http://popups.lib.uliege.be/1373-5411/index.php?id=1887 Index terms fr 0 http://popups.lib.uliege.be/1373-5411/index.php?id=4559 I simulated a large Hopfield neural net which had the signum instead of sigmoid activation function so that it could be naturally physically implemented, e.g. in spin systems. It has been used in computational simulations in order to analyze the following capabilities of processing very large and complex data sets (e.g., protein-structure data-bases): 1. completion of patterns; 2. recognition of patterns; 3. prediction of unknown parameters; 4. a.nticipation. While for tasks 1-3 we use a memory-ba,se of previouslylearned examples using "a,ssociations", ta.sk 4 is equivalent to case 3 re-interpreted for temporal (or timeseries) prediction, i.e. prediction of unknown future parameter-values (instead of unknown present ones). For tasks 3 and 4 it is concluded that a generalization of the model used in simulation, like phase-Hebb processing or quantum-like information dynamics, if more promising. Data-structure conditions for success of tasks l-4 are discussed in a complex "real-life" example. Mon, 14 Oct 2024 11:35:24 +0200 Mon, 14 Oct 2024 13:02:15 +0200 http://popups.lib.uliege.be/1373-5411/index.php?id=4559 http://popups.lib.uliege.be/1373-5411/index.php?id=1883 This article is a small survey and pioneering as a starting point for a longer research project : to utilize generalized semi-infinite optimization for purposes of prediction. Firstly, it reflects tbe analytical and inverse (intrinsic) behaviour of generalized semi-infinite optimization problems P(f,h,g,u,v) and presents interpretations of them from the viewpoint of anticipatory systems. These differentiable problems admit an infinite set Y(x) of inequality constraints y, which depends on the state x. Under suitable assumptions, we present global stability properties of the feasible set and corresponding structural stability properties of the entire optimization problem (Weber, 2002 ; Weber, 2003). The achieved results are a basis of algorithm design. In the course of explanation, the perturbational approach gives rise to reconstructions. By studying three applications of generalized semi-infinite optimization, secondly, we interpret these aspects of inverse problems in the sense of prediction. The three anticipatory systems are : (i) Reverse Chebycchev approximation, where we describe a given system by a neighbouring easier one as long as possible under some error tolerance. We begin by a motivating problem from chemical engineering and turn then to time-dependent systems. (ii) Time-minimal or -maximal optimization problems, where we want to pull or push the time-horizon of some process to present time or into the future. We mention global warming and turn to further kinds of biosystems. (iii) Computational biology, where we are concerned with prediction and stability of DNA microarray gene-expression patterns. Wed, 17 Jul 2024 12:56:01 +0200 Thu, 10 Oct 2024 16:49:39 +0200 http://popups.lib.uliege.be/1373-5411/index.php?id=1883 http://popups.lib.uliege.be/1373-5411/index.php?id=4379 An anticipatory reasoning-reacting system anticipates based on anticipatory reasoning, which can draw new, previously unknown and/or unrecognized conclusions about some future event or events whose occurrence and truth are uncertain at the point of time when the reasoning is being performed. To perform anticipatory reasoning, we need to express the real world, predictive laws and behavioural patterns of the target domain as empirical theories represented by logical formulas which called world model, predictive model, and behavioural model correspondingly. However, there is no case to show what these models are and how to construct these models. To this end, this paper proposes a general procedure to construct these models, and presents a case study of runway incursion prevention. Besides, this paper also discusses the evaluation of the models. Thu, 10 Oct 2024 10:08:32 +0200 Thu, 10 Oct 2024 10:08:52 +0200 http://popups.lib.uliege.be/1373-5411/index.php?id=4379 http://popups.lib.uliege.be/1373-5411/index.php?id=1093 Anticipation denotes a poised state of mind. (This is "Anticipation" in an Subjective sense.) Anticipation is also taken to denote the premeditation of ‘things yet to come’. (This is “Anticipation“ in an Objective sense.) The 2nd meaning, the foreknowing of the future, effecticly kills our realisation of creation: if we know what will happen, when, how and why. the experience of the future will become indistinguishable from our experience of the past/present. The experience of Life and creation will be totally lost. This is the kind of anticipation we will not want. The kind of Anticipation we will want. is the knowledge of moments to maximise our experience in of creation, by optimising our involvement. (The subjective mechanisms involved are separately described in a parallel paper; "Options & Choices, Doubts & Decisions) By understanding what (Subjective) Anticipation is not (objective predictability) the subjective realisation of Anticipation can be enhanced. This involves the principles of Total System Inversion, the properties of Boundary Transition, and the Criticality, Catastrophe, Collapse and Compressibility of a system. All of these reflect our own involvement; which is the basis for our understanding of Anticipation in the fullest sense. Fri, 05 Jul 2024 11:31:35 +0200 Tue, 08 Oct 2024 13:47:17 +0200 http://popups.lib.uliege.be/1373-5411/index.php?id=1093 http://popups.lib.uliege.be/1373-5411/index.php?id=1613 The real monetary economy is grounded upon monetary flow equilibration or the activity of actualizing monetary flow continuity at each economic agent except for the central bank. Every update of monetary flow continuity at each agent constantly causes monetary flow equilibration at the neighborhood agents. Every monetary flow equilibration as the activity of shooting the mark identified as monetary flow continuity turns out to be off the mark and constantly generate the similar activities in sequence. Monetary flow equilibration ceaselessly reverberating in the economy performs two functions. One is to seek an organization on its own, and the other is to perturb the ongoing organization. Monetary flow equilibration as the agency of seeking and perturbing its organizational so serves as a means of predicting its behavior. The likely organizational behavior could be the one that remains most robust against monetary flow equilibration as an agency of applying perturbations. Mon, 15 Jul 2024 15:53:33 +0200 Mon, 07 Oct 2024 12:43:59 +0200 http://popups.lib.uliege.be/1373-5411/index.php?id=1613 http://popups.lib.uliege.be/1373-5411/index.php?id=3768 Here, I explore the idea that consciousness is something that the brain learns to do rather than an intrinsic property of certain neural states and not others. Starting from the idea that neural activity is inherently unconscious, the question thus becomes: How does the brain learn to be conscious? I suggest that consciousness arises as a result of the brain's continuous attempts at predicting not only the consequences of its actions on the world and on other agents, but also the consequences of activity in one cerebral region on activity in other regions. By this account, the brain continuously and unconsciously learns to redescribe its own activity to itself, so developing systems of metarepresentations that characterize and qualify the target first-order representations. Mon, 30 Sep 2024 14:06:19 +0200 Mon, 30 Sep 2024 14:06:28 +0200 http://popups.lib.uliege.be/1373-5411/index.php?id=3768 http://popups.lib.uliege.be/1373-5411/index.php?id=3654 We first discuss the importance of making a controller interpretable and give an overview of the existing models and structures for that purpose. We then propose an approach to designing fuzzy controllers based on the B-spline model by learning. Unlike other normalised parametrised set functions for defining fuzzy sets, B-splines do not necessarily span membership values from zero to one but possess the property of "partition of unity". B-splines can be automatically determined after each input is partitioned. Learning of a fuzzy controller based on B-splines is then equivalent to the adaptation of a B-spline interpolator. Parameters of the controller output of each rule can be rapidly adapted by gradient descent. Optimal placements of the non-uniform B-splines for specifying each input can be found by Genetic Algorithms. Through comparative examples of function approximation we show that training of such a fuzzy controller generally provides results with minimal error. The approach can be extended to the problems of high-dimensional input by combining neural networks with a fuzzy control model. Thu, 26 Sep 2024 10:31:08 +0200 Thu, 26 Sep 2024 10:31:30 +0200 http://popups.lib.uliege.be/1373-5411/index.php?id=3654 http://popups.lib.uliege.be/1373-5411/index.php?id=2292 An anticipatory reasoning-reacting system (ARRS) has been proposed as a highly reliable and highly secure reactive system. The most important component of an ARRS is its anticipatory reasoning engine (ARE). We have proposed temporal relevant logic (TRL) as a sound logical basis of anticipatory reasoning, and shown that parallel processing techniques are effective to efficient anticipatory reasoning. This paper presents a real ARE we are developing based on TRLs. We define basic requirements of an ARE, discuss implementation issues for an ARE, present our implementation techniques, and show and discuss some current experimental results obtained by using our ARE. Our ARE can also be used in other computing anticipatory systems where anticipatory reasoning plays a key role. Wed, 31 Jul 2024 12:53:25 +0200 Wed, 31 Jul 2024 12:53:33 +0200 http://popups.lib.uliege.be/1373-5411/index.php?id=2292 http://popups.lib.uliege.be/1373-5411/index.php?id=2184 A primary application of regression analysis is prediction. In this paper, we consider the definition of the domain of the model in which prediction is valid. This is important because prediction made outside the domain may be unacceptably different from the true responses. We provide a criterion that can be used to decide whether prediction is valid at a certain point. The criterion is based on the existence of an unbiased estimate of the distribution function associated to the "future" observation. In addition, in the context of regression analysis, the categorical control problem that is quite different from the numerical control problem in the setting of the target is considered. Categorical control may be compared to interval prediction, whereas numerical control is compared to point prediction. Our derivation is based on the Scheffé-type simultaneous tolerance interval at two distinct points. Tue, 30 Jul 2024 12:43:28 +0200 Tue, 30 Jul 2024 12:43:35 +0200 http://popups.lib.uliege.be/1373-5411/index.php?id=2184