Imprecise Reasoning Research Articles

An integrated system of energy generation, storages, and appliances consumption based on machine learning techniques and internet of things

In the swiftly evolving micro-grid landscape, this paper explores the pivotal role of residential consumers and their active engagement. The primary focus is on revolutionizing customer management through the integration of smart homes, with an emphasis on facilitating demand response and bolstering operational efficiency. Also, the innovative Power Generation, Electricity Storages, and Energy Consumption in Smart Homes (PG–ES–ECSH) approach is introduced, meticulously designed for next-generation technologies, and delve into an smart home load control system. This system, employing Extreme Machine Learning (EML) and an imprecise reasoning framework, aims to enhance computational efficiency in managing diverse electrical energy sources and small-scale power generation appliances. The decision-making process of the framework incorporates real-time monitoring of environmental data, power generation, and battery storage. Our research significantly contributes to the evolution of smart homes and micro-grids by embracing technologies crucial for the future, such as machine learning and the Internet of Things (IoT). In this paper, the application of IoT and EML are used to enhance the connection between users in the microgrid and the learning rate of the proposed system, respectively. Emphasizing the burgeoning significance of energy management in smart homes, our study underscores the profound impact of energy resources on overall quality of life. With the increasing prevalence of smart homes, there is a growing demand for efficient energy utilization and management. Noteworthy is the PG–ES–ECSH method's impressive accuracy ratio of 96 % and a precision rate of 92 %. These findings highlight the imperative need for cost-effective energy management solutions, particularly in the face of escalating electricity consumption and the integration of novel power resources.

Decentralized robust evolving cloud-based controller for two input two output systems

The fuzzy logic controller (FLC) makes it possible to control a system using IF-THEN rules through human intellect. It tackles parameter uncertainty using imprecise reasoning. The fuzzy logic controller is usually tuned using offline methods. An online evolving adaptation of fuzzy controller design is a recent trend in fuzzy rule-based systems. The robust evolving cloud-based controller (RECCo) is one such controller implemented for single-input-single-output (SISO) systems. The membership functions and consequent rules are automatically updated in real time based on the input data. In this paper, a decentralized robust evolving cloud-based controller (DRECCo) is proposed for two-input-two-output (TITO) systems. It consists of two independent loops with RECCos having a nonparametric premise facet and an adaptive proportional-integral-derivative (PID) model consequent facet. The effectiveness of the proposed method is validated for the benchmark interacting two-tank process (ITTP) and quadruple-tank process (QTP) by simulation and in real time. The results indicate that with the information of loop pairing and the forward-acting/reverse-acting nature of the process, the proposed controller can adapt itself to ensure set-point tracking and disturbance rejection.

Imprecise Uncertain Reasoning: A Distributional Approach.

The contribution proposes to model imprecise and uncertain reasoning by a mental probability logic that is based on probability distributions. It shows how distributions are combined with logical operators and how distributions propagate in inference rules. It discusses a series of examples like the Linda task, the suppression task, Doherty's pseudodiagnosticity task, and some of the deductive reasoning tasks of Rips. It demonstrates how to update distributions by soft evidence and how to represent correlated risks. The probabilities inferred from different logical inference forms may be so similar that it will be impossible to distinguish them empirically in a psychological study. Second-order distributions allow to obtain the probability distribution of being coherent. The maximum probability of being coherent is a second-order criterion of rationality. Technically the contribution relies on beta distributions, copulas, vines, and stochastic simulation.

Binary Fields and Economics through Fuzzy Logic Approach and Boolean Algebra Using Multidimensional Processing with Respect to Artificial Neural Networks and Machine Learning

The fuzzy logic represents the relationship between precision and uncertainty. As the uncertainty in a theme is high, then less precise we can be in our conception. A binary logic admits only the opposites of true and false, a logic which does not admit digress of truth and there are no variations in magnitudes, but only two possible results. As more complex a system is, then more imprecise or inexact is the information that we have to the system. Aristotle mentioned that “It is the mark of an instructed mind to rest satisfied with that degree of precision which the nature of the subject admits, and not to seek exactness where only an approximation of the truth is possible”. So, Aristotelian logic does not admit imprecision in truth. However, Aristotle’s quote is so relevant with the approach that admits uncertainty. The theme is the balance between the precision with the uncertainty in a concept. The case of imprecision comes up from physical processes upon on imprecise human reasoning. Requiring precision in engineering models and economics means high cost and long lead times in production and development. So, considering the use of fuzzy logic then ponder the need for exploiting the tolerance for imprecision. According to the traditional view of science, uncertainty represents an undesirable situation, and must be excluded at any cost. Max Black referred to vagueness, where the possible states are not clearly. According to his essay in 1937 known as “Vagueness: An exercise in logical analysis” presented some remarks by Plato about Uncertainty in geometry. Bertand Russell in 1923 pointed out that “all traditional logic habitually assumes that precise symbols are being employed”. So, follow some proposals.

De Finetti and Savage on the normative relevance of imprecise reasoning: a reply to Arthmar and Brady

De Finetti and Savage on the normative relevance of imprecise reasoning: a reply to Arthmar and Brady

Why do we yawn? The importance of evidence for specific yawn-induced effects

Gallup (this issue) believes that our recent review on the function of yawning ( Guggisberg et al., 2010) is unbalanced and that it ignores evidence for his thermoregulation hypothesis. Here we address these criticisms and show them to be untenable. While we never claimed that the social hypothesis of yawning has “definite experimental support”, we emphasize the importance of experimental evidence for specific effects of yawns when considering why we yawn. The only specific effect of yawning that could be demonstrated so far is its contagiousness in humans, some non-human primates, and possibly dogs, whereas all studies investigating physiological consequences of yawns were unable to observe specific yawn-induced effects in the individual of any species. The argument that from an evolutionary perspective, yawns must have a “primitive” physiological function arises from imprecise reasoning.

Macroevolutionary patterns of pollination accuracy: a comparison of three genera

We hypothesize that pollination efficiency selects for equal distances between the pollinator reward and the anthers, and the stigmas, creating an adaptive ridge. We predict that this fitness surface governs the divergence of many plant species. We use the theory of adaptive accuracy, precision and mean optimality to assess how close populations lie to the hypothesized adaptive ridge and which factors contribute to departure from the optimum. Patterns of accuracy of pollen placement and receipt were compared across species in three study systems, Dalechampia (Euphorbiaceae), Collinsieae (Plantaginaceae) and Stylidium (Stylidiaceae), in order to assess the roles of stamen/stigma imprecision and population mean departure from the optimum in the generation of floral inaccuracy. We found that population mean departure from the optimum was the most important factor in Dalechampia, female imprecision and departure from the optimum were about equally important factors in Collinsieae, and stamen and stigma imprecision were equally important in Stylidium, with virtually no departure from the optimum. Possible reasons for imprecision and departure from the optimum were assessed using phylogenetically informed methods, indicating important roles of limited floral integration in the generation of imprecision, and conflicting selective pressures, associated with outcrossing, in the generation of departure from the optimum.

Situational computing: An innovative architecture with imprecise reasoning

Situation awareness is introduced as a more holistic variant of context awareness where situations are regarded as logically aggregated pieces of context. Situational computing can be viewed as the pervasive computing paradigm that deals with situational context representation and reasoning. One important problem that arises in such paradigm is the imperfect observations (e.g., sensor readings) that lead to the estimation of the current user situation. Hence, the knowledge upon which the context/situation aware paradigm is built is rather vague. To deal with this shortcoming, we propose the use of Fuzzy Logic theory with the purpose of determining (inferring) and reasoning about the current situation of the involved user. We elaborate on the architectural model that enables the system to assume actions autonomously according to previous user reactions and current situation. The captured, imperfect contextual information is matched against pre-developed situation ontologies in order to approximately infer the current user context. Finally, we present a series of experimental results that provide evidence on the flexible, efficient nature of the proposed situational computing.

Variety of judgements admitted in imprecise statistical reasoning

Intending the present paper as a contribution towards precautionary decision-making, the authors give a brief overview and summary of their experience in the modelling of different judgements admitted within the framework of the theory of coherent imprecise previsions and of possible use in reliability analysis. The paper specifically focuses on direct, comparative, structural and unreliable judgements.

Imprecise reasoning in geographic information systems

The concept of space underlying geographic information systems is basically Euclidean, requiring all subjects to adhere to the same view of space. This makes most attempts to deal with imprecise or uncertain geographic information difficult or sometimes even impossible. In this paper, we describe a way of incorporating imprecise qualitative spatial reasoning with quantitative reasoning in geographic information systems that is not restricted to Euclidean geometry. The ideal is to use fuzzy sets to model qualitative spatial relations among objects, like The downtown shopping mall is close to the harbor. The membership function of such a fuzzy set defines a fuzzy distance operator, for which a new algorithm is introduced in this paper.

An incongruous imprecise reasoning model and its abstract reasoning algorithms based on pansystems methodology

In this paper, the author puts forward an incongruous imprecise reasoning model in expert systems from the viewpoints of pansystems methodology and suggests some new algebra algorithms for propagating uncertainty factors in this model. It identifies the existing reasoning models and provides some new ideas about reasoning and computing.

Reasoning with imprecise knowledge to enhance intelligent decision support

Decisionmakers often have to deal with knowledge that is both unstructured and imprecise in nature. Lack of structure forces the use of heuristic programming and artificial intelligence methods in automated decision support systems for such problems. At the same time, the knowledge representation and manipulation methods used should be able to include imprecise knowledge, where imprecision may be caused by uncertainty and/or fuzziness. The authors use their previously developed methodology (see Decision Support Syst. vol.2, no.3, 1986 and vol.2, no.4, 1986) for using imprecise reasoning in decision support systems to describe a prototype system based on this methodology. The system's behavior is examined using a set of sample problems. The knowledge representation mechanisms used are shown to have significant expressive power and facilitate the knowledge of engineering process through greater flexibility in the representation and use of imprecise knowledge. The system is able to provide additional information to the user that cannot readily be provided in systems limited to precise reasoning. >

Accuracy of death certification for acute bacterial meningitis

The accuracy of death certification is reviewed for 94 fatal cases of bacterial meningitis in children. The findings are, in general, reassuring about the reliability of diagnostic certification in this age-group. Meningococcal infections were coded to a high degree of accuracy, although it is emphasized that meningococcal meningitis and septicaemia should be regarded as a single disease entity for vital statistical purposes. Other types of meningitis were also coded accurately at the diagnostic level of “meningitis”. They were, however, coded less accurately at the level of the specific microbiological diagnosis. Reasons for imprecision in certification are discussed.