Merge branch 'master' of https://github.com/aimacode/aima-pseudocode

* 'master' of https://github.com/aimacode/aima-pseudocode:
  Update README.md
  Add prior-sample and rejection-sampling markdowns.
  Add Hybrid-Wumpus-Agent markdown

Author: SnShine

README.md

@@ -170,6 +170,11 @@ The file [aima3e-algorithms.pdf](https://github.com/aimacode/pseudocode/blob/mas
        <td align="center"></td>
        <td><a href="md/WalkSAT.md">WALKSAT</a></td>
    </tr>
+   <tr>
+       <td align="center">&bull;</td>
+       <td align="center"></td>
+       <td><a href="md/Hybrid-Wumpus-Agent.md">HYBRID-WUMPUS-AGENT</a></td>
+   </tr>
    <tr>
        <td align="center">&bull;</td>
        <td align="center"></td>
@@ -217,6 +222,16 @@ The file [aima3e-algorithms.pdf](https://github.com/aimacode/pseudocode/blob/mas
        <td align="center"></td>
        <td><a href="md/Elimination-Ask.md">ELIMINATION-ASK</a></td>
    </tr>
+   <tr>
+       <td align="center">&bull;</td>
+       <td align="center"></td>
+       <td><a href="md/Prior-Sample.md">PRIOR-SAMPLE</a></td>
+   </tr>
+   <tr>
+       <td align="center">&bull;</td>
+       <td align="center"></td>
+       <td><a href="md/Rejection-Sampling.md">REJECTION-SAMPLING</a></td>
+   </tr>
    <tr>
        <td align="center">&bull;</td>
        <td align="center"></td>

md/Elimination-Ask.md

@@ -10,7 +10,7 @@ __function__ ELIMINATION-ASK(_X_, __e__, _bn_) __returns__ a distribution over _
  __for each__ _var_ __in__ ORDER(_bn_.VARS) __do__  
    _factors_ ← \[MAKE\-FACTOR(_var_, __e__) | _factors_\]  
    __if__ _var_ is a hidden variable __then__ _factors_ ← SUM\-OUT(_var_, _factors_)  
-__return__ NORMALIZE(POINTWISE\-PRODUCT(_factors_))  
+ __return__ NORMALIZE(POINTWISE\-PRODUCT(_factors_))  
 
 ---
 __Figure__ ?? The variable elimination algorithm for inference in Bayesian networks.

md/Hybrid-Wumpus-Agent.md

@@ -0,0 +1,40 @@
+# HYBRID-WUMPUS-AGENT
+
+## AIMA3e
+__function__ HYBRID-WUMPUS-AGENT(_percept_) __returns__ an _action_  
+ __inputs__: _percept_, a list, \[_stench_, _breeze_, _glitter_, _bump_, _scream_\]  
+ __persistent__: _KB_, a knowledge base, initially the atemporal "wumpus physics"  
+      _t_, a counter, initially 0, indicating time  
+      _plan_, an action sequence, initially empty  
+
+ TELL(_KB_, MAKE-PERCEPT-SENTENCE(_percept_, _t_))  
+ TELL the _KB_ the temporal "physics" sentences for time _t_  
+&emsp;_safe_ &larr; {\[_x_, _y_\] : ASK(_KB_, _OK_<sup>_t_</sup><sub>_x_,_y_</sub>) = _true_}  
+&emsp;__if__ ASK(_KB_, _Glitter_<sup>_t_</sup>) = _true_ __then__  
+&emsp;&emsp;&emsp;_plan_ &larr; \[_Grab_\] + PLAN-ROUTE(_current_, {\[1,1\]}, _safe_) + \[_Climb_\]  
+&emsp;__if__ _plan_ is empty __then__  
+&emsp;&emsp;&emsp;_unvisited_ &larr; {\[_x_, _y_\] : ASK(_KB_, _L_<sup>_t'_</sup><sub>_x_,_y_</sub>) = _false_ for all _t'_ &le; _t_}  
+&emsp;&emsp;&emsp;_plan_ &larr; PLAN-ROUTE(_current_, _unvisited_ &cap; _safe_, _safe_)  
+&emsp;__if__ _plan_ is empty and ASK(_KB_, _HaveArrow_<sup>_t_</sup>) = _true_ __then__  
+&emsp;&emsp;&emsp;_possible\_wumpus_ &larr; {\[_x_, _y_\] : ASK(_KB_, &not;_W_<sub>_x_,_y_</sub>) = _false_}  
+&emsp;&emsp;&emsp;_plan_ &larr; PLAN-SHOT(_current_, _possible\_wumpus_, _safe_)  
+&emsp;__if__ _plan_ is empty __then__ //no choice but to take a risk  
+&emsp;&emsp;&emsp;_not\_unsafe_ &larr; {\[_x_, _y_\] : ASK(_KB_, &not;_OK_<sup>_t_</sup><sub>_x_,_y_</sub>) = _false_}  
+&emsp;&emsp;&emsp;_plan_ &larr; PLAN-ROUTE(_current_, _unvisited_ &cap; _not\_unsafe_, _safe_)  
+&emsp;__if__ _plan_ is empty __then__  
+&emsp;&emsp;&emsp;_plan_ &larr; PLAN-ROUTE(_current_, {\[1,1\]}, _safe_) + \[_Climb_\]  
+&emsp;_action_ &larr; POP(_plan_)  
+&emsp;TELL(_KB_, MAKE-ACTION-SENTENCE(_action_, _t_))  
+&emsp;_t_ &larr; _t_ + 1  
+&emsp;__return__ _action_
+
+---
+__function__ PLAN-ROUTE(_current_, _goals_, _allowed_) __returns__ an action sequence  
+&emsp;__inputs__: _current_, the agent's current position  
+&emsp;&emsp;&emsp;&emsp;&emsp;_goals_, a set of squares; try to plan a route to one of them  
+&emsp;&emsp;&emsp;&emsp;&emsp;_allowed_, a set of squares that can form part of the route  
+
+&emsp;_problem_ &larr; ROUTE-PROBLEM(_current_, _goals_, _allowed_)  
+&emsp;__return__ A\*\-GRAPH-SEARCH(_problem_)  
+
+__Figure__ ?? A hybrid agent program for the wumpus world. It uses a propositional knowledge base to infer the state of the world, and a combination of problem-solving search and domain-specific code to decide what actions to take.

md/Prior-Sample.md

@@ -0,0 +1,13 @@
+# PRIOR-SAMPLE
+
+## AIMA3e
+__function__ PRIOR-SAMPLE(_bn_) __returns__ an event sampled from the prior specified by _bn_  
+&emsp;__inputs__: _bn_, a Bayesian network specifying joint distribution __P__(_X<sub>1</sub>_, &hellip;, _X<sub>n</sub>_)  
+
+&emsp;__x__ &larr; an event with _n_ elements  
+&emsp;__foreach__ varaible _X<sub>i</sub>_ __in__ _X<sub>1</sub>_, &hellip;, _X<sub>n</sub>_ __do__  
+&emsp;&emsp;&emsp;_x_\[_i_\] &larr; a random sample from __P__(_X<sub>i</sub>_ &vert; _parents_(_X<sub>i</sub>_))  
+&emsp;__return x__
+
+---
+__Figure__ ?? A sampling algorithm that generates events from a Bayesian network. Each variable is sampled according to the conditional distribution given the values already sampled for the variable's parents.

md/Rejection-Sampling.md

@@ -0,0 +1,18 @@
+# REJECTION-SAMPLING
+
+## AIMA3e
+__function__ REJECTION-SAMPLING(_X_, __e__, _bn_, _N_) __returns__ an estimate of __P__(_X_ | __e__)  
+ __inputs__: _X_, the query variable  
+     __e__, observed values for variables __E__  
+     _bn_, a Bayesian network  
+     _N_, the total number of samples to be generated  
+ __local variables__: __N__, a vector of counts for each value of _X_, initially zero  
+
+ __for__ _j_ = 1 to _N_ __do__  
+   __x__ ← PRIOR\-SAMPLE(_bn_)  
+   __if x__ is consistent with __e then__  
+     __N__\[_x_\] ← __N__\[_x_\] + 1 where _x_ is the value of _X_ in __x__  
+ __return__ NORMALIZE(__N__)  
+
+---
+__Figure__ ?? The rejection\-sampling algorithm for answering queries given evidence in a Bayesian network.