systems that know and understand and think and learn
Here is a graphic on how various reasoning technologies fit the practical requirements for reasoning discussed below:
This proved surprisingly controversial during correspondence with colleagues from the Vulcan work on SILK and its evolution at http://www.coherentknowledge.com.
The requirements that motivated this were the following: Continue reading “Requirements for Logical Reasoning”
If you are using one of the more popular rules engines, chances are you can blame me. I popularized the technology of forward-chaining production rules based on the Rete Algorithm. Others have certainly contributed; my path is the one that led to open-source implementations and many commercial products, including those of IBM, Oracle, SAP, TIBCO, Red Hat, and too many others to mention (e.g., see this).
Today, I want to make clear that the future prospects for production rule technology are diminishing. My objective here is to explain why most rule-based technologies are no good and why some are much better. Although production rule technology is much better than most rule-based technologies, I hope to also make clear that in the age of IBM’s Watson, Google’s Brain, and the semantic web, production rule technology is inadequate.
Rules have become so pervasive in the software business that vendors of all types of software say they have them. Consider, for example, that even Microsoft Outlook has rules!
Continue reading “Confessions of a production rule vendor (part 1)”
Our efforts at acquiring deep knowledge from a college biology text have enabled us to answer a number of questions that are beyond what has been previously demonstrated.
For example, we’re answering questions like:
A couple of these are at higher levels on the Bloom scale of cognitive skills than Watson can reach (which is significantly higher than search engines).
As some other posts have shown in images, we can translate completely natural sentences into formal logic. We actually do the reasoning using Vulcan’s SILK, which has great capabilities, including defeasibility. We can also output to RIF or SBVR, but the temporal aspects and various things such as modality and the need for defeasibility favor SILK or Cyc for the best reasoning and QA performance.
One thing in particular is worth noting: this approach does better with causality and temporal logic than is typically considered by most controlled natural language systems, whether they are translating to a business rules engine or a logic formalism, such as first order or description logic. The approach promises better application development and knowledge management capabilities for more of the business process management and complex event processing markets.
I received notice of a Victorian government position offering $106k, as follows, today:
You will have proven experience as a BRMS Developer within a Java/JEE environment using IBM‘s WebSphere ILOG JRules platform. You will have implementation experience using integration technologies (e.g. Web Services, JMS) and have the ability to liaise with and engage key stakeholders. Ideally you will also have knowledge and/or exposure to IBM‘s WebSphere integration suite (including the MQ Series).
This got a reaction out of me since we’re looking for people (although emphasizing logic, semantics, and English rather than any particular engine). At first, I thought it must be a Java job, but stakeholder engagement indicates this is a full-fledged knowledge engineering position.
$100k for anyone with strong, specific experience seems low. For someone that can understand objectives and translate requirements into operational business logic, it seems lower.
I’m surprised there isn’t more of an Ilog premium, too. JBoss Drools consultants can make more than this.
FICO reported 9% growth in revenues year over year.
This is in sharp contrast to the success that Ilog has enjoyed under the IBM umbrella.
Blaze Advisor doesn’t seem to make sense as a stand-alone tool any more. Applications are great, and so are combinations of BI/optimization/rules, but if the BRMS tool will survive independently it needs to find more traction, perhaps outside of Fair Isaac.
Fair Isaac’s recent press release touts the “key differentiator” of Blaze Advisor 7.0 as:
the innovative Decision Graph visual metaphor, a decision tree management solution that makes even the most complex rule sets easier to manage and explain
Of course, a decision tree is really more like a root system (i.e., the tree is upside down). So, what this capability is particularly good for explaining how some pretty structured logic gets wherever it lands up, which FICO touts:
The new capability is especially valuable to businesses that need to be able to explain their decision logic to external auditors and regulators, or to internal parties such as senior management.
Unfortunately, this capability is only good for business logic that has been heavily analyzed and transformed from more natural forms of knowledge that stakeholders and regulators understand or communicate about.
FICO’s suggestion is that after operational guidelines and regulations have been laboriously translated (literally and, hopefully, appropriately) into if-then-else logic (i.e., decision trees) that viewing the path through the “code” will be informative to stakeholders and suitable for regulators. That seems like quite a stretch given the immediately following sentence, which indicates the complexity of using the metaphor in the first place:
Decision Graph gives business analysts a more intuitive way to view and navigate decision trees, which can reach 10,000 nodes or more.
Lots of people are attracted to such “visual programming” metaphors, but they are extremely limited in their logical expressiveness and, therefore, in their utility. Still, if you are using induction technology (such as FICO’s) to produce very large decision trees (independent of governing policies or regulations) such a tool can be useful, if only to understand what the machine “discovered” or to explain “how” it reached a decision, albeit post facto, which may or may not be compliant with governing doctrine.
The press release also talks about using this structure to experiment or optimize certain decisions by simulation, which is good stuff. FICO has long led in this area, especially in the markets it focuses on (i.e., B2C financial). This would have been a better central point, imo.
Overall, I agree with the following quote embedded within the release:
“Business rules management provides a shared platform for CIOs and business managers to help their enterprises stay competitive, and making business logic clearer to all parties is an essential part of that collaboration,” said Jim Sinur, a vice president at Gartner Research specializing in business rules management systems. “Better visualization of business logic can provide a huge uplift for companies that are looking for ways to improve business decisions.”
Showing thousands of nodes in a tree or cells in a table does not accomplish the appropriate goal (i.e., effective collaboration) of the first clause, however. And Jim did not say that decision trees provide effective visualization.
My take: the best approach is to guarantee that the statements of business policy and regulation are unambiguously understood by machine intelligence that automatically translates them into completely reliable systems.
That is, the best visualization for general purposes may be plain English.
If you are considering the use of any of the following business rules management systems (BRMS):
you can learn a lot by carefully examining this video on decisions using scoring in Ilog. (The video is also worth considering with respect to Corticon since it authors and renders conditions, actions, and if-then rules within a table format.)
This article is a detailed walk through that stands completely independently of the video (I recommend skipping the first 50 seconds and watching for 3 minutes or so). You will find detailed commentary and insights here, sometimes fairly critical but in places complimentary. JRules is a mature and successful product. (This is not to say to a CIO that it is an appropriate or low risk alternative, however. I would hold on that assessment pending an understanding of strategy.)
The video starts by creating a decision table using this dialog:
Note that the decision reached by the resulting table is labeled but not defined, nor is the information needed to consult the table specified. As it turns out, this table will take an action rather than make a decision. As we will see it will “set the score of result to a number”. As we will also see, it references an application. Given an application, it follows references to related concepts, such as borrowers (which it errantly considers synonomous with applicants), concerning which it further pursues employment information.
Continue reading “IBM Ilog JRules for business modeling and rule authoring”
The folks from Knowledge Partners have a post that I found thanks to Sandy Kemsley, whose blog often provides good pointers. This article talks about the decision perspective on business rules. It makes some good points, on which I would like to elaborate albeit at a more semantic or knowledge-level.
Every language has three kinds of statements: questions, statements, and commands. There are also some peripheral types, such as exclamations (Yikes!), but in business processes and decisions only declarative and imperative sentences matter.
From a process- or decision-oriented perspective, decisions are always phrased as imperative sentences. Otherwise, the statements reflected in any business process, whether you are using BPMN or a BRMS, are declarative sentences.
Decisions are imperative sentences because they state an action to be taken. For example, decline a loan or offer a discount. It’s really pretty simple. A decision is an action. Rules that don’t take actions are statements of truth. Such declarative statements of truth are perfect for formal logic, logic programming, and semantic technologies. It’s the action that requires the production rule technology that dominates the market for and applications of rules.
The authors of the aforementioned article use the following diagram to explain the benefits of the decision-oriented approach in simplifying business processes:
The impact is very simple. If you eliminate how you reach decisions from the flow that you diagram in BPMN things get simpler. It’s really as simple as realizing that you have removed all the “if” parts (i.e., the antecedents) of the rule logic from the flow chart.
So who in their right mind would use a business process tool to express any business logic? Continue reading “What could be more strategic than process or decision management?”
Today, I came upon some commentary by a business rule colleague, Carlos Serranos-Morales, of Fair Isaac concerning a presentation I made at the Business Rules Forum. During the presentation I showed some sentences that are beyond the current state of the art in the business rules industry. Generally speaking, these were logical statements that did not use the word “if”. (Note, however, that many of the them could be expressed in SBVR, OMG’s semantics of business vocabulary and rules standard). Carlos argued that such statements should be more precisely articulated within the specific context of a business process.
Here is the slide that triggered the controversy:
On the heels of IBM’s acquisition of Lombardi comes Progress Software’s acquisition of Savvion. The salient similarities are that IBM is adding BPM applications to its middleware stack as is Progress, at least with regard to its enterprise service bus offerings. More interesting is the relationship between Progress’ complex event processing software and Savvion’s BPM. Also of note is the vendor-provided integration of JBOSS Rules within Savvion versus the unrealized potential of IBM’s Ilog with respect to Lombardi.
We’ve written several times about the artificial distinction between CEP and BPM, their inevitable convergence, and the immature integration of business rules with business process management and event processing that inhibits knowledge-driven governance and decisioning. Continue reading “Rule and event-driven business process M&A”