Rules in Artificial Intelligence. Short introduction at Ecole 42 for an AI Meetup. Symbolic and non symbolic approaches in AI.

1. © 2015 IBM Corporation
Rules in Artificial Intelligence
Dec 2015 – Presentation at Ecole 42
Pierre Feillet – IBM Decision automation architect
feillet@fr.ibm.com

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Agenda
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 Origins
 Expert System -> Inference Engine -> Rules
 Current state
 From raw inference engine to Entreprise decision automation
 Business Rules in Bluemix

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RULES TO MIMIC HUMAN MIND
From Expert Systems to Operation Decision Management

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Expert Systems
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 An expert system is a computer system that emulates the decision-making ability
of a human expert. Expert systems are designed to solve complex problems by
reasoning about knowledge, represented primarily as if–then rules rather than
through conventional procedural code.
 The first expert systems were created in the 1970s and then proliferated in the
1980s. Expert systems were among the first truly successful forms of AI software.
Expert systems were introduced by the Stanford Heuristic Programming Project.
Applied to domains where expertise was highly valued and complex, such as
diagnosing infectious diseases (Mycin).
 The typical expert system consisted of a knowledge base and an inference engine.
 The knowledge base stored facts about the world.
 The inference engine applied logical rules to the knowledge base and deduced
new knowledge. This process would iterate as each new fact in the knowledge
base could trigger additional rules in the inference engine.

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Rule Logic
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 2 primarily modes of rule inference: forward chaining and backward chaining.
 Forward chaining starts with the known facts and asserts new facts.
Ex: Socrate is Human so he is mortal
 Backward chaining starts with goals, and works backward to determine what facts
must be asserted so that the goals can be achieved.
Ex: Is Socrate mortal? It would search through the knowledge base and
determine if Socrates was Human and if so would assert he is also Mortal.
 Can include a common technique was to integrate the inference engine with a
user interface to ask questions when facts are not enough and would then use
that information accordingly.

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Rule Logic
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 An inference engine cycles through three sequential steps: match rules, select
rules, and execute rules. The execution of the rules will often result in new facts or
goals being added to the knowledge base which will trigger the cycle to repeat.
This cycle continues until no new rules can be matched.
 In the first step, match rules, the inference engine finds all of the rules that are
triggered by the current contents of the knowledge base. In forward chaining the
engine looks for rules where the antecedent (left hand side) matches some fact in
the knowledge base. In backward chaining the engine looks for antecedents that
can satisfy one of the current goals.
 In the second step select rules, the inference engine prioritizes the various rules
that were matched to determine the order to execute them.
 In the final step, execute rules, the engine executes each matched rule in the
order determined in step two and then iterates back to step one again. The cycle
continues until no new rules are matched.
 Rule engine algorithms: RETE, IBM Fastpath & Sequential, etc
 Stateless & stateful processing

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From Expert Systems to Operational Decision Management
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 Goal: Empower Business Users to author,
test, simulate, deploy their decision logic
 Bring a Business Model on the top of Java,
XML, JSON, COBOL, etc
 Add high level rule artifacts: Decision Table
& Trees
 Provide near natural language DSLs with
editors to write your rules in your preferred
locale: Chinese, English
 Integrate the rule engine into a server to
scale, and hot deploy ruleset in a 24/7
manner
 Trace decisions for auditability
 Cloud
 PaaS & SaaS
Rule
engine
Business
Model
Localized
Business
Languages
Decision
warehouse
Decision
Server
Testing &
Simulation
Business
Rules
Tools Cloud

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IBM BUSINESS RULES
Business rules as a service in IBM Bluemix

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Your Application
Externalize Decisions from Applications into Business Rules
Manage decision logic independently from applications
Your Application
Decision logic
 Natural language rules can be easily read
 Externalized rules are easy to change
 Centralized rules enable reuse and
consistency
 Rules written in software code cannot be
read easily
 Hard coded rules are difficult to change
 Rules intertwined within applications
cannot be reused by other systems
Business Rules

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IBM Business Rules, a Smarter Process high value service
Familiar Environment for Authoring
Developers can download an Eclipse
based authoring tool and author rules in a
familiar user-friendly environment.
Separate Business Logic
Business logic is authored separately from
the application which enables easier
change in business policy / logic and
codified capture of business policies,
practices and regulations..
Business logic is easily expressed with
business rules to automate decisions with
the fidelity of a subject matter expert.
Bridge Business Users and Developers
Deploy Versioned Business Logic
Multiple versions of the Business logic can
be tested and deployed in the same
Business Rules Service. Switching,
upgrading, sharing business logic across
applications has never been easier.
Enables developers to spend less time recoding and testing when the
business policy changes. The Business Rules service minimizes your
code changes by keeping business logic separate from application logic.
Business Rules

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The Business Rules service simplifies the experience of creating and
managing mobile app business logic – making apps more adaptable

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Developing and deploying applications using the Business Rules
service
IBM Bluemix
One
app
Another
app
Business Rules
service instance
Author business rules with Rule
Designer plug-ins for Eclipse
Deploy
business
rules
Develop and push app code
Call the
service
Users access
apps from their
devices
Non-Bluemix apps
can call the service
too
Call the service

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Wrap up
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 Rules
 From 70s IA to today enterprise decision management
 A large number of companies leverage some kinds of business rules (finance, banking)
 Empower developers and business users to automate decision making
 Provides transparency and explanation
 Dynamic deployment
 Rules are based on causality while Big Data & Machine Learning are based on correlation
 Perspectives to bridge rules & ML
 Try Business Rules in Bluemix https://console.ng.bluemix.net on London or Sydney
datacenters
Business Rules

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Q & A
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