-FC-Ask         | `fol_fc_ask` | [`logic.py`][logic] |
 | 9.6     | FOL-BC-Ask         | `fol_bc_ask` | [`logic.py`][logic] |
 | 9.8     | Append             |            |              |
-| 10.1    | Air-Cargo-problem    |          |
-| 10.2    | Spare-Tire-Problem |          |
-| 10.3    | Three-Block-Tower  |          |
-| 10.7    | Cake-Problem       |          |
-| 10.9    | Graphplan          |          |
+| 10.1    | Air-Cargo-problem    |`air_cargo`          |[`planning.py`][planning]|
+| 10.2    | Spare-Tire-Problem |  `spare_tire`        |[`planning.py`][planning]|
+| 10.3    | Three-Block-Tower  |  `three_block_tower`        |[`planning.py`][planning]|
+| 10.7    | Cake-Problem       |   `have_cake_and_eat_cake_too`       |[`planning.py`][planning]|
+| 10.9    | Graphplan          |    `GraphPlan`      |[`planning.py`][planning]|
 | 10.13   | Partial-Order-Planner |          |
 | 11.1    | Job-Shop-Problem-With-Resources |          |
 | 11.5    | Hierarchical-Search |          |
 | 11.8    | Angelic-Search   |          |
-| 11.10   | Doubles-tennis     |          |
+| 11.10   | Doubles-tennis     | `double_tennis_problem`          |[`planning.py`][planning]|
 | 13      | Discrete Probability Distribution | `ProbDist` | [`probability.py`][probability] |
 | 13.1    | DT-Agent           | `DTAgent` | [`probability.py`][probability] |
 | 14.9    | Enumeration-Ask    | `enumeration_ask` | [`probability.py`][probability] |

logic.py

@@ -842,7 +842,22 @@ def subst(s, x):
 
 
 def fol_fc_ask(KB, alpha):
-    raise NotImplementedError
+    """A simple forward-chaining algorithm. [Figure 9.3]"""
+    while new is not None:
+        new = []
+        for rule in KB:
+            p, q = parse_definite_clause(standardize_variables(rule))
+            for p_ in random.KB.clauses:
+                if p != p_:
+                    for theta in (subst(theta, p) == subst(theta, p_)):
+                        q_ = subst(theta, q)
+                        if  not unify(q_,KB.sentence in KB) or not unify(q_, new):
+                            new.append(q_)
+                            phi = unify(q_,alpha)
+                            if phi is not None:
+                                return phi
+        KB.tell(new)
+    return None
 
 
 def standardize_variables(sentence, dic=None):

planning.py

@@ -237,6 +237,7 @@ def goal_test(kb):
 
     return PDLL(init, [eat_cake, bake_cake], goal_test)
 
+
 class Level():
     """
     Contains the state of the planning problem
@@ -531,8 +532,8 @@ def double_tennis_problem():
     init = [expr('At(A, LeftBaseLine)'),
             expr('At(B, RightNet)'),
             expr('Approaching(Ball, RightBaseLine)'),
-            expr('Partner(A,B)'),
-            expr('Partner(A,B)')]
+            expr('Partner(A, B)'),
+            expr('Partner(B, A)')]
 
     def goal_test(kb):
         required = [expr('Goal(Returned(Ball))'), expr('At(a, RightNet)'), expr('At(a, LeftNet)')]
@@ -543,17 +544,17 @@ def goal_test(kb):
 
     ##actions
     #hit
-    precond_pos=[expr("Approaching(Ball,loc)"), expr("At(actor,loc)")]
+    precond_pos=[expr("Approaching(Ball, loc)"), expr("At(actor, loc)")]
     precond_neg=[]
     effect_add=[expr("Returned(Ball)")]
     effect_rem = []
-    hit = Action(expr("Hit(actor,Ball)"), [precond_pos, precond_neg], [effect_add, effect_rem])
+    hit = Action(expr("Hit(actor, Ball)"), [precond_pos, precond_neg], [effect_add, effect_rem])
 
     #go
-    precond_pos = [ expr("At(actor,loc)")]
+    precond_pos = [expr("At(actor, loc)")]
     precond_neg = []
-    effect_add = [expr("At(actor,to)")]
-    effect_rem = [expr("At(actor,loc)")]
-    go = Action(expr("Go(actor,to)"), [precond_pos, precond_neg], [effect_add, effect_rem])
+    effect_add = [expr("At(actor, to)")]
+    effect_rem = [expr("At(actor, loc)")]
+    go = Action(expr("Go(actor, to)"), [precond_pos, precond_neg], [effect_add, effect_rem])
 
     return PDLL(init, [hit, go], goal_test)

probability.py

@@ -643,5 +643,7 @@ def particle_filtering(e, N, HMM):
         w[i] = float("{0:.4f}".format(w[i]))
 
     # STEP 2
+
     s = weighted_sample_with_replacement(N, s, w)
+
     return s