Time for the next generation of knowledge automation

In preparing for my workshop at the Business Rules Forum in Las Vegas on November 5th, I have focused on the following needs in reasoning about processes, about events, and about or over time:

  1. Reasoning at a point within a [business] process
  2. Reasoning about events that occur over time.
  3. Reasoning about a [business] process (as in deciding what comes next)
  4. Reasoning about and across different states (as in planning)

Enterprise decision management (EDM) addresses the first.  Complex event processing (CEP) is concerned with the second.  In theory, EDM could address the third but it does not in practice.  This third item includes  the issue of governing and defining workflow or event-driven business processes rather than point decisions within such business processes. 

Business applications of rules have not advanced to include the fourth item.  That is to say, business has yet to significantly leverage reasoning or problem solving techniques that are common in artificial intelligence.  For example, artificially intelligent question and answer systems, which are being developed for  the semantic web,  can do more than retrieve data – they perform inference.  Commercial database and business intelligence queries are typically much less intelligent, which presents a number of opportunities that I don’t want to go into here but would happy to discuss with interested parties.  The point here is that business does not use reasoning much at all, let alone to search across the potential ramifications of alternative decisions or courses of action before making or taking one.  Think of playing chess or a soccer-playing robot planning how to advance the ball on goal.  Why shouldn’t business strategies or tactical business decisions benefit from a little simulated look-ahead along with a lot of inference and evaluation?

Even though I have recently become more interested in the fourth of these areas, I expect the audience at the business rules forum to be most interested in the first two points above.  There will also be some who have enough experience with complex business processes, which are common in larger enterprises.  These folks will be interested in the third item.  Only the most advanced applications, such as in biochemical process planning, will be interested in the fourth.  I don’t expect many of them to attend!

The notion of enterprise decision management (EDM) is focused on point decision making within a business process.  For enterprises that are concerned with governing business processes, a model of the process itself must be available to the business rules that govern its operation.  I’ve written elsewhere about the need for an ontology of events and processes in order to effectively integrate business process management (BPM) with business rules.  Here, and in the workshop, I intend to get a little more specific about the requirements, what is lacking in current standards and offerings, and what we’re trying to do about it. Continue reading “Time for the next generation of knowledge automation”

Goals and backward chaining using the Rete Algorithm

I was prompted to post this by request from Mark Proctor and Peter Lin and in response to recent comments on CEP and backward chaining on Paul Vincent’s blog (with an interesting perspective here).

I hope those interested in artificial intelligence enjoy the following paper .  I wrote it while Chief Scientist of Inference Corporation.  It was published in the International Joint Conference on Artificial Intelligence over twenty years ago. 

The bottom line remains:

  1. intelligence requires logical inference and, more specifically, deduction
  2. deduction is not practical without a means of subgoaling and backward chaining
  3. subgoaling using additional rules to assert goals or other explicit approaches is impractical
  4. backward chaining using a data-driven rules engine requires automatic generation of declarative goals

We implemented this in Inference Corporation’s Automated Reasoning Tool (ART) in 1984.  And we implemented it again at Haley a long time ago in a rules langauge we called “Eclipse” years before Java.

Regretably, to the best of my knowledge, ART is no longer available from Inference spin-off Brightware or its further spin-off, Mindbox.  To the best of my knowledge, no other business rules engine or Rete Algorithm automatically subgoals,  including CLIPS, JESS, TIBCO Business Events (see above), Fair Isaac’s Blaze Advisor, and Ilog Rules/JRules.  After reading the paper, you may understand that the resulting lack of robust logical reasoning capabilities is one of the reasons that business rules has not matured to a robust knowledge management capability, as discussed elsewhere in this blog.  Continue reading “Goals and backward chaining using the Rete Algorithm”

Behind the CEP curtain – it’s about time, not the cache

TIBCO is the CEP vendor most focused on the market for business rules, as reflected in Paul Vincent’s post here.  Although I agree with Paul that rule vendors are not currently offering enough in terms of support for long-running processes, the conclusions that he draws in favor of considering a CEP alternative to a BRMS are not compelling yet.

Paul said that rules don’t address the following that are addressed by CEP:

  1. BAM (business activity monitoring) and the other BPM (business performance management)
  2. Complex-rule processing
  3. Customer-centric (portfolio-based) decisions / policies

I am sure Paul was just being flippant, but you may notice that there is a bit of a war going on between CEP, BPM and rules right now.  Continue reading “Behind the CEP curtain – it’s about time, not the cache”

Agile Business Rules Management Requires Methodology

Don’t miss the great post about his and Ilog’s take on rule and decision management methodologies by James Taylor today (available here). 

Here’s the bottom line:

  1. Focus on what the system does or decides.
    • Focus on the actions taken during a business process and the decisions that govern them and the deductions that they rely on.
    • In priority order, focus on actions, then decisions, then deductions.
  2. Don’t expect to automate every nuance of an evolving business process on day one – iterate.
    • Iteratively elaborate and refine the conditions and exceptions under which
      • actions show be taken,
      • decisions are appropriate, and
      • deductions hold true

Another way of summing this up is:

Try not to use the word “then” in your rules!

Do check out the Ilog methodology as well as the one I developed for Haley that is available here.  The key thing Continue reading “Agile Business Rules Management Requires Methodology”

Elicitation and Management of Rules, Requirements and Decisions

A manager of an enterprise architecture group recently asked me how to train business analysts to elicit or harvest rules effectively. We talked for a bit about the similarities in skills between rules and requirements and agreed that analysts will fail to understand rules as they fail to understand requirements.

For example, just substitute rules in the historical distribution of requirements failures:[1]

  • 34% Incorrect requirements
  • 24% Inadequate requirements
  • 22% Ambiguous requirements
  • 9% Inconsistent requirements
  • 4% Poor scoping of requirements
  • 4% Transcription errors in requirements
  • 3% New or changing requirements

Continue reading “Elicitation and Management of Rules, Requirements and Decisions”

Managing Semantics, Vocabulary and Business Rules as Knowledge

A client recently asked me for guidance in establishing a center of excellence concerning business rules within their organization. Their objectives included:

  1. Accumulate requisite skills for productive success.
  2. Establish methodologies for productive, reliable and repeatable success.
  3. Accumulate and reuse content (e.g., definitions, requirements, regulations, and policies) across implementations, departments or divisions.
  4. Establish multiple tutorial and reusable reference implementations, including application development, tooling, and integration aspects.
  5. Establish centralized or transferable infrastructure, including architectural aspects, tools and repositories that reflect and support established methodologies, reusable content, and reference implementations.
  6. Establish criteria, best practices and rationale for various administrative matters, especially change management concerning the life cycles of content (e.g., regulations or policies) and applications (e.g., releases and patches).

I was quickly surprised to find myself struggling to write down recommendations for the skill set required to seed the core staff.  My recommendations were less technical than the client may have expected.   After further consideration, it became clear than any discrepancy in expectations arose from differences in our unvoiced strategic assumptions.  Objectives, such as those listed above, are no substitute for a clearly articulated mission and strategy.  

Continue reading “Managing Semantics, Vocabulary and Business Rules as Knowledge”