Adonis Diaries

Posts Tagged ‘knowledge representation

 “An exercise for taxonomy of methods”

Article #14 in Human Factors

I am going to let you have a hand at classifying methods by providing a list of various methods that could be used in Industrial engineering, Human Factors, Ergonomics, and Industrial Psychology.

This first list of methods is organized in the sequence used to analyzing part of a system or a mission;

The second list is not necessarily randomized though thrown in without much order; otherwise it will not be an excellent exercise.

First, let us agree that a method is a procedure or a set of step by step process that our for runners of geniuses and scholars have tested, found it good, agreed on it on consensus basis and offered it for you to use for the benefit of progress and science.

Many of you will still try hard to find short cuts to anything, including methods, for the petty argument that the best criterion to discriminating among clever people is who waste time on methods and who are nerds.

Actually, the main reason I don’t try to teach many new methods in this course is that students might smack run into a real occupational stress which they are not immune of, especially that methods in human factors are complex and time consuming.

Here is this famous list of a few methods and you are to decide which ones are still in the conceptual phases and which have been “operationalized“.

The first list contains the following methods: operational analysis, activity analysis, critical incidents, function flow, decision/action, action/information analyses, functional allocation, task, fault tree, failure modes and effects analyses, time line, link analyses, simulation, controlled experimentation  operational sequence analysis, and workload assessment.

The second list is constituted of methods that human factors are trained to utilize if need be such as: verbal protocol, neural network, utility theory, preference judgments, psycho-physical methods, operational research, prototyping, information theory, cost/benefit methods, various statistical modeling packages, and expert systems.

Just wait, let me resume.

There are those that are intrinsic to artificial intelligence methodology such as: fuzzy logic, robotics, discrimination nets, pattern matching, knowledge representation, frames, schemata, semantic network, relational databases, searching methods, zero-sum games theory, logical reasoning methods, probabilistic reasoning, learning methods, natural language understanding, image formation and acquisition, connectedness, cellular logic, problem solving techniques, means-end analysis, geometric reasoning system, algebraic reasoning system.

If your education is multidisciplinary you may catalog the above methods according to specialty disciplines such as: artificial intelligence, robotics, econometrics, marketing, human factors, industrial engineering, other engineering majors, psychology or mathematics.

The most logical grouping is along the purpose, input, process/procedure, and output/product of the method, otherwise it would be impossible to define and understand any method.

Methods could be used to analyze systems, provide heuristic data about human performance, make predictions, generate subjective data, discover the cause and effects of the main factors, or evaluate the human-machine performance of products or systems.

The inputs could be qualitative or quantitative such as declarative data, categorical, or numerical generated from structured observations, records, interviews, questionnaires, computer generated or outputs from prior methods.

The outputs could be point data, behavioral trends, graphical in nature, context specific, generic, or reduction in alternatives.

The process could be a creative graphical or pictorial model, logical hierarchy or in network alternative, operational, empirical, informal, or systematic.

You may also group these methods according to their mathematical branches such as algebraic, probabilistic, or geometric.

You may collect them as to their deterministic, statistical sampling methods and probabilistic characters.

You may differentiate the methods as belonging to categorical, ordinal, discrete or continuous measurements.

You may wish to investigate the methods as parametric, non parametric, distribution free population or normally distributed.

You may separate them on their representation forms such as verbal, graphical, pictorial, or in table.

You may discriminate them on heuristic, observational, or experimental scientific values.

You may bundle these methods on qualitative or quantitative values.

You may as well separate them on their historical values or modern techniques based on newer technologies.

You may select them as to their state of the art methods such as ancient methods that new information and new paradigms have refuted their validity or recently developed.

You may define the methods as those digitally or analytically amenable for solving problems.

You may choose to draw several lists of those methods that are economically sounds, esoteric, or just plainly fuzzy sounding.

You may opt to differentiate these methods on requiring high level of mathematical reasoning that are out of your capability and those that can be comprehended through persistent efforts.

You could as well sort them according to which ones fit nicely into the courses that you have already taken, but failed to recollect that they were indeed methods worth acquiring for your career.

You may use any of these taxonomies to answer an optional exam question with no guarantees that you might get a substantial grade.

It would be interesting to collect statistics on how often these methods are being used, by whom, for what rational and by which line of business and by which universities.

It would be interesting to translate these methods into Arabic, Chinese, Japanese, Hindu, or Russian.

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Article #50, (September 10, 2006)

Computational Rationality in Artificial Intelligence

The field of Artificial Intelligence would have been more on target if it was called “Computational Rationality”, which is fundamentally the approach of the fourth system:  Any scientific field, to be considered mature, a consensus by the professionals has to be reached; and the roots of the field of knowledge should be firmly grounded on mathematics and recognized traditional “rational logic”.

            In order to have a holistic view of the elements that are considered in AI, it would be interesting to picture a table of basic elements such as designed for the chemistry elements. Thus A for atom, Ab for alpha-beta search, Al for algorithm, Af for automatic theory formation, At for augmented transition net, B for Bayes’ rule, Bf for breadth first search, Bl for blocks-world labelling heuristics, Br for binary relations, Bt for backtracking search, C for concept formation, Cd for COND, Cf for closure of functions, Cg for context-free grammar,  Cl for cellular logic,  Cn for constraint, Cr for circular reaction paradigm, Cs for CONS, Df for differentiation, D for depth, Dc for Dempster-Shafer calculus, De for debugging, Df for DEFUN, Di for discovery, Dm for dialogue management, Dn for discrimination network,  Ds for depth first search, E for expertise, F for frame, Fc for forward chaining, Fm for formula manipulation, Fz for fuzzy logic, H for heuristic, Hm for the Hough transform, Ht for Herbrand’s theorem, I for ISA hierarchy, In for integration, Ie for inference engine, If for image formation and acquisition, Ih for inheritance, In for inference network, Ir for image representation, Kr for knowledge representation, L for lexicon, Lp for LISP, Lr for logical reasoning, Ls for LEIBNIZ structure, M for morphology, Mp for MAPCAR, Nm for numerical model, P for Prolog, Pa for pattern matching, Pb for probability, Pc for propositional calculus, Pd for predicate calculus,  Pf for preprocessing of low-pass filtering, Pg for pragmatics, Pl for planning, Pp for parallel processing, Ps for problem solving, Pr for production rule, Py for PYTHAGORUS, Q for quantization, R for robotics, Ra for Ramer’s algorithm, Rb for rule-based systems, Rc for recursive lists, Rd for relational database, Re for relaxation, Rn for resolution, S for semantics, Sc for schemata, Sf for sufficiency factor, Sg for segmentation, Sh for shell, Sn for semantic net, Sp for script, Sq for SETQ, SS for state space, St for stereo, Sx for S-expression, Sy for syntax, U for unification, T for topology, Tt for Turing’s test, V for version space, Z for zero-sum games.

It would be interesting to classify these AI elements into chapters such as: Programming in LISP, Productions and matching, Knowledge representation, Search methods, Logical reasoning, Probabilistic reasoning, Learning, Natural-language understanding, Vision, and Expert systems…

Is our intelligence that natural or acquired through customs, tradition, formal teaching, standards of moral value, community consensus…Even a person living in a forest, away from any mankind habitation and communication, he is learning from animal behaviors communicated from customs, tradition, teaching processes inculcated by the particular animal communities…

How mankind intelligence differ and developed? Mostly through trading with other human communities:  Exchanging expert tools of production, learning mechanisms, various customs and traditions…Variety of perspectives to looking at life, the universe and survival processes…

(To be developed further)




February 2023

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