Supervisory Control of Fuzzy Discrete Event Systems: A Formal Approach

Fuzzy {\it discrete event systems} (DESs) were proposed recently by Lin and Ying [19], which may better cope with the real-world problems with fuzziness, impreciseness, and subjectivity such as those in biomedicine. As a continuation of [19], in this paper we further develop fuzzy DESs by dealing with supervisory control of fuzzy DESs. More specifically, (i) we reformulate the parallel composition of crisp DESs, and then define the parallel composition of fuzzy DESs that is equivalent to that in [19]; {\it max-product} and {\it max-min} automata for modeling fuzzy DESs are considered; (ii) we deal with a number of fundamental problems regarding supervisory control of fuzzy DESs, particularly demonstrate controllability theorem and nonblocking controllability theorem of fuzzy DESs, and thus present the conditions for the existence of supervisors in fuzzy DESs; (iii) we analyze the complexity for presenting a uniform criterion to test the fuzzy controllability condition of fuzzy DESs modeled by max-product automata; in particular, we present in detail a general computing method for checking whether or not the fuzzy controllability condition holds, if max-min automata are used to model fuzzy DESs, and by means of this method we can search for all possible fuzzy states reachable from initial fuzzy state in max-min automata; also, we introduce the fuzzy $n$-controllability condition for some practical problems; (iv) a number of examples serving to illustrate the applications of the derived results and methods are described; some basic properties related to supervisory control of fuzzy DESs are investigated. To conclude, some related issues are raised for further consideration

L’INTELLECT INCARNÉ: Sur les interprétations computationnelles, évolutives et philosophiques de la connaissance

Modern cognitive science cannot be understood without recent developments in computer science, artificial intelligence (AI), robotics, neuroscience, biology, linguistics, and psychology. Classic analytic philosophy as well as traditional AI assumed that all kinds of knowledge must eplicitly be represented by formal or programming languages. This assumption is in contradiction to recent insights into the biology of evolution and developmental psychology of the human organism. Most of our knowledge is implicit and unconscious. It is not formally represented, but embodied knowledge which is learnt by doing and understood by bodily interacting with ecological niches and social environments. That is true not only for low-level skills, but even for high-level domains of categorization, language, and abstract thinking. Embodied cognitive science, AI, and robotics try to build the embodied mind in an artificial evolution. From a philosophical point of view, it is amazing that the new ideas of embodied mind and robotics have deep roots in 20th-century philosophy.Die moderne Kognitionswissenschaft kann nicht verstanden werden ohne Einbeziehung der neuesten Errungenschaften aus der Computerwissenschaft, künstlichen Intelligenz (AI), Robotik, Neurowissenschaft, Biologie, Linguistik und Psychologie. Die klassische analytische Philosophie, wie auch die traditionelle AI, setzten voraus, dass alle Arten des Wissens explizit durch formale oder Programmsprachen dargestellt werden müssen. Diese Annahme steht im Widerspruch zu den rezenten Einsichten in die Evolutionsbiologie und Entwicklungspsychologie des menschlichen Organismus. Der größte Teil unseres Wissens ist implizit und unbewusst. Es ist kein formal repräsentiertes, sondern ein verkörpertes Wissen, das durch Handeln gelernt und durch körperliche Interaktion mit ökologischen Nischen und gesellschaftlichen Umgebungen verstanden wird. Dies gilt nicht nur für niedere Fertigkeiten, sondern auch für höher gestellte Domänen: Kategorisierung, Sprache und abstraktes Denken. Die verkörperte Erkenntniswissenschaft, AI und Robotik versuchen, den verkörperten Geist in einer artifiziellen Evolution zu bilden. Vom philosophischen Standpunkt gesehen ist es erstaunlich, wie tief die neuen Ideen des verkörperten Geistes und der Robotik in der Philosophie des 20. Jahrhunderts verankert sind.La science cognitive moderne ne peut être comprise sans les progrès récents en informatique, intelligence artificielle, robotique, neuroscience, biologie, linguistique et psychologie. La philosophie analytique classique et l’intelligence artificielle traditionnelle présumaient que toutes les sortes de savoir devaient être représentées explicitement par des langages formels ou programmatiques. Cette thèse est en contradiction avec les découvertes récentes en biologie de l’évolution et en psychologie évolutive de l’organisme humain. La majeure partie de notre savoir est implicite et inconsciente. Elle n’est pas représentée formellement, mais constitue un savoir incarné, qui s’acquiert par l’action et se comprend en interaction corporelle avec nos niches écologiques et nos environnements sociaux. Cela n’est pas seulement vrai pour nos aptitudes élémentaires, mais aussi pour nos facultés supérieures de catégorisation, de langage et de pensée abstraite. Science cognitive incarnée, l’intelligence artificielle, ainsi que la robotique, tentent de construire un intellect incarné en évolution artificielle. Du point de vue philosophique, il est admirable de voir à quel point les nouvelles idées d’intellect incarné et de robotique sont ancrées dans la philosophie du XXe siècle

Reinforcement Learning

Brains rule the world, and brain-like computation is increasingly used in computers and electronic devices. Brain-like computation is about processing and interpreting data or directly putting forward and performing actions. Learning is a very important aspect. This book is on reinforcement learning which involves performing actions to achieve a goal. The first 11 chapters of this book describe and extend the scope of reinforcement learning. The remaining 11 chapters show that there is already wide usage in numerous fields. Reinforcement learning can tackle control tasks that are too complex for traditional, hand-designed, non-learning controllers. As learning computers can deal with technical complexities, the tasks of human operators remain to specify goals on increasingly higher levels. This book shows that reinforcement learning is a very dynamic area in terms of theory and applications and it shall stimulate and encourage new research in this field

Two Forms of Inconsistency in Quantum Foundations

Recently, there has been some discussion of how Dutch Book arguments might be used to demonstrate the rational incoherence of certain hidden variable models of quantum theory (Feintzeig and Fletcher 2017). In this paper, we argue that the 'form of inconsistency' underlying this alleged irrationality is deeply and comprehensively related to the more familiar 'inconsistency' phenomenon of contextuality. Our main result is that the hierarchy of contextuality due to Abramsky and Brandenburger (2011) corresponds to a hierarchy of additivity/convexity-violations which yields formal Dutch Books of different strengths. We then use this result to provide a partial assessment of whether these formal Dutch Books can be interpreted normatively.Comment: 26 pages, 5 figure

Detectability Of Fuzzy Discrete Event Systems

Dynamic systems that can be modeled in terms of discrete states and a synchronous events are known as discrete event systems (DES). A DES is defined in terms of states, events, transition dynamics, and initial state. Knowing the system’s state is crucial in many applications for certain actions (events) to be taken. A DES system is considered a fuzzy discrete event system (FDES) if its states and events are vague in nature; for such systems, the system can be in more than one state at the same time with different degrees of possibility (membership). In this research we introduce a fuzzy discrete event system with constraints (FDESwC) and investigate its detectabilities. This research aims to address the gap in previous studies and extend existing definitions of detectability of DES to include the detectability in systems with substantial vagueness such as FDES. These definitions are first reformulated to introduce N-detectability for DES, which are further extended to define four main types of detectabilities for FDES: strong N-detectability, (weak) N-detectability, strong periodic N-detectability, and (weak) periodic N-detectability. We first partition the FDES into trajectories of a length dictated by the depth of the event’s string (length of the event sequence); each trajectory consists of a number of nodes, which are further investigated for detectability by examining them against the newly introduced certainty criterion. Matrix computation algorithms and fuzzy logic operations are adopted to calculate the state estimates based on the current state and the occurring events. Vehicle dynamics control example is used to demonstrate the practical aspect of developed theorems in real-world applications