The Basics

An expert system is a program capable of pairing up a set of facts with a set of rules to those facts, and execute some actions based on the matching rules.

Facts

Facts are the basic unit of information of PyKnow. They are used by the system to reason about the problem.

Let’s enumerate some facts about Facts, so… metafacts ;)

  1. The class Fact is a subclass of dict.

    >>> f = Fact(a=1, b=2)
    >>> f['a']
    1
    
  2. Therefore a Fact does not mantain an internal order of items.

    >>> Fact(a=1, b=2)  # Order is arbirary :O
    Fact(b=2, a=1)
    
  3. In contrast to dict, you can create a Fact without keys (only values), and Fact will create a numeric index for your values.

    >>> f = Fact('x', 'y', 'z')
    >>> f[0]
    'x'
    
  4. You can mix autonumeric values with key-values, but autonumeric must be declared first:

    >>> f = Fact('x', 'y', 'z', a=1, b=2)
    >>> f[1]
    'y'
    >>> f['b']
    2
    
  5. You can subclass Fact to express different kinds of data or extend it with your custom functionality.

    class Alert(Fact):
        """The alert level."""
        pass
    
    class Status(Fact):
        """The system status."""
        pass
    
    f1 = Alert('red')
    f2 = Status('critical')
    
    from pyknow import Fact
    from django.contrib.auth.models import User as DjangoUser
    
    class User(Fact):
        @classmethod
        def from_django_model(cls, obj):
            return cls(pk=obj.pk,
                       name=obj.name,
                       email=obj.email)
    
        def save_to_db(self):
            return DjangoUser.create(**self)
    
  6. Fact fields can be validated automatically for you if you define them using Field. Field uses the Schema library internally for data validation. Also, a field can be declared mandatory or have a default.

Rules

In PyKnow a rule is a callable, decorated with Rule.

Rules have two components, LHS (left-hand-side) and RHS (right-hand-side).

  • The LHS describes (using patterns) the conditions on which the rule * should be executed (or fired).
  • The RHS is the set of actions to perform when the rule is fired.

For a Fact to match a Pattern, all pattern restrictions must be True when the Fact is evaluated against it.

class MyFact(Fact):
    pass

@Rule(MyFact())  # This is the LHS
def match_with_every_myfact():
    """This rule will match with every instance of `MyFact`."""
    # This is the RHS
    pass

@Rule(Fact('animal', family='felinae'))
def match_with_cats():
    """
    Match with every `Fact` which:

      * f[0] == 'animal'
      * f['family'] == 'felinae'

    """
    print("Meow!")

You can use logic operators to express complex LHS conditions.

@Rule(
    AND(
        OR(User('admin'),
           User('root')),
        NOT(Fact('drop-privileges'))
    )
)
def the_user_has_power():
    """
    The user is a privileged one and we are not dropping privileges.

    """
    enable_superpowers()

For a Rule to be useful, it must be a method of a KnowledgeEngine subclass.

Note

For a list of more complex operators you can check the pyknow.operator module.

Facts vs Patterns

The difference between Facts and Patterns is small. In fact, Patterns are just Facts containing Pattern Conditional Elements instead of regular data. They are used only in the LHS of a rule.

If you don’t provide the content of a pattern as a PCE, PyKnow will enclose the value in a LiteralPCE automatically for you.

Also, you can’t declare any Fact containing a PCE, if you do, you will receive a nice exception back.

>>> ke = KnowledgeEngine()
>>> ke.declare(Fact(L("hi")))
Traceback (most recent call last):
  File "<ipython-input-4-b36cff89278d>", line 1, in <module>
    ke.declare(Fact(L('hi')))
  File "/home/pyknow/pyknow/engine.py", line 210, in declare
    self.__declare(*facts)
  File "/home/pyknow/pyknow/engine.py", line 191, in __declare
    "Declared facts cannot contain conditional elements")
TypeError: Declared facts cannot contain conditional elements

DefFacts

Most of the time expert systems needs a set of facts to be present for the system to work. This is the purpose of the DefFacts decorator.

@DefFacts()
def needed_data():
    yield Fact(best_color="red")
    yield Fact(best_body="medium")
    yield Fact(best_sweetness="dry")

All DefFacts inside a KnowledgeEngine will be called every time the reset method is called.

Note

The decorated method MUST be generators.

New in version 1.7.0: The reset() method accepts any number of keyword parameters whose gets passed to DefFacts decorated methods if those methods present the same parameters.

KnowledgeEngine

This is the place where all the magic happens.

The first step is to make a subclass of it and use Rule to decorate its methods.

After that, you can instantiate it, populate it with facts, and finally run it.

greet.py
from pyknow import *

class Greetings(KnowledgeEngine):
    @DefFacts()
    def _initial_action(self):
        yield Fact(action="greet")

    @Rule(Fact(action='greet'),
          NOT(Fact(name=W())))
    def ask_name(self):
        self.declare(Fact(name=input("What's your name? ")))

    @Rule(Fact(action='greet'),
          NOT(Fact(location=W())))
    def ask_location(self):
        self.declare(Fact(location=input("Where are you? ")))

    @Rule(Fact(action='greet'),
          Fact(name=MATCH.name),
          Fact(location=MATCH.location))
    def greet(self, name, location):
        print("Hi %s! How is the weather in %s?" % (name, location))

engine = Greetings()
engine.reset()  # Prepare the engine for the execution.
engine.run()  # Run it!
$ python greet.py
What's your name? Roberto
Where are you? Madrid
Hi Roberto! How is the weather in Madrid?

Handling facts

The following methods are used to manipulate the set of facts the engine knows about.

declare

Adds a new fact to the factlist (the list of facts known by the engine).

>>> engine = KnowledgeEngine()
>>> engine.reset()
>>> engine.declare(Fact(score=5))
<f-1>
>>> engine.facts
<f-0> InitialFact()
<f-1> Fact(score=5)

Note

The same fact can’t be declared twice unless facts.duplication is set to True.

retract

Removes an existing fact from the factlist.

Both, the index and the fact can be used with retract
>>> engine.facts
<f-0> InitialFact()
<f-1> Fact(score=5)
<f-2> Fact(color='red')
>>> engine.retract(1)
>>> engine.facts
<f-0> InitialFact()
<f-2> Fact(color='red')

modify

Retracts some fact from the factlist and declares a new one with some changes. Changes are passed as arguments.

>>> engine.facts
<f-0> InitialFact()
<f-1> Fact(color='red')
>>> engine.modify(engine.facts[1], color='yellow', blink=True)
<f-2>
>>> engine.facts
<f-0> InitialFact()
<f-2> Fact(color='yellow', blink=True)

duplicate

Adds a new fact to the factlist using an existing fact as a template and adding some modifications.

>>> engine.facts
<f-0> InitialFact()
<f-1> Fact(color='red')
>>> engine.duplicate(engine.facts[1], color='yellow', blink=True)
<f-2>
>>> engine.facts
<f-0> InitialFact()
<f-1> Fact(color='red')
<f-2> Fact(color='yellow', blink=True)

Engine execution procedure

This is the usual process to execute a KnowledgeEngine.

  1. The class must be instantiated, of course.
  2. The reset method must be called:
    • This declares the special fact InitialFact. Necessary for some rules to work properly.
    • Declare all facts yielded by the methods decorated with @DefFacts.
  3. The run method must be called. This starts the cycle of execution.

Cycle of execution

In a conventional programming style, the starting point, the stopping point, and the sequence of operations are defined explicitly by the programmer. With PyKnow, the program flow does not need to be defined quite so explicitly. The knowledge (Rules) and the data (Facts) are separated, and the KnowledgeEngine is used to apply the knowledge to the data.

The basic execution cycle is as follows:

  1. If the rule firing limit has been reached the execution is halted.
  2. The top rule on the agenda is selected for execution. If there are no rules on the agenda, the execution is halted.
  3. The RHS actions of the selected rule are executed (the method is called). As a result, rules may be activated or deactivated. Activated rules (those rules whose conditions are currently satisfied) are placed on the agenda. The placement on the agenda is determined by the salience of the rule and the current conflict resolution strategy. Deactivated rules are removed from the agenda.

Difference between DefFacts and declare

Both are used to declare facts on the engine instance, but:

  • declare adds the facts directly to the working memory.
  • Generators declared with DefFacts are called by the reset method, and all the yielded facts they are added to the working memory using declare.