py_rete

Introduction

The py_rete project aims to implement a Rete engine in native python. This system is built using one the description of the Rete algorithms provided by Doorenbos (1995). It also makes heavy use of ideas from the Experta project (although no code is used from this project as it utilizes an LGPL license).

The purpose of this system is to support basic expert / production system AI capabilities in a way that is easy to integrate with other Python based AI/ML systems.

Installation

This package is installable via pip with the following command: pip install -U py_rete.

It can also be installed directly from GitHub with the following command: pip install -U git+https://github.com/cmaclell/py_rete@master

The Basics

The two high-level structures to support reasoning with py_rete are facts and productions.

Facts

Facts represent the basic units of knowledge that the productions match over. Here are a few examples of facts and how they work.

1. Facts are a subclass of dict, so you can treat them similar to dictionaries.

>>> f = Fact(a=1, b=2)
>>> f['a']
1

2. Facts extend dictionaries, so they also support positional values without keys. These values are assigned numerical indices based on their position.

>>> f = Fact('a', 'b', 'c')
>>> f[0]
'a'

3. Facts can support mixed positional and named arguments, but positional must come before named and named arguments do not get positional references.

>>> f = Fact('a', 'b', c=3, d=4)
>>> f[0]
'a'
>>> f['c']
3

5. Facts support nesting with other facts.

>>> f = Fact(subfact=Fact())
Fact(subfact=Fact())

Note that there will be issues if facts contain other data structures that contain facts (they will not be properly added to the rete network or to productions).

Productions

Similar to Experta's rules, Productions are functions that are decorated with conditions that govern when they execute and bind the arguments necessary for their execution.

Productions have two components:

• Conditions, which are essentially facts that can contain pattern matching variables.
• A Function, which is executed for each rule match, with the arguments to the function being passed the bindings from pattern matching variables.

Here is an example of a simple Productions that binds with all Facts that have the color red and prints 'I found something red' for each one:

@Production(Fact(color='red'))
def alert_something_red():
    print("I found something red")

Productions also support logical operators to express more complex conditions.

@Production(AND(OR(Fact(color='red'),
                   Fact(color='blue')),
	        NOT(Fact(color='green'))))
def alert_something_complex():
    print("I found something red or blue without any green present")

Bitwise logical operators can be used as shorthand to make composing complex conditions easier.

@Production((Fact(color='red') | Fact(color='blue')) & ~Fact(color='green'))
def alert_something_complex2():
    print("I found something red or blue without any green present")

In addition to matching simple facts, pattern matching variables can be used to match values from Facts. Matching ensures that variable bindings are consistent across conditions. Additionally, variables are passed to arguments in the function with the same name during matching. For example, the following production finds a Fact with a lastname attribute. For each Fact it finds, it prints "I found a fact with a lastname attribute: <lastname>". Note, the V('lastname') corresponds to a variable named lastname that can bind with values from Facts during matching. Additionally the variable (V('lastname')) and the function argument lastname match have the same name, which enables the matcher to the variable bindings into the function.

@Production(Fact(lastname=V('lastname')))
def found_relatives(lastname):
    print("I found a fact with a lastname: {}".format(lastname))

It is also possible to employ functional tests (lambdas or functions) using Filter conditions. Like the function that is being decorated, Filter conditions pass variable bindings to their equivelently named function arguments. It is important to note that positive facts that bind with these variables need to be listed in the production before the tests that use them.

@Production(Fact(value=V('a')) &
            Fact(value=V('b')) &
            Filter(lambda a, b: a > b) &
            Fact(value=V('c')) &
            Filter(lambda b, c: b > c))
def three_values(a, b, c):
    print("{} is greater than {} is greater than {}".format(a, b, c))

It is also possible to bind facts to variables as well, using the bitshift operator.

@Production(V('name_fact') << Fact(name=V('name')))
def found_name(name_fact):
    print("I found a name fact {}".format(name_fact))

ReteNetwork

To engage in reasoning facts and productions are loaded into a ReteNetwork, which facilitates the matching and application of productions to facts.

Here is how you create a network:

net = ReteNetwork()

Once a network has been created, then facts can be added to it.

f1 = Fact(light_color="red")
net.add_fact(f1)

Note, facts added to the network cannot contain any variables or they will trigger an exception when added. Additionally, once a fact has been added to network it is assigned a unique internal identifier.

This makes it possible to update the fact.

f1['light_color'] = "green"
net.update_fact(f1)

It also make it possible to remove the fact.

net.remove_fact(f1)

When updating a fact, note that it is not updated in the network until the update_fact method is called on it. An update essentially equates to removing and re-adding the fact.

Productions can also be added to the network. Productions also can make use of the net variable, which is automatically bound to the Rete network the production has been added to. This makes it possible for productions to update the contents of the network when they are fired. For example, the following functions have an argument called net that is bound to the rete network even though there is no variable by that name in the production conditions.

>>> from py_rete.network import ReteNetwork
>>> from py_rete.production import Production
>>> from py_rete.fact import Fact
>>> from py_rete.common import V
>>>
>>> f1 = Fact(light_color="red")
>>> 
>>> @Production(V('fact') << Fact(light_color="red"))
>>> def make_green(net, fact):
>>>     print('making green')
>>>     fact['light_color'] = 'green'
>>>     net.update_fact(fact)
>>> 
>>> @Production(V('fact') << Fact(light_color="green"))
>>> def make_red(net, fact):
>>>     print('making red')
>>>     fact['light_color'] = 'red'
>>>     net.update_fact(fact)
>>> 
>>> light_net = ReteNetwork()
>>> light_net.add_fact(f1)
>>> light_net.add_production(make_green)
>>> light_net.add_production(make_red)

Once the above fact and productions have been added the network can be run.

>>> light_net.run(5)
making green
making red
making green
making red
making green

The number passed to run denotes how many rules the network should fire before terminating.

In addition to this high-level function for running the network, there are also some lower-level capabilities that can be used to more closely control the rule execution.

For example, you can get all the production matches from the matches property.

matches = list(light_net.matches)

You can also get just the new matches.

new = list(light_net.new_matches)

You can fire one of the matches.

>>> matches[0].fire()
making red