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Extending and integrating a hybrid knowledge representation system into the cognitive architecture ACT-R - 15th International Conference of the Italian Association for Artificial Intelligence
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Rules in Artificial Intelligence

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

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Rules in Artificial Intelligence

  1. 1. © 2015 IBM Corporation Rules in Artificial Intelligence Dec 2015 – Presentation at Ecole 42 Pierre Feillet – IBM Decision automation architect feillet@fr.ibm.com
  2. 2. © 2015 IBM Corporation Agenda 2  Origins  Expert System -> Inference Engine -> Rules  Current state  From raw inference engine to Entreprise decision automation  Business Rules in Bluemix
  3. 3. © 2015 IBM Corporation RULES TO MIMIC HUMAN MIND From Expert Systems to Operation Decision Management
  4. 4. © 2015 IBM Corporation Expert Systems 4  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.
  5. 5. © 2015 IBM Corporation Rule Logic 5  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.
  6. 6. © 2015 IBM Corporation Rule Logic 6  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
  7. 7. © 2015 IBM Corporation From Expert Systems to Operational Decision Management 7  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
  8. 8. © 2015 IBM Corporation IBM BUSINESS RULES Business rules as a service in IBM Bluemix
  9. 9. © 2015 IBM Corporation 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
  10. 10. © 2015 IBM Corporation 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
  11. 11. © 2015 IBM Corporation The Business Rules service simplifies the experience of creating and managing mobile app business logic – making apps more adaptable
  12. 12. © 2015 IBM Corporation 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
  13. 13. © 2015 IBM Corporation Wrap up 13  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
  14. 14. © 2015 IBM Corporation Q & A 14

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

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