IntelliFest Favorite Presentations: 2010-2012

Part of my path to knowledge engineering mastering has been to read everything available out there today about the modern knowledge engineering landscape. IntelliFest graciously posted all of their presentations from 2010-2012, and all of them are helpful. The following, though, were particularly helpful to me, and I wanted to share which ones and why, they follow:

• ~ 10-24-12
  
  • Paul Snow and Paul van den Berg: Using Niyum to Select Software Licensing Agreements
    • Good example of a problem getting solved, license conformance for MS for a big organization.
  • Lin Long-Ji: Building Efficient Predictive Models for Serving On-Line Advertisements
    • Good overview to ponder if you really want to do this kind of stuff.
  • Chandra Mouleeswaran: Distributed Rules – Revisited
    • A good paper mostly for the overview of parallel and distributed RE tech.
  • Gary Riley: Implementation Comparison of Three Expert Systems
    • Great bio looks like a real developer. Would have been great to have details.
• ~ 10-25-12
  
  • Jacob Feldman: Modeling and Solving Decision Optimization Problems

    • Constraint and Linear Programming problems. Schedule and resource allocation, complex configurations. supply chain, staffing, and vehicle routing. Constraint satisfaction problems, what: define entire solution space and the constraints on the variables and then optimize in the found solutions. Nice examples and tech. JSR-331. Staffing example.

  • Owen James: Rulebase Benchmarks – 25 Years On

    • Wild story about IBM buying Informix. Rules benchmarks: Miss Manners. Waltz. WaltzDB. Aeronautical Route Planner. VLSI Chip Designer Weaver. Great benchmarks over the years.

• ~ 10-26-12
  
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    • Geoffrey De Smet: Resource optimization with Drools Planner
      • Great constraint problem solving examples and tool drools planner. This a nice pres about planner.
• ~ Alan Moore: Deploying Knowledge Based Technologies In Embedded Systems
  

  • A CAD system that embeds CLIPS to help it determine whether or not it should allow user actions. Intel SmartTV uses IE SCripting with CLIPS. Lots of examples kind of neat.

• ~ Brian Jones: Using Rules to Build Languages
  
  • Great article about using a RE rather than lex/yacc or antlr.
• ~ Chandra Mouleeswaran: Integrating Classic Rule Engines with Modern Scalable Architectures
  

  • Seems like a guy really dialed into it all. Great points exactly all the things that I noticed from my work perspective.

• ~ Charles Young: Understanding Production Systems 101
  
  • Worthwhile overview of many things.
• ~ Edson Tirelli: BRMS: Best (and worst) Practices and Real World Examples
  
  • Nice article. Explains stuff, overviews. Many best practices, very nice.
• ~ George Williamson: Achieving Scalability In Rule Based Systems
  
  • Nice. Jess. Railway. Solution space vs. CPS?
• ~ George Williamson: Rules-Based Applications-Design Decisions
  

  • Think this guy  posted a lot about Jess.Union pacific railroad. Is a developer first, RE second. Nice sharing of his worldview and experience. Great real-world stuff on roles, responsibilities, user-facing stuff, and even how to test. He uses Fitnesse. He talks about using Fitnesse to do a ton of stuff. Great use case.

• ~ Jacob Feldman: Constraint Programming 101
  
  • Nice overview of constraint programming.
• ~ John Laird: The Role of Production Rules in a General Cognitive Architecture
  
  • Wonderful background of the history of rules engines.
• ~ Luke Voss: User-Mediated Rule Engine Execution
  
  • Intriguing idea is that REs sort of suck so let an expert tweak it at a deep level.
• ~ Michael Walsh: Domain Specific Language And Rules Engine Implemented In Python
  
  • A from scratch RE in Python with a DLS for the rules.
• ~  Paul Haley: Roadmap for Rules, Semantics, and Business
  
  • Fascinating background. Most of the envisioned approaches are realized today albeit differently.
• ~ Paul Vincent: Event Driven Rules Experiences In CEP
  

  • More CEP. Clients: FedEx, PJM, AllState. Important because it gives a taste of where REs fit as CEPs.

• ~ Peter Voss: Artificial General Intelligence and Modes of Learning
  

  • Virtual agents. Key question: what kind of intelligence are we interested in? Narrow AI is chess playing, facial recognition, and medical diagnosis. Big AI is learning and acquiring new skills.

• ~ Rolando Hernandez: Becoming a Knowledge Engineer
  
  • Humongo vision of how things should be. Helpful.
• ~ Rolando Hernandez: Building Mobiles Lube Knowledge Base and Knowledge Supply Chain
  

  • Nice demo of process and vision, historical tech usages. Basically capture all of Mobile’s working knowledge in an expert system. Hugely good examples and details.

• ~ Wolfgang Laun: Domain Specific Languages Notation For Experts
  
  • Nice example of a Drools DSL.