Advanced Decision Support and Machine Intelligence by EEPM (Evolutive Elementary Pragmatic Model)
(duration: 3.5 hours)

Professor Rodolfo A. Fiorini
Department of Electronics, Information and Bioengineering
Politecnico di Milano University, Milano, Lombardia
ITALY
E-mail: rodolfo.fiorini@polimi.it
 

Abstract: Decision theory, based on "fixed universe" or a model of possible outcomes, ignores and minimizes the effect of events that are "outside model". Deep epistemic limitations reside in some parts of the areas covered in decision making. Unfortunately, the "probabilistic veil" can be very opaque computationally, and misplaced precision leads to confusion. Current human made application and system can be quite fragile to unexpected perturbation because statistics can fool you, unfortunately. The Elementary Pragmatic Model (EPM) is a high operative and didactic, versatile tool and new application areas are envisaged continuously. Recently, EPM contributed to find a solution to the double-bind problem in classic information and algorithmic theory. In turn, this new awareness has allowed to enlarge our panorama for neurocognitive system behaviour understanding, and to develop information conservation and regeneration systems in a numeric self-reflexive/reflective evolutive reference framework. Accordingly, new methods and models to build effective applications and strategies, from new forms of inter- and trans-disciplinarity, can be conceived conveniently. Rational human thinking is like a solid archipelago emerging out of an ocean of unaware intuitions. Human brain is an harmonization machine fed by unaware intuitions to produce learning and rational awareness about our environment. Our "Eulogic Thought" emerges out of a continuous harmonization interaction between Paleologic and Neologic Thought components. "Emotional Intelligence" (EI) and "Emotional Creativity" (EC) coexist at the same time with "Rational Thinking," sharing the same input environment information. Different forms of knowledge representations inducing a non- deductive procedure during inferences exist. Nevertheless, a logically closed model cannot cope with ontological uncertainty by itself; it needs a fundamental evolutive component: a complementary logical aperture operational support extension. To achieve this goal, it is possible to use two coupled irreducible information management subsystems, based on the following ideal coupled irreducible asymptotic dichotomy: "Information Reliable Predictability" and "Information Reliable Unpredictability". To behave realistically, overall system must guarantee both Logical Closure and Logical Aperture, both fed by environmental "noise" (better� from what human beings call "noise"). So, a natural operating point can emerge as a new Trans-disciplinary Reality Level, out of the Interaction of Two Complementary Irreducible Information Management Subsystems within their environment. In this way, it is possible to extend the traditional EPM approach to an Evolutive EPM (EEPM) framework, in order to profit by both classic EPM intrinsic Self-Reflexive Functional Logical Closure and new numeric CICT Self-Reflective Functional Logical Aperture. EPM can be thought as a reliable starting subsystem to initialize a process of continuous self-organizing and self- logic learning refinement. This tutorial presents the latest contribute to machine intelligence, advanced decision support models and simulation, offering examples of new forms of evolutive behaviour inter- and trans-disciplinarity modeling.

Short biography: Born in Ancona, Italy, Rodolfo A. Fiorini completed his high school education at Liceo Scientifico Luigi II di Savoia in 1969, Ancona, IT. In 1975, he graduated in Electronic Bioengineering at the Politecnico di Milano University, Milano, IT. In 1979, Dr. Fiorini was awarded for "Energetics Fellowship" from the Italian Ministry of Scientific Research and University and attended Bocconi University, School of Economics, and Politecnico di Milano University, specializing in Energetics. He obtained his Ph.D. degree in Energetics (100/100) from Politecnico di Milano University, in 1984. In the 80s, Dr. Fiorini was research associate to Stanford University (SU), Department of Aeronautics and Astronautics, Stanford, CA, U.S.A. and to University of California at Los Angeles (UCLA), Department of Computer Science, CA, USA. The U.S.A. DOL appointed him with the Ph.D. degree in Bioengineering in 1989. A senior member of the IEEE and the EMBC, Dr. Fiorini is currently a tenured Professor at the Department of Electronics, Information and Bioengineering (DEIB) at Politecnico di Milano University, Milano, IT, where he founded the Research Group on Computational Information Conservation Theory (CICT), and he is responsible for the main course on Wellbeing Technology Assessment. His current research interests include biomedical cybernetics, neuroscience, wellbeing, consciousness, nanoscience, computer simulation techniques, information theory, anticipatory learning systems, networked-control systems, machine learning, machine intelligence, statistical and deterministic signal processing, statistical and combinatorial optimization techniques, decision support systems, advanced management systems. Author of more than 230 national, international, scientific and technical, articles, papers, seminars, presentations and books. Since 1995 Dr. Fiorini has been a Co-Chair and Programme Committee Member for ISATA International Symposia (Virtual Reality and Supercomputing Applications; Simulation, Diagnosis and Virtual Reality Applications; Robotics, Motion and Machine Vision; Mechatronics, Efficient Computer Support for Engineering, Manufacturing, Testing & Reliability). Currently, Dr. Fiorini is associated editor of the Journal of Technology in Behavioral Science (JTiBS), sponsored by the Coalition for Technology in Behavioral Science (CTiBS) and an editorial panel member for EC Oceanography open access journal.