Modeling of Phenomena and Dynamic Logic of Phenomena (original) (raw)

Modeling phenomena and dynamic logic of phenomena

Modeling a complex phenomenon such as the mind presents tremendous computational complexity challenges. Modeling field theory (MFT) addresses these challenges in a non-traditional way. The main idea behind MFT is to match levels of uncertainty of the model (also, a problem or some theory) with levels of uncertainty of the evaluation criterion used to identify that model. When a model becomes more certain, then the evaluation criterion is adjusted dynamically to match that change to the model. This process is called the Dynamic Logic of Phenomena (DLP) for model construction and it mimics processes of the mind and natural evolution. This paper provides a formal description of DLP by specifying its syntax, semantics, and reasoning system. We also outline links between DLP and other logical approaches. Computational complexity issues that motivate this work are presented using an example of polynomial models.

Dynamic logic of phenomena and cognition

Proceedings of the International Joint Conference on Neural Networks, 2008

Modelling phenomena and dynamic logic of phenomena

Journal of Applied Non-Classical Logics, 2012

Modeling of complex phenomena such as the mind presents tremendous computational complexity challenges. Modeling field theory (MFT) addresses these challenges in a non-traditional way. The main idea behind MFT is to match levels of uncertainty of the model (also, problem or theory) with levels of uncertainty of the evaluation criterion used to identify that model. When a model becomes more certain, then the evaluation criterion is adjusted dynamically to match that change to the model. This process is called the Dynamic Logic of Phenomena (DLP) for model construction and it mimics processes of the mind and natural evolution. This paper provides a formal description of DLP by specifying its syntax, semantics, and reasoning system. We also outline links between DLP and other logical approaches. Computational complexity issues that motivate this work are presented using an example of polynomial models. There are two current trends within the modeling of physical phenomena and within logic. In modeling physical phenomena, the trend is to put in additional logical structures to classical mathematical techniques, whereas in logic it is to add "dynamics" to the logic [12-17], with an aim to represent and reason about actions rather than static propositions. The subjects in this area include Action Logic,

Modeling field theory and logic

2007 International Conference on Integration of Knowledge Intensive Multi-Agent Systems, KIMAS 2007, 2007

Modeling of complex phenomena such as the mind presents tremendous computational complexity challenges. The Neural Modeling (NMF) Fields theory and Phenomena Dynamic Logic (PDL) address these challenges in a non-traditional way. The main idea behind its success is matching the levels of uncertainty of the problem/model and the levels of uncertainty of the evaluation criterion used to identify the model. When a model becomes more certain then the evaluation criterion is also adjusted dynamically to match the adjusted model. This process mimics processes of the mind and natural evolution at the neural level. This paper describes the generalization of P-DL for data fusion and mining of heterogeneous spatial objects in cyberphysical space.

Probabilistic dynamic logic of phenomena and cognition

Proceedings of the International Joint Conference on Neural Networks, 2010

The purpose of this paper is to develop further the main concepts of Phenomena Dynamic Logic (P-DL) and Cognitive Dynamic Logic (C-DL), presented in the previous paper. The specific character of these logics is in matching vagueness or fuzziness of similarity measures to the uncertainty of models. These logics are based on the following fundamental notions: generality relation, uncertainty relation, simplicity relation, similarity maximization problem with empirical content and enhancement (learning) operator. We develop these notions in terms of logic and probability and developed a Probabilistic Dynamic Logic of Phenomena and Cognition (P-DL-PC) that relates to the scope of probabilistic models of brain. In our research the effectiveness of suggested formalization is demonstrated by approximation of the expert model of breast cancer diagnostic decisions. The P-DL-PC logic was previously successfully applied to solving many practical tasks and also for modelling of some cognitive processes.

Probabilistic dynamic logic of cognition

Biologically Inspired Cognitive Architectures, 2013

We developed an original approach to cognition, based on the previously developed theory of neural modeling fields and dynamic logic. This approach is based on the detailed analysis and solution of the problems of artificial intelligence -combinatorial complexity and logic and probability synthesis. In this paper we interpret the theory of neural modeling fields and dynamic logic in terms of logic and probability, and obtain a Probabilistic Dynamic Logic of Cognition (PDLC). We interpret the PDLC at the neural level. As application we considered the task of the expert decision-making model approximation for the breast cancer diagnosis. First we extracted this model from the expert, using original procedure, based on monotone Boolean functions. Then we applied PDLC for learning this model from data. Because of this model may be interpreted at the neural level, it may be considered as a result of the expert brain learning. In the last section we demonstrate, that the model extracted from the expert and the model obtained by the expert learning are in good correspondence. This demonstrate that PDLC may be considered as a model of learning cognitive process. A v ai l abl e a t w w w . s c i e n c e d i r e c t. c o m j o u r n a l h o m e p a g e : w w w . e l s e v i e r . c o m / l o c a t e / b i c a 160 E.E. Vityaev et al.

Formal Analysis of Dynamics within Philosophy of Mind by Computer Simulation

Minds and Machines, 2009

Computer simulations can be useful tools to support philosophers in validating their theories, especially when these theories concern phenomena showing nontrivial dynamics. Such theories are usually informal, whilst for computer simulation a formally described model is needed. In this paper, a methodology is proposed to gradually formalise philosophical theories in terms of logically formalised dynamic properties. One outcome of this process is an executable logic-based temporal specification, which within a dedicated software environment can be used as a simulation model to perform simulations. This specification provides a logical formalisation at the lowest aggregation level of the basic mechanisms underlying a process. In addition, dynamic properties at a higher aggregation level that may emerge from the mechanisms specified by the lower level properties, can be specified. Software tools are available to support specification, and to automatically check such higher level properties against the lower level properties and against generated simulation traces. As an illustration, three case studies are discussed showing successful applications of the approach to formalise and analyse, among others, Clark's theory on extended mind, Damasio's theory on core consciousness, and Dennett's perspective on intertemporal decision making and altruism.

Physics and Logical Openness in Cognitive Models

arXiv (Cornell University), 2007

It is here proposed an analysis of symbolic and sub-symbolic models for studying cognitive processes, centered on emergence and logical openness notions. The Theory of Logical Openness connects the Physics of system/environment relationships to the system informational structure. In this theory, cognitive models can be ordered according to a hierarchy of complexity depending on their logical openness degree, and their descriptive limits are correlated to Gödel-Turing Theorems on formal systems. The symbolic models with low logical openness describe cognition by means of semantics which fix the system/environment relationship (cognition in vitro), while the subsymbolic ones with high logical openness tends to seize its evolutive dynamics (cognition in vivo). An observer is defined as a system with high logical openness. In conclusion, the characteristic processes of intrinsic emergence typical of "bio-logic"-emerging of new codes-require an alternative model to Turing-computation, the natural or bio-morphic computation, whose essential features we are going here to outline.

Modeling of Phenomena and Dynamic Logic of Phenomena (original) (raw)

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