Article: Modeling Jonathan Altfeld's Modeling Strategies
I've often been asked about my own model for Modeling, or a model for decision strategy elicitation (or knowledge acquisition). I've received glowing references with respect to my "Expert Systems" or "Artificial Intelligence" background before getting into NLP. These fields helped me to develop working, financially viable knowledge acquisition & cognitive modeling skills.
Throughout my exploration of NLP and other disciplines, if there were one area in which I've been repeatedly complimented, it would have to be learning strategies and skills modeling. I'm certain I'm not an ideal model or exemplar for some things, yet I have gratefully achieved some extremely high levels of skill and knowledge in certain domains, such as accelerated learning, and knowing intuitively the best thing to say in any given scenario (my NLP coaching clients have come to depend on this greatly!).
So obviously I pay attention to topics like this, because I believe I'm always using my own unique model for modeling, if that's what you'd call it. And I haven't yet found only one pattern (or set) that keeps repeating. I do believe its there, its just that since I started keeping much better track of WHAT I'M DOING as I'm doing it, I keep finding myself doing things differently depending on the specific circumstances.
So far, then, I'd say my effectiveness -- wherever I'm effective -- seems to be my behavioral flexibility. I break many of my own less-useful behavioral or communication patterns wherever I find them (unless they're patterns I *REALLY* enjoy... ;)
I do write my specific strategies up, as I find them, in order to identify my own models for modeling, and for potential publishing. Many of the NLP articles here are evidence of that!
I'll continue to publish some of my concrete modeling ideas as they emerge and arise.
Expert Systems (or Rule-Based Reasoning) is an area of artificial intelligence wherein people develop software to behave just like Human Experts do when presented with a new case requiring some process or diagnosis. This domain of work assumes that there is a static body of knowledge, or, a set of rigid rules (called a "knowledge base"), that are valid for... right now. I think this has had merit, given some limitations. And despite being static, a knowledge base can be quite sophisticated in terms of the varying order of how rules apply, the level of rules -- i.e. how specific do they get...
You can have rules which assume something and make any number of inferences from that point, such that if the original assumption becomes incorrect everything relative to that original assumption disappears...
You can have rules which depend on (1) the presence of specific information, on (2) the absence of information, and on (3) millions of combination's of both (1) & (2).
You can have rules which have priorities that change over the course of a certain process.
Using these ideas and MANY more, the opportunity for designing or building complex relationships of rules are endless.
Now, as a knowledge engineer (KE), I found that one can reach several limits as to what you can successfully build in any financially viable time frame without running into degradation on several fronts. One of the biggies is the fact that the model of rule-based-reasoning alone makes it difficult (not impossible) to design a Knowledge-Base whose 'architecture' or intrinsic flow structure will survive multiple major adjustments/ augmentations to the knowledge base itself!
You see, Rule-Based-Reasoning was not developed as a successful (i.e. viable) learning model, though some have developed systems that were reasonably good at developing certain kinds of new rules on-the-fly without a KE to intervene & write the code.
For that, the software industry turned to fuzzy logic, neural networks & genetic algorithms, to name some of the more popular approaches.
Nowadays the best work in modeling human intelligence -- with software -- is in the creative combination of these and other approaches (this is known as Hybrid Systems).
So. I DO still use KE techniques doing NLP Coaching and for eliciting knowledge, but more often than not, since many of those often do NOT account for the successful elicitation of KINESTHETIC information, I delve directly into the META model, and back out of it whenever my INTUITION tells me to pursue a certain path. This is part of what makes me such an effective NLP Coach. Meanwhile, modeling the nature of my Intuition... continues on an ongoing basis... ;)
- Jonathan Altfeld