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"""Representations and Inference for Logic (Chapters 7-9, 12)
Covers both Propositional and First-Order Logic. First we have four
important data types:
KB Abstract class holds a knowledge base of logical expressions
KB_Agent Abstract class subclasses agents.Agent
Expr A logical expression, imported from utils.py
substitution Implemented as a dictionary of var:value pairs, {x:1, y:x}
Be careful: some functions take an Expr as argument, and some take a KB.
Logical expressions can be created with Expr or expr, imported from utils, TODO
or with expr, which adds the capability to write a string that uses
the connectives ==>, <==, <=>, or <=/=>. But be careful: these have the
operator precedence of commas; you may need to add parens to make precedence work.
Then we implement various functions for doing logical inference:
pl_true Evaluate a propositional logical sentence in a model
tt_entails Say if a statement is entailed by a KB
pl_resolution Do resolution on propositional sentences
dpll_satisfiable See if a propositional sentence is satisfiable
And a few other functions:
to_cnf Convert to conjunctive normal form
unify Do unification of two FOL sentences
diff, simp Symbolic differentiation and simplification
isnumber, issequence, Expr, expr, subexpressions
from collections import defaultdict
# ______________________________________________________________________________
class KB:
"""A knowledge base to which you can tell and ask sentences.
To create a KB, first subclass this class and implement
tell, ask_generator, and retract. Why ask_generator instead of ask?
The book is a bit vague on what ask means --
For a Propositional Logic KB, ask(P & Q) returns True or False, but for an
FOL KB, something like ask(Brother(x, y)) might return many substitutions
such as {x: Cain, y: Abel}, {x: Abel, y: Cain}, {x: George, y: Jeb}, etc.
So ask_generator generates these one at a time, and ask either returns the
first one or returns False."""
def __init__(self, sentence=None):
def ask(self, query):
"""Return a substitution that makes the query true, or, failing that, return False."""
def ask_generator(self, query):
"""Yield all the substitutions that make query true."""
def retract(self, sentence):
class PropKB(KB):
"""A KB for propositional logic. Inefficient, with no indexing."""
def __init__(self, sentence=None):
self.clauses = []
if sentence:
self.tell(sentence)
"""Yield the empty substitution {} if KB entails query; else no results."""
if tt_entails(Expr('&', *self.clauses), query):
yield {}
def ask_if_true(self, query):
"""Return True if the KB entails query, else return False."""
Darius Bacon
a validé
for _ in self.ask_generator(query):
Darius Bacon
a validé
return False
def retract(self, sentence):
"""Remove the sentence's clauses from the KB."""
for c in conjuncts(to_cnf(sentence)):
if c in self.clauses:
self.clauses.remove(c)
# ______________________________________________________________________________
def KB_AgentProgram(KB):
"""A generic logical knowledge-based agent program. [Figure 7.1]"""
steps = itertools.count()
def program(percept):
KB.tell(make_percept_sentence(percept, t))
action = KB.ask(make_action_query(t))
KB.tell(make_action_sentence(action, t))
return action
return Expr("Percept")(percept, t)
return expr("ShouldDo(action, {})".format(t))
return Expr("Did")(action[expr('action')], t)
return program
def is_symbol(s):
"""A string s is a symbol if it starts with an alphabetic char."""
return isinstance(s, str) and s[:1].isalpha()
def is_var_symbol(s):
"""A logic variable symbol is an initial-lowercase string."""
return is_symbol(s) and s[0].islower()
def is_prop_symbol(s):
"""A proposition logic symbol is an initial-uppercase string."""
return is_symbol(s) and s[0].isupper()
"""Return a set of the variables in expression s.
>>> variables(expr('F(x, x) & G(x, y) & H(y, z) & R(A, z, 2)')) == {x, y, z}
return {x for x in subexpressions(s) if is_variable(x)}
def is_definite_clause(s):
"""Returns True for exprs s of the form A & B & ... & C ==> D,
where all literals are positive. In clause form, this is
~A | ~B | ... | ~C | D, where exactly one clause is positive.
>>> is_definite_clause(expr('Farmer(Mac)'))
True
"""
if is_symbol(s.op):
return True
antecedent, consequent = s.args
all(is_symbol(arg.op) for arg in conjuncts(antecedent)))
withal
a validé
def parse_definite_clause(s):
"""Return the antecedents and the consequent of a definite clause."""
withal
a validé
assert is_definite_clause(s)
if is_symbol(s.op):
return [], s
else:
antecedent, consequent = s.args
return conjuncts(antecedent), consequent
withal
a validé
A, B, C, D, E, F, G, P, Q, x, y, z = map(Expr, 'ABCDEFGPQxyz')
# ______________________________________________________________________________
def tt_entails(kb, alpha):
"""Does kb entail the sentence alpha? Use truth tables. For propositional
kb's and sentences. [Figure 7.10]. Note that the 'kb' should be an
Expr which is a conjunction of clauses.
>>> tt_entails(expr('P & Q'), expr('Q'))
True
"""
assert not variables(alpha)
def tt_check_all(kb, alpha, symbols, model):
"""Auxiliary routine to implement tt_entails."""
if not symbols:
if pl_true(kb, model):
result = pl_true(alpha, model)
assert result in (True, False)
return result
else:
return True
else:
P, rest = symbols[0], symbols[1:]
return (tt_check_all(kb, alpha, rest, extend(model, P, True)) and
tt_check_all(kb, alpha, rest, extend(model, P, False)))
def prop_symbols(x):
if not isinstance(x, Expr):
elif is_prop_symbol(x.op):
return {symbol for arg in x.args for symbol in prop_symbols(arg)}
return {symbol for arg in x.args for symbol in constant_symbols(arg)}
def predicate_symbols(x):
"""Return a set of (symbol_name, arity) in x.
All symbols (even functional) with arity > 0 are considered."""
if not isinstance(x, Expr) or not x.args:
return set()
pred_set = {(x.op, len(x.args))} if is_prop_symbol(x.op) else set()
pred_set.update({symbol for arg in x.args for symbol in predicate_symbols(arg)})
return pred_set
def tt_true(s):
"""Is a propositional sentence a tautology?
>>> tt_true('P | ~P')
True
"""
def pl_true(exp, model={}):
"""Return True if the propositional logic expression is true in the model,
and False if it is false. If the model does not specify the value for
every proposition, this may return None to indicate 'not obvious';
this may happen even when the expression is tautological."""
op, args = exp.op, exp.args
return model.get(exp)
elif op == '~':
p = pl_true(args[0], model)
if p is None:
return None
else:
return not p
elif op == '|':
result = False
for arg in args:
p = pl_true(arg, model)
if p is True:
return True
if p is None:
result = None
return result
elif op == '&':
result = True
for arg in args:
p = pl_true(arg, model)
if p is False:
return False
if p is None:
result = None
return result
p, q = args
return pl_true(~p | q, model)
return pl_true(p | ~q, model)
pt = pl_true(p, model)
qt = pl_true(q, model)
if op == '<=>':
return pt == qt
elif op == '^': # xor or 'not equivalent'
return pt != qt
else:
raise ValueError("illegal operator in logic expression" + str(exp))
# ______________________________________________________________________________
def to_cnf(s):
"""Convert a propositional logical sentence to conjunctive normal form.
That is, to the form ((A | ~B | ...) & (B | C | ...) & ...) [p. 253]
(~B & ~C)
"""
if isinstance(s, str):
s = expr(s)
s = eliminate_implications(s) # Steps 1, 2 from p. 253
s = move_not_inwards(s) # Step 3
return distribute_and_over_or(s) # Step 4
def eliminate_implications(s):
"""Change implications into equivalent form with only &, |, and ~ as logical operators."""
a, b = args[0], args[-1]
return b | ~a
return a | ~b
elif s.op == '<=>':
return (a | ~b) & (b | ~a)
assert len(args) == 2 # TODO: relax this restriction
return Expr(s.op, *args)
def move_not_inwards(s):
"""Rewrite sentence s by moving negation sign inward.
>>> move_not_inwards(~(A | B))
if s.op == '~':
def NOT(b):
return move_not_inwards(~b)
a = s.args[0]
if a.op == '~':
return move_not_inwards(a.args[0]) # ~~A ==> A
if a.op == '&':
return associate('|', list(map(NOT, a.args)))
if a.op == '|':
return associate('&', list(map(NOT, a.args)))
return s
elif is_symbol(s.op) or not s.args:
return s
else:
return Expr(s.op, *list(map(move_not_inwards, s.args)))
def distribute_and_over_or(s):
"""Given a sentence s consisting of conjunctions and disjunctions
of literals, return an equivalent sentence in CNF.
>>> distribute_and_over_or((A & B) | C)
((A | C) & (B | C))
"""
if s.op == '|':
if s.op != '|':
return distribute_and_over_or(s)
return distribute_and_over_or(s.args[0])
conj = first(arg for arg in s.args if arg.op == '&')
if not conj:
others = [a for a in s.args if a is not conj]
rest = associate('|', others)
return associate('&', [distribute_and_over_or(c | rest)
for c in conj.args])
elif s.op == '&':
return associate('&', list(map(distribute_and_over_or, s.args)))
else:
return s
def associate(op, args):
"""Given an associative op, return an expression with the same
meaning as Expr(op, *args), but flattened -- that is, with nested
instances of the same op promoted to the top level.
>>> associate('&', [(A&B),(B|C),(B&C)])
(A & B & (B | C) & B & C)
>>> associate('|', [A|(B|(C|(A&B)))])
if len(args) == 0:
return _op_identity[op]
elif len(args) == 1:
return args[0]
else:
return Expr(op, *args)
_op_identity = {'&': True, '|': False, '+': 0, '*': 1}
"""Given an associative op, return a flattened list result such
that Expr(op, *result) means the same as Expr(op, *args)."""
result = []
if arg.op == op:
collect(arg.args)
else:
result.append(arg)
def conjuncts(s):
"""Return a list of the conjuncts in the sentence s.
>>> conjuncts(A & B)
[A, B]
>>> conjuncts(A | B)
[(A | B)]
"""
def disjuncts(s):
"""Return a list of the disjuncts in the sentence s.
>>> disjuncts(A | B)
[A, B]
>>> disjuncts(A & B)
[(A & B)]
"""
# ______________________________________________________________________________
def pl_resolution(KB, alpha):
"""Propositional-logic resolution: say if alpha follows from KB. [Figure 7.12]"""
clauses = KB.clauses + conjuncts(to_cnf(~alpha))
new = set()
while True:
n = len(clauses)
pairs = [(clauses[i], clauses[j])
for i in range(n) for j in range(i+1, n)]
for (ci, cj) in pairs:
resolvents = pl_resolve(ci, cj)
new = new.union(set(resolvents))
for c in new:
def pl_resolve(ci, cj):
"""Return all clauses that can be obtained by resolving clauses ci and cj."""
clauses = []
for di in disjuncts(ci):
for dj in disjuncts(cj):
if di == ~dj or ~di == dj:
removeall(dj, disjuncts(cj)))
clauses.append(associate('|', dnew))
return clauses
# ______________________________________________________________________________
"""A KB of propositional definite clauses."""
def tell(self, sentence):
assert is_definite_clause(sentence), "Must be definite clause"
self.clauses.append(sentence)
"""Yield the empty substitution if KB implies query; else nothing."""
if pl_fc_entails(self.clauses, query):
yield {}
def retract(self, sentence):
self.clauses.remove(sentence)
def clauses_with_premise(self, p):
"""Return a list of the clauses in KB that have p in their premise.
This could be cached away for O(1) speed, but we'll recompute it."""
if c.op == '==>' and p in conjuncts(c.args[0])]
def pl_fc_entails(KB, q):
"""Use forward chaining to see if a PropDefiniteKB entails symbol q.
[Figure 7.15]
Surya Teja Cheedella
a validé
>>> pl_fc_entails(horn_clauses_KB, expr('Q'))
True
"""
count = {c: len(conjuncts(c.args[0]))
for c in KB.clauses
if c.op == '==>'}
agenda = [s for s in KB.clauses if is_prop_symbol(s.op)]
while agenda:
p = agenda.pop()
if not inferred[p]:
inferred[p] = True
for c in KB.clauses_with_premise(p):
count[c] -= 1
if count[c] == 0:
agenda.append(c.args[1])
return False
Surya Teja Cheedella
a validé
""" [Figure 7.13]
Simple inference in a wumpus world example
"""
wumpus_world_inference = expr("(B11 <=> (P12 | P21)) & ~B11")
Surya Teja Cheedella
a validé
""" [Figure 7.16]
Propositional Logic Forward Chaining example
"""
horn_clauses_KB = PropDefiniteKB()
for s in "P==>Q; (L&M)==>P; (B&L)==>M; (A&P)==>L; (A&B)==>L; A;B".split(';'):
Surya Teja Cheedella
a validé
horn_clauses_KB.tell(expr(s))
# ______________________________________________________________________________
# DPLL-Satisfiable [Figure 7.17]
def dpll_satisfiable(s):
"""Check satisfiability of a propositional sentence.
This differs from the book code in two ways: (1) it returns a model
rather than True when it succeeds; this is more useful. (2) The
function find_pure_symbol is passed a list of unknown clauses, rather
than a list of all clauses and the model; this is more efficient."""
clauses = conjuncts(to_cnf(s))
return dpll(clauses, symbols, {})
def dpll(clauses, symbols, model):
"""See if the clauses are true in a partial model."""
unknown_clauses = [] # clauses with an unknown truth value
for c in clauses:
return False
unknown_clauses.append(c)
if not unknown_clauses:
return model
P, value = find_pure_symbol(symbols, unknown_clauses)
if P:
return dpll(clauses, removeall(P, symbols), extend(model, P, value))
P, value = find_unit_clause(clauses, model)
if P:
return dpll(clauses, removeall(P, symbols), extend(model, P, value))
if not symbols:
raise TypeError("Argument should be of the type Expr.")
P, symbols = symbols[0], symbols[1:]
return (dpll(clauses, symbols, extend(model, P, True)) or
dpll(clauses, symbols, extend(model, P, False)))
def find_pure_symbol(symbols, clauses):
"""Find a symbol and its value if it appears only as a positive literal
(or only as a negative) in clauses.
>>> find_pure_symbol([A, B, C], [A|~B,~B|~C,C|A])
(A, True)
"""
for s in symbols:
found_pos, found_neg = False, False
if not found_pos and s in disjuncts(c):
found_pos = True
if not found_neg and ~s in disjuncts(c):
found_neg = True
if found_pos != found_neg:
return s, found_pos
return None, None
def find_unit_clause(clauses, model):
"""Find a forced assignment if possible from a clause with only 1
variable not bound in the model.
>>> find_unit_clause([A|B|C, B|~C, ~A|~B], {A:True})
(B, False)
"""
for clause in clauses:
return None, None
def unit_clause_assign(clause, model):
"""Return a single variable/value pair that makes clause true in
the model, if possible.
>>> unit_clause_assign(A|B|C, {A:True})
(None, None)
>>> unit_clause_assign(B|~C, {A:True})
(None, None)
>>> unit_clause_assign(~A|~B, {A:True})
(B, False)
"""
P, value = None, None
for literal in disjuncts(clause):
sym, positive = inspect_literal(literal)
if sym in model:
if model[sym] == positive:
return None, None # clause already True
elif P:
return None, None # more than 1 unbound variable
else:
P, value = sym, positive
return P, value
def inspect_literal(literal):
"""The symbol in this literal, and the value it should take to
make the literal true.
>>> inspect_literal(P)
(P, True)
>>> inspect_literal(~P)
(P, False)
"""
if literal.op == '~':
# ______________________________________________________________________________
# Walk-SAT [Figure 7.18]
def WalkSAT(clauses, p=0.5, max_flips=10000):
"""Checks for satisfiability of all clauses by randomly flipping values of variables
"""
symbols = {sym for clause in clauses for sym in prop_symbols(clause)}
# model is a random assignment of true/false to the symbols in clauses
model = {s: random.choice([True, False]) for s in symbols}
for i in range(max_flips):
satisfied, unsatisfied = [], []
for clause in clauses:
(satisfied if pl_true(clause, model) else unsatisfied).append(clause)
if not unsatisfied: # if model satisfies all the clauses
return model
clause = random.choice(unsatisfied)
if probability(p):
# Flip the symbol in clause that maximizes number of sat. clauses
# Return the the number of clauses satisfied after flipping the symbol.
model[sym] = not model[sym]
count = len([clause for clause in clauses if pl_true(clause, model)])
model[sym] = not model[sym]
return count
model[sym] = not model[sym]
# If no solution is found within the flip limit, we return failure
# ______________________________________________________________________________
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class WumpusKB(PropKB):
"""
Create a Knowledge Base that contains the atemporal "Wumpus physics" and temporal rules with time zero.
"""
def __init__(self,dimrow):
super().__init__()
self.dimrow = dimrow
self.tell('( NOT W1s1 )')
self.tell('( NOT P1s1 )')
for i in range(1, dimrow+1):
for j in range(1, dimrow+1):
bracket = 0
sentence_b_str = "( B" + i + "s" + j + " <=> "
sentence_s_str = "( S" + i + "s" + j + " <=> "
if i > 1:
sentence_b_str += "( P" + (i-1) + "s" + j + " OR "
sentence_s_str += "( W" + (i-1) + "s" + j + " OR "
bracket += 1
if i < dimRow:
sentence_b_str += "( P" + (i+1) + "s" + j + " OR "
sentence_s_str += "( W" + (i+1) + "s" + j + " OR "
bracket += 1
if j > 1:
if j == dimRow:
sentence_b_str += "P" + i + "s" + (j-1) + " "
sentence_s_str += "W "+ i + "s" + (j-1) + " "
else:
sentence_b_str += "( P" + i + "s" + (j-1) + " OR "
sentence_s_str += "( W" + i + "s" + (j-1) + " OR "
bracket += 1
if j < dimRow:
sentence_b_str += "P" + i + "s" + (j+1) + " "
sentence_s_str += "W" + i + "s" + (j+1) + " "
for _ in range(bracket):
sentence_b_str += ") "
sentence_s_str += ") "
sentence_b_str += ") "
sentence_s_str += ") "
self.tell(sentence_b_str)
self.tell(sentence_s_str)
## Rule that describes existence of at least one Wumpus
sentence_w_str = ""
for i in range(1, dimrow+1):
for j in range(1, dimrow+1):
if (i == dimrow) and (j == dimrow):
sentence_w_str += " W" + dimRow + "s" + dimrow + " "
else:
sentence_w_str += "( W" + i + "s" + j + " OR "
for _ in range(dimrow**2):
sentence_w_str += ") "
self.tell(sentence_w_str)
## Rule that describes existence of at most one Wumpus
for i in range(1, dimrow+1):
for j in range(1, dimrow+1):
for u in range(1, dimrow+1):
for v in range(1, dimrow+1):
if i!=u or j!=v:
self.tell("( ( NOT W" + i + "s" + j + " ) OR ( NOT W" + u + "s" + v + " ) )")
## Temporal rules at time zero
self.tell("L1s1s0")
for i in range(1, dimrow+1):
for j in range(1, dimrow + 1):
self.tell("( L" + i + "s" + j + "s0 => ( Breeze0 <=> B" + i + "s" + j + " ) )")
self.tell("( L" + i + "s" + j + "s0 => ( Stench0 <=> S" + i + "s" + j + " ) )")
if i != 1 or j != 1:
self.tell("( NOT L" + i + "s" + j + "s" + "0 )")
self.tell("WumpusAlive0")
self.tell("HaveArrow0")
self.tell("FacingEast0")
self.tell("( NOT FacingWest0 )")
self.tell("( NOT FacingNorth0 )")
self.tell("( NOT FacingSouth0 )")
def make_action_sentence(self, action, time):
self.tell(action + time)
def make_percept_sentence(self, percept, time):
self.tell(percept + time)
def add_temporal_sentences(self, time):
if time == 0:
return
t = time - 1
## current location rules (L2s2s3 represent tile 2,2 at time 3)
## ex.: ( L2s2s3 <=> ( ( L2s2s2 AND ( ( NOT Forward2 ) OR Bump3 ) )
## OR ( ( L1s2s2 AND ( FacingEast2 AND Forward2 ) ) OR ( L2s1s2 AND ( FacingNorth2 AND Forward2 ) ) )
for i in range(1, self.dimrow+1):
for j in range(1, self.dimrow+1):
self.tell("( L" + i + "s" + j + "s" + time + " => ( Breeze" + time + " <=> B" + i + "s" + j + " ) )")
self.tell("( L" + i + "s" + j + "s" + time + " => ( Stench" + time + " <=> S" + i + "s" + j + " ) )")
s = "( L" + i + "s" + j + "s" + time + " <=> ( ( L" + i + "s" + j + "s" + t + " AND ( ( NOT Forward"\
+ t + " ) OR Bump" + time + " ) )"
count = 2
if i != 1:
s += " OR ( ( L" + (i - 1) + "s" + j + "s" + t + " AND ( FacingEast" + t + " AND Forward" + t\
+ " ) )"
count += 1
if i != self.dimrow:
s += " OR ( ( L" + (i + 1) + "s" + j + "s" + t + " AND ( FacingWest" + t + " AND Forward" + t\
+ " ) )"
count += 1
if j != 1:
if j == self.dimrow:
s += " OR ( L" + i + "s" + (j - 1) + "s" + t + " AND ( FacingNorth" + t + " AND Forward" + t\
+ " ) )"
else:
s += " OR ( ( L" + i + "s" + (j - 1) + "s" + t + " AND ( FacingNorth" + t + " AND Forward" \
+ t + " ) )"
count += 1
if j != self.dimrow:
s += " OR ( L" + i + "s" + (j + 1) + "s" + t + " AND ( FacingSouth" + t + " AND Forward" + t\
+ " ) )"
for _ in range(count):
s += " )"
## add sentence about location i,j
self.tell(s)
## add sentence about safety of location i,j
self.tell("( OK" + i + "s" + j + "s" + time + " <=> ( ( NOT P" + i + "s" + j + " ) AND ( NOT ( W" + i\
+ "s" + j + " AND WumpusAlive" + time + " ) ) ) )")
## Rules about current orientation
## ex.: ( FacingEast3 <=> ( ( FacingNorth2 AND TurnRight2 ) OR ( ( FacingSouth2 AND TurnLeft2 )
## OR ( FacingEast2 AND ( ( NOT TurnRight2 ) AND ( NOT TurnLeft2 ) ) ) ) ) )
a = "( FacingNorth" + t + " AND TurnRight" + t + " )"
b = "( FacingSouth" + t + " AND TurnLeft" + t + " )"
c = "( FacingEast" + t + " AND ( ( NOT TurnRight" + t + " ) AND ( NOT TurnLeft" + t + " ) ) )"
s = "( FacingEast" + (t + 1) + " <=> ( " + a + " OR ( " + b + " OR " + c + " ) ) )"
this.tell(s)
a = "( FacingNorth" + t + " AND TurnLeft" + t + " )"
b = "( FacingSouth" + t + " AND TurnRight" + t + " )"
c = "( FacingWest" + t + " AND ( ( NOT TurnRight" + t + " ) AND ( NOT TurnLeft" + t + " ) ) )"
s = "( FacingWest" + (t + 1) + " <=> ( " + a + " OR ( " + b + " OR " + c + " ) ) )"
this.tell(s)
a = "( FacingEast" + t + " AND TurnLeft" + t + " )"
b = "( FacingWest" + t + " AND TurnRight" + t + " )"
c = "( FacingNorth" + t + " AND ( ( NOT TurnRight" + t + " ) AND ( NOT TurnLeft" + t + " ) ) )"
s = "( FacingNorth" + (t + 1) + " <=> ( " + a + " OR ( " + b + " OR " + c + " ) ) )"
this.tell(s)
a = "( FacingWest" + t + " AND TurnLeft" + t + " )"
b = "( FacingEast" + t + " AND TurnRight" + t + " )"
c = "( FacingSouth" + t + " AND ( ( NOT TurnRight" + t + " ) AND ( NOT TurnLeft" + t + " ) ) )"
s = "( FacingSouth" + (t + 1) + " <=> ( " + a + " OR ( " + b + " OR " + c + " ) ) )"
this.tell(s)
## Rules about last action
self.tell("( Forward" + t + " <=> ( NOT TurnRight" + t + " ) )")
self.tell("( Forward" + t + " <=> ( NOT TurnLeft" + t + " ) )")
##Rule about the arrow
self.tell("( HaveArrow" + time + " <=> ( HaveArrow" + (time - 1) + " AND ( NOT Shot" + (time - 1) + " ) ) )")
##Rule about Wumpus (dead or alive)
self.tell("( WumpusAlive" + time + " <=> ( WumpusAlive" + (time - 1) + " AND ( NOT Scream" + time + " ) ) )")
# ______________________________________________________________________________
class WumpusPosition():
def __init__(self, X, Y, orientation):
self.X = X
self.Y = Y
self.orientation = orientation
def get_location(self):
return self.X, self.Y
def get_orientation(self):
return self.orientation
def equals(self, wumpus_position):
if wumpus_position.get_location() == self.get_location() and \
wumpus_position.get_orientation()==self.get_orientation():
return True
else:
return False
# ______________________________________________________________________________
"""An agent for the wumpus world that does logical inference. [Figure 7.20]"""
def __init__(self):
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super().__init__()
self.dimrow = 3
self.kb = WumpusKB(self.dimrow)
self.t = 0
self.plan = list()
self.current_position = WumpusPosition(1, 1, 'UP')
def execute(self, percept):
self.kb.make_percept_sentence(percept, self.t)
self.kb.add_temporal_sentences(self.t)
temp = list()
for i in range(1, self.dimrow+1):
for j in range(1, self.dimrow+1):
if self.kb.ask_with_dpll('L' + i + 's' + j + 's' + self.t):
temp.append(i)
temp.append(j)
if self.kb.ask_with_dpll('FacingNorth' + self.t):
self.current_position = WumpusPosition(temp[0], temp[1], 'UP')
elif self.kb.ask_with_dpll('FacingSouth' + self.t):
self.current_position = WumpusPosition(temp[0], temp[1], 'DOWN')
elif self.kb.ask_with_dpll('FacingWest' + self.t):
self.current_position = WumpusPosition(temp[0], temp[1], 'LEFT')
elif self.kb.ask_with_dpll('FacingEast' + self.t):
self.current_position = WumpusPosition(temp[0], temp[1], 'RIGHT')
safe_points = list()
for i in range(1, self.dimrow+1):
for j in range(1, self.dimrow+1):
if self.kb.ask_with_dpll('OK' + i + 's' + j + 's' + self.t):
safe_points.append([i, j])
if self.kb.ask_with_dpll('Glitter' + self.t):
goals = list()
goals.append([1, 1])
self.plan.append('Grab')
actions = plan_route(self.current_position,goals,safe_points)
for action in actions:
self.plan.append(action)
self.plan.append('Climb')
if len(self.plan) == 0:
unvisited = list()
for i in range(1, self.dimrow+1):
for j in range(1, self.dimrow+1):
for k in range(1, self.dimrow+1):
if self.kb.ask_with_dpll("L" + i + "s" + j + "s" + k):
unvisited.append([i, j])
unvisited_and_safe = list()
for u in unvisited:
for s in safe_points:
if u not in unvisited_and_safe and s == u:
unvisited_and_safe.append(u)
temp = plan_route(self.current_position,unvisited_and_safe,safe_points)
for t in temp:
self.plan.append(t)
if len(self.plan) == 0 and self.kb.ask_with_dpll('HaveArrow' + self.t):
possible_wumpus = list()
for i in range(1, self.dimrow+1):
for j in range(1, self.dimrow+1):
if not self.kb.ask_with_dpll('W' + i + 's' + j):
possible_wumpus.append([i, j])
temp = plan_shot(self.current_position, possible_wumpus, safe_points)
for t in temp:
self.plan.append(t)
if len(self.plan) == 0:
not_unsafe = list()
for i in range(1, self.dimrow+1):
for j in range(1, self.dimrow+1):
if not self.kb.ask_with_dpll('OK' + i + 's' + j + 's' + self.t):
not_unsafe.append([i, j])
temp = plan_route(self.current_position, not_unsafe, safe_points)
for t in temp:
self.plan.append(t)
if len(self.plan) == 0:
start = list()
start.append([1, 1])
temp = plan_route(self.current_position, start, safe_points)
for t in temp:
self.plan.append(t)
self.plan.append('Climb')
action = self.plan[1:]
self.kb.make_action_sentence(action, self.t)
self.t += 1
return action