Going on 5 years ago, I wrote part 1. Now, finally, it’s time for the rest of the story.
A nationwide physical therapy business is renowned for eliminating chronic pain. One unique aspect of this business is that it eliminates pain not by manipulation but by providing clients with expertly selected sequences of exercises that address problems in their functional anatomy. In effect, the business helps people fix themselves and teaches them to maintain their musculoskeletal function for pain-free life.
The business has dozens of clinics with many more therapists. Its expertise comes primarily from its founder and a number of long-time employees who have learned through experience. We have been engaged to assist with several challenges on several occasions.
In the spring of 2012, Vulcan engaged Automata for a knowledge acquisition (KA) experiment. This article provides background on the context of that experiment and what the results portend for artificial intelligence applications, especially in the areas of education. Vulcan presented some of the award-winning work referenced here at an AI conference, including a demonstration of the electronic textbook discussed below. There is a video of that presentation here. The introductory remarks are interesting but not pertinent to this article.
Background on Vulcan’s Project Halo
From 2002 to 2004, Vulcan developed a Halo Pilot that could correctly answer between 30% and 50% of the questions on advanced placement (AP) tests in chemistry. The approaches relied on sophisticated approaches to formal knowledge representation and expert knowledge engineering. Of three teams, Cycorp fared the worst and SRI fared the best in this competition. SRI’s system performed at the level of scoring a 3 on the AP, which corresponds to earning course credit at many universities. The consensus view at that time was that achieving a score of 4 on the AP was feasible with limited additional effort. However, the cost per page for this level of performance was roughly $10,000, which needed to be reduced significantly before Vulcan’s objective of a Digital Aristotle could be considered viable.
In preparation for generating RIF and SBVR from the Linguist, we have produced an OWL ontology for the pertinent aspects of the SBVR specification. We hope that this is helpful to others and would sincerely appreciate any corrections or comments on how to improve it.
Our software is translating even long and complicated sentences from regulations to textbooks into formal logic (i.e,, not necessarily first-order logic, but more general predicate calculus). As you can see below, we can translate this understanding into various logical formalisms including defeasible first-order logic, which we are applying in Vulcan’s Project Halo. This includes classical first-order logic and related standards such as RIF or SBVR, as well as building or extending an ontology or description logic (e.g., OWL-DL).
We’re excited about these capabilities in various applications, such as in advancing science and education at Vulcan and formally understanding, analyzing and automating policy and regulations in enterprises.
Working as part of Vulcan’s Project Halo, Automata is applying a natural language understanding system that translates carefully formulated sentences into formal logic so as to answer questions that typically require deeper knowledge and inference than demonstrated by Watson.
The objective over the next three quarters is to acquire enough knowledge from the 9th edition of Campbell’s Biology textbook to demonstrate three things.
- First, that the resulting system answers, for example, biology advanced placement (AP) exam questions more competently than existing systems (e.g., Aura or Inquire).
- Second, that knowledge from certain parts of the textbook is effectively translated from English into formal knowledge with sufficient breadth and depth of coverage and semantics.
- Third, that the knowledge acquisition process proves efficacious and accessible to less than highly skilled knowledge engineers so as to accelerate knowledge acquisition beyond 2012.
Included in the second of these is a substantial ontology of background knowledge expected of students in order to comprehend the selected parts of the textbook using a combination of OWL, logic, and English sentences from sources other than the textbook.
Automata is hiring logicians, linguists, and biologists to work as consultants, contracts, or employees for:
- Interactive tree-banking and word-sense disambiguation of several thousand sentences.
- Extending its lexical ontology and a broad-coverage grammar of English with additional vocabulary and deeper semantics, especially concerning cellular biology and related scientific knowledge including chemistry, physics, and math.
- Maturing its upper and middle ontology of domain independent knowledge using OWL in combination with various other technologies, including description logic, first-order logic, high-order logic, modal logic, and defeasible logic.
- Enhancing its platform for text-driven knowledge engineering towards a collaborative wiki-like architecture for self-aware content in scientific education and biomedical applications.
Terms of engagement are flexible; ranging from small units of work to full-time employment. We are based in Pittsburgh, Pennsylvania and Vulcan is headquartered in Seattle, Washington, but the team is distributed across the country and overseas.
Please contact Paul Haley by e-mail to his first name at this domain.
 Vulcan: http://www.vulcan.com/TemplateCompany.aspx?contentId=54; Project Halo: http://www.projecthalo.com/
Video introduction/overview :http://videolectures.net/aaai2011_gunning_halobook/
 tree-banking and WSD: http://www.omg.org/spec/SBVR and http://en.wikipedia.org/wiki/Word-sense_disambiguation
 e.g., SILK (http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.174.1796 ) and SBVR (http://www.omg.org/spec/SBVR)
I received notice of a Victorian government position offering $106k, as follows, today:
BRMS Developer (WebSphere ILOG JRules)
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.
I was just asked for some background on business rules and the major players, preferably in the form of videos. The request came in by email, so I didn’t have the opportunity to immediately ask “why”. Below I give some specific and direct responses, but first a few thoughts about clarifying objectives.
I don’t know of any video that is particularly good from an executive overview standpoint concerning “business rules” or even “decision management” let alone “management of active knowledge”. My recommendation is to clarify the objective before drilling into “business rules”, which is a technical perspective. What is it that you are trying to accomplish? Most abstractly, it could be to manage and improve performance of an activity or an organization. That kind of answer or focus is the right place to start and then work backwards to the technical approach rather than start with an inadequately conceived technical need. This is one of the major problems with business rules as an independent market or product line.
Learning from Enterprise Decision Management
While at Fair Isaac, James Taylor saw this clearly. He articulated the enterprise decision management (EDM) and positioned the business rules capability Fair Isaac acquired with Blaze Software in that space. That is, more as a strategic objective than as a tool or technology. This is an example of the proper way to think about business rules.
The decision management perspective was also narrowly focused on point decision making (e.g., using rules) but James and others (e.g., John Lucker of Deloitte) have appropriately expanded the strategy of decision management to include analytics, which produce and inform decision making (i.e., business rules), into a continuous process not of point decision making, but more closed-loop, continuous process improvement. Over recent years, this has evolved into the broader market of performance management, which also includes performance optimization.
The key thing to consider when considering inquiries about “the applications and market for business rules” is the applications of knowledge. The “knowledge engineering” community is often too focused on the sources of knowledge. Focusing on sources rather than opportunities and benefits is a big part of why the business rules market has been subsumed into the business process management market, which is small in comparison to the business intelligence market, the fastest growing segment of which is performance management.
Semantic enterprise performance optimization checklist:
Here’s a checklist to consider when framing your considerations of strategies and tactics that might involve business rules technology:
- What knowledge (including policies, regulations, objectives, goals) is involved?
- What knowledge is superficial (i.e., derived from or approximations of) versus deeper knowledge?
- Will you capture the motivation for a decision rather than how that decision is made using rules?
- How will the performance of your decision management or governance system be evaluated?
- Is the knowledge involved in evaluating such performance part of the knowledge that you will capture and management?
- How does the manner of evaluation relate to goals and objectives and over what time frames?
- Is the knowledge about goals and objectives time frames part of the knowledge to be managed?
- Are your decisions rigidly governed in every aspect or do you need the business process to include experimentation and optimization?
Most business rules efforts are focused on contexts so narrow that they are reduced to technical buying criteria without much or any consideration of the above. That is, most business rule efforts do not even cover point 1 above. Few reach bullet 2 and only strategic thinkers get to the third.
Specific recommendations for the naive question:
So I went off looking for videos… You can find some on technical matters involving IBM/Ilog but I didn’t find any good videos from IBM at the business strategy level which concerned knowledge-based process/decision management/governance, which surprised.
A video from the vendors of Visual Rules touches on many of the traditional buying points that IT people typically formulate before evaluating vendors (here).
Although it did not respond to the inquiry, I sent along this video of James’ since it touches on so many of the aspects beyond business rules that are increasingly in vogue, even if it does not go far enough towards things like the business motivation model and the market for performance management, imo.
And for a very thorough background in the form of an analyst presentation that is consistent with all of the above, John Rymer of Forrester is most thorough in the two longer presentations that are here and there.
Please send me any other content that you would recommend!
If you are considering the use of any of the following business rules management systems (BRMS):
- IBM Ilog JRules
- Red Hat JBoss Rules
- Fair Isaac Blaze Advisor
- Oracle Policy Automation (i.e., Haley in Siebel, PeopleSoft, etc.)
- Oracle Business Rules (i.e., a derivative of JESS in Fusion)
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.
Ron Ross was kind enough to send me a copy of his recently publishd 3rd edition of his book, Business Rule Concepts. Ron has been at the forefront of mainstreaming business rule capture for decades. Personally, I am most fond of his leadership in establishing the Object Management Group’s Semantics of Business Vocabulary and Rules standard (OMG’s SBVR). This book is an indispensible backgrounder and introduction to the concepts necessary to effectively manage business rules using this standard.