In several papers we have discussed a computing model, called the interactive granular computing (IGrC), for interactive computations on complex granules. In this paper, we compare two models of computing, namely the ...
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In several papers we have discussed a computing model, called the interactive granular computing (IGrC), for interactive computations on complex granules. In this paper, we compare two models of computing, namely the Turing model and the IGrC model.
The theory of rough sets was founded by Zdzislaw Pawlak as a framework for data and knowledge exploration. His seminal paper titled "Rough Sets" was published in 1982, in International Journal of Computer an...
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ISBN:
(数字)9788396242396
ISBN:
(纸本)9788396242396
The theory of rough sets was founded by Zdzislaw Pawlak as a framework for data and knowledge exploration. His seminal paper titled "Rough Sets" was published in 1982, in International Journal of Computer and Information Sciences. One of the key aspects that lets us use rough sets in practical scenarios is the notion of information system, which comes from even earlier Professor Pawlak's works. Information systems are the means for data and knowledge representation. They constitute the input to rough set mechanisms aimed at computing approximations of concepts and deriving compacted, interpretable decision models. In particular, the fundamental notion of the indiscernibility relation is defined on the basis of a given information system. Accordingly, we discuss to what extent information systems can serve as the basis for intelligent systems. We claim that in many cases it is not enough to treat a data set - represented as an information system - as a purely abstract object with no linkage to the data origins. Oppositely, we should give ourselves a technical possibility to construct information systems dynamically, taking into account interaction with physical environments where the data comes from. With this respect, we refer to the notions of interactive granular computing and we generally consider together the paradigms of rough sets, information systems, and information granulation.
In this paper we have proposed a variation of Barwise and Seligman's proposal for information flow among a distributed network of agents by bringing in a notion of belief structure and a notion of graded inference...
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In this paper we have proposed a variation of Barwise and Seligman's proposal for information flow among a distributed network of agents by bringing in a notion of belief structure and a notion of graded inference. In their proposal, belief profile of an agent has played an important role as it is explicitly mentioned in the principles of information flow. In contrary, while developing the formal counterpart of information flow they did not address any connection with the belief profile of an agent. Besides, they have accommodated a non-deterministic notion of derivation to design the local logic of an agent keeping a room open for unsound inferences. Our proposal here is to bring in the notion of belief structure of an agent in the development of the local logic of an agent, and convert the non-deterministic nature of consequence to a deterministic graded (four-valued) notion of consequence. For the time being, we have focused on a specific context of decision making by aggregating opinions of agents based on an approach, known as the theory of graded consequence. (C) 2019 Published by Elsevier Inc.
Reaction system is a model of interactive computations which was motivated by the functioning of the living cell. It is an idealized mathematical model, also because it abstracts from the complex nature of the physica...
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Reaction system is a model of interactive computations which was motivated by the functioning of the living cell. It is an idealized mathematical model, also because it abstracts from the complex nature of the physical systems where only partial, incomplete information is available (e.g., about their states). The framework of rough sets was developed to deal with such incomplete information. In this paper we establish a connection between reaction systems and rough sets. This is done in a somewhat broader perspective of the relationship between "pure" mathematical models and "realistic models" that take into account the limitation of perceiving physical reality.
Reaction system is a model of interactive computations which was motivated by the functioning of the living cell. It is an idealized mathematical model, also because it abstracts from the complex nature of the physica...
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Reaction system is a model of interactive computations which was motivated by the functioning of the living cell. It is an idealized mathematical model, also because it abstracts from the complex nature of the physical systems where only partial, incomplete information is available (e.g., about their states). The framework of rough sets was developed to deal with such incomplete information. In this paper we establish a connection between reaction systems and rough sets. This is done in a somewhat broader perspective of the relationship between "pure" mathematical models and "realistic models" that take into account the limitation of perceiving physical reality.
In the context of complex granule computations within the interactivegranular Computating (IGC) paradigm we frame a cognitive task where user perceptions of the suitability of a good are in relation to the parameters...
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In the context of complex granule computations within the interactivegranular Computating (IGC) paradigm we frame a cognitive task where user perceptions of the suitability of a good are in relation to the parameters of the device producing it, all within a learning loop aimed at continuously improving those perceptions. We achieve this goal by extending the Fuzzy Inference System (FIS) paradigm to contexts where variables reckoning the user perceptions live in a non-metric space, hence neither users nor the learning algorithm have access to their true value. Namely, receiving in input a set of both crisp and fuzzy variables (respectively, from the hard_suit and the soft_suit of the c-granule to account for user and device logs), the inference system is asked to compute via the link_suit a set of crisp parameters satisfying some fuzzy evaluations stated by the user. A further complication is that the outputs are evaluated exactly in terms of the true unknown values held by the fuzzy attributes, which in turn must be inferred by the system. The whole work arose from everyday life problems faced by the European Project Social&Smart with the aim of optimally regulating household appliance runs. It represents a special instance of interactive Rough granularcomputing (IRGC) that we face with a two-phase procedure that is reminiscent of the distal learning in neurocontrol. A web service is available where the reader may check the efficiency of the assessed procedure.
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