Qualitative Constraints Research Articles

A Unified Framework for Knowledge Intensive Gradient Boosting: Leveraging Human Experts for Noisy Sparse Domains

Incorporating richer human inputs including qualitative constraints such as monotonic and synergistic influences has long been adapted inside AI. Inspired by this, we consider the problem of using such influence statements in the successful gradient-boosting framework. We develop a unified framework for both classification and regression settings that can both effectively and efficiently incorporate such constraints to accelerate learning to a better model. Our results in a large number of standard domains and two particularly novel real-world domains demonstrate the superiority of using domain knowledge rather than treating the human as a mere labeler.

Finding the Key Structure of Mechanical Parts with Formal Concept Analysis

Aiming at the problem that the assembly body model is difficult to classify and retrieve (large information redundancy and poor data consistency), an assembly body retrieval method oriented to key structures was presented. In this paper, a decision formal context is transformed from the 3D structure model. The 3D assembly structure model of parts is defined by the adjacency graph of function surface and qualitative geometric constraint graph. The assembly structure is coded by the linear symbol representation of compounds in chemical database. An importance or cohesion as the weight to a decision-making objective on the context is defined by a rough set method. A weighted concept lattice is introduced on it. An important formal concept means a key structure, since the concept represents the relations between parts’ function surfaces. It can greatly improve the query efficiency.

Компактное представление ограничений на основе новой интерпретации понятия «кортеж многоместного отношения»

The article discusses various points of view on the concept of "tuple of a multi-place relation" used in mathematics and information technology. Special attention is paid to an evolution of the concept “tuple” within Constraint Programming technology where the emergence of new interpretations for the concept “tuple” is related with attempts to design more “compact” table representation of qualitative relations comparing with typical relational tables. This “compact” representation can serve as a basis for accelerating qualitative constraint satisfaction procedures. In studied prototype-works, such varieties of table constraints as compressed-tables and smart-tables were proposed. In so doing, the concept of compressed- and smart- tuple substantially differs from traditional concept of tuple of a multi-place relation. However, the known table constraints types are not well suited for modeling and processing all types of quality relations, for example, there are inconveniences when modeling production rules. The article proposes a new type of table constraints – smart-tables of D-type and equivalent transformations rules allowing the initial constraint satisfaction problem to be effectively reduced. The application of the smart-tables of D-type allows in some cases to substantially reduce computer memory consumption comparing with application of the known types of table constraints. In particular, smart-tables of D-types are well suited for modeling production rules, some types of logical expirations and some types of global constraints.

LA FUNZIONE INTERPRETATIVA DEL PENSIERO ‘PARLANTE’ E L’INCONSCIO COGNITIVO

Macchi and Bagassi propose a conception of mind bounded by the qualitative constraint of relevance at conscious and unconscious levels. The core of this conception is an interpretative function in language and thought as adaptive characteristic of the human cognitive system. This perspective is supported by evidence from the authors' research on insight problem solving, which they consider a privileged route to understanding what kind of special unconscious thought produces the solution. During incubation, in the absence of conscious control, relevance constraint allows multilayered thinking to discover a new interpretation of the data that finally offers an exit from the impasse. The authors speculate that the creative act of restructuring implies a form of high-level unconscious thought, the unconscious analytic thought.

LA FUNZIONE INTERPRETATIVA DEL PENSIERO ‘PARLANTE’ E L’INCONSCIO COGNITIVO

Macchi and Bagassi propose a conception of mind bounded by the qualitative constraint of relevance at conscious and unconscious levels. The core of this conception is an interpretative function in language and thought as adaptive characteristic of the human cognitive system. This perspective is supported by evidence from the authors' research on insight problem solving, which they consider a privileged route to understanding what kind of special unconscious thought produces the solution. During incubation, in the absence of conscious control, relevance constraint allows multilayered thinking to discover a new interpretation of the data that finally offers an exit from the impasse. The authors speculate that the creative act of restructuring implies a form of high-level unconscious thought, the unconscious analytic thought.

Collective singleton-based consistency for qualitative constraint networks: Theory and practice

Partial singleton weak path-consistency, or partial ▪-consistency for short, is essential for tackling challenging fundamental reasoning problems associated with qualitative constraints networks. Briefly put, partial ▪-consistency ensures that each base relation of each of the constraints of a qualitative constraint network can define a singleton relation in its corresponding partially weakly path-consistent, or partially ⋄-consistent for short, subnetwork. In this paper, we propose a stronger local consistency that couples ▪-consistency with the idea of collectively deleting certain unfeasible base relations by exploiting singleton checks. We then propose an algorithm for enforcing this new consistency and a lazy variant of that algorithm for approximating the new consistency that, given a qualitative constraint network, both outperform the respective algorithm for enforcing partial ▪-consistency in that network. With respect to the lazy algorithmic variant in particular, we show that it runs up to 5 times faster than our original exhaustive algorithm whilst exhibiting very similar pruning capability. We formally prove certain properties of our new local consistency and our algorithms, and motivate their usefulness through demonstrative examples and a thorough experimental evaluation with random qualitative constraint networks of the Interval Algebra and the Region Connection Calculus from the phase transition region of two different generation models. Finally, we provide evidence of the crucial role of the new consistency in tackling the minimal labeling problem of a qualitative constraint network, which is the problem of finding the strongest implied constraints of that network.

Synthesis of the structure for the optimal system of flow treatment of raw materials

This paper demonstrates that contemporary studies into optimization of technological processes do not take into consideration in the models of systems and in the applied criteria the requirements to the overall efficiency of the process and compliance with the objectives of the owner of a privately-held industrial enterprise. This necessitates the reduction of cost and time of a technological operation, as well as maximization of the added value of the primary product. The effectiveness of the system of a flow treatment of raw materials is estimated using a specialized model, which was synthesized in the course of this work. The proposed model is different in that it includes units to calculate the unit cost of a product depending on the quality indicator and the degree of correspondence to the proposed quantitative and qualitative constraints. There are calculation units for the dynamics of change in a qualitative indicator of the finished product depending on a flow of raw materials and the energy supplied to treatment. The units are also required to calculate the consumption of resource and energy for the transporting and treatingg parts of the system in the interval, defined as the time taken for a conditional batch to pass through the installation. Using the developed model makes it possible to determine the value for the performance indicator for any permissible technological mode and to perform a global optimization of the process. Thus, there is a transition from the requirements to efficiency in general terms to setting the technological process parameters. Here we propose the analytical form for a performance indicator, suitable as an optimization criterion for modes of the technological installation with a continuous supply of raw-material and energy products. We have experimentally studied a model of the flow-through electric heater with units that calculate time and cost parameters, which has demonstrated its adequacy. The developed optimality criterion was verified and the possibility of its application was proven for determining the optimal permissible operating modes of the technological equipment with a continuous supply of raw materials and energy.

Impact of Implementation of Goods and Service Tax on Indian Banking Sector

The banking sector is one of the biggest and revenue generating sector in our economy. Indiais a country with impressively splendid banks with sufficient capital and well-regulated rulesand regulations. One of the biggest transformations that the sector faced during this period isGST i.e., Goods and Service Tax, a new tax regime introduced in the midnight of 1 July2017. Now the new tax regime has become one year old and there are so many changeswhich happened in the banking sector during this one-year periods. Introduction of GST tothe banking sector was one the highly risky and challenging role for the government. GST isa replacement to the Value Added Tax (VAT) which was implied on goods and services. Themain purpose of studying the impact of implementation of GST is to avoid double taxationon goods and services. It is a self-regulated tax system with a simplifies tax regime whichreduces the multiplicity of tax. The purpose of this study is to know the challenges faced bythe Banking sector and its effects on the customers after the implementation of the GST.New tax regime made an incredible step by the abolish of centralized registration of thebanks. Now all the bank branches have to register under GST in each state for the smoothfunctioning. The tax rate has created an impression in the banking sector that the sector iscontributing much toward the economic growth of the country. Tax slabs is anotherimportant and critical thing discussed in this paper which has substantially increasedcompared to the old tax regime. Data for the study have been collected from secondary datasources such as journals, internet, and news articles. Using the ABCD qualitative analysistechnique, advantages, benefits, constraints, and disadvantages for both banks and thecustomers for payment of GST are identified.

Constraining chemical transport PM2.5 modeling outputs using surface monitor measurements and satellite retrievals: application over the San Joaquin Valley.

Advances in satellite retrieval of aerosol type can improve the accuracy of near-surface air quality characterization by providing broad regional context and decreasing metric uncertainties and errors. The frequent, spatially extensive and radiometrically consistent instantaneous constraints can be especially useful in areas away from ground monitors and progressively downwind of emission sources. We present a physical approach to constraining regional-scale estimates of PM2.5, its major chemical component species estimates, and related uncertainty estimates of chemical transport model (CTM; e.g., the Community Multi-scale Air Quality Model) outputs. This approach uses ground-based monitors where available, combined with aerosol optical depth and qualitative constraints on aerosol size, shape, and light-absorption properties from the Multi-angle Imaging SpectroRadiometer (MISR) on the NASA Earth Observing System’s Terra satellite. The CTM complements these data by providing complete spatial and temporal coverage. Unlike widely used approaches that train statistical regression models, the technique developed here leverages CTM physical constraints such as the conservation of aerosol mass and meteorological consistency, independent of observations. The CTM also aids in identifying relationships between observed species concentrations and emission sources.Aerosol air mass types over populated regions of central California are characterized using satellite data acquired during the 2013 San Joaquin field deployment of the NASA Deriving Information on Surface Conditions from Column and Vertically Resolved Observations Relevant to Air Quality (DISCOVER-AQ) project. We investigate the optimal application of incorporating 275 m horizontal-resolution aerosol air-mass-type maps and total-column aerosol optical depth from the MISR Research Aerosol retrieval algorithm (RA) into regional-scale CTM output. The impact on surface PM2.5 fields progressively downwind of large single sources is evaluated using contemporaneous surface observations. Spatiotemporal R2 and RMSE values for the model, constrained by both satellite and surface monitor measurements based on 10-fold cross-validation, are 0.79 and 0.33 for PM2.5, 0.88 and 0.65 for NO3-, 0.78 and 0.23 for SO42-, and 1.01 for NH+, 0.73 and 0.23 for OC, and 0.31 and 0.65 for EC, respectively. Regional cross-validation temporal and spatiotemporal R2 results for the satellite-based PM2.5 improve by 30 % and 13 %, respectively, in comparison to unconstrained CTM simulations and provide finer spatial resolution. SO42- cross-validation values showed the largest spatial and spatiotemporal R2 improvement, with a 43 % increase. Assessing this physical technique in a well- instrumented region opens the possibility of applying it globally, especially over areas where surface air quality measurements are scarce or entirely absent.

Leveraging Variable Elimination for Efficiently Reasoning about Qualitative Constraints

We introduce, study, and evaluate a novel algorithm in the context of qualitative constraint-based spatial and temporal reasoning that is based on the idea of variable elimination, a simple and general exact inference approach in probabilistic graphical models. Given a qualitative constraint network [Formula: see text], our algorithm utilizes a particular directional local consistency, which we denote by [Formula: see text]-consistency, in order to efficiently decide the satisfiability of [Formula: see text]. Our discussion is restricted to distributive subclasses of relations, i.e., sets of relations closed under converse, intersection, and weak composition and for which weak composition distributes over non-empty intersections for all of their relations. We demonstrate that enforcing [Formula: see text]-consistency in a given qualitative constraint network defined over a distributive subclass of relations allows us to decide its satisfiability, and obtain similar useful results for the problems of minimal labelling and redundancy. Further, we present a generic method that allows extracting a scenario from a satisfiable network, i.e., an atomic satisfiable subnetwork of that network, in a very simple and effective manner. The experimentation that we have conducted with random and real-world qualitative constraint networks defined over a distributive subclass of relations of the Region Connection Calculus and the Interval Algebra, shows that our approach exhibits unparalleled performance against state-of-the-art approaches for checking the satisfiability of such constraint networks.

Low-carbon design of structural components by integrating material and structural optimization

Mechanical parts, especially large mechanical structures, consume a lot of resources and energy in manufacturing stage and produce large amounts of carbon emissions. To reduce the carbon emissions of mechanical parts from the perspective of manufacturing system, an approach integrating structural optimization and material selection is studied. A hybrid optimization model for low-carbon design of mechanical parts is established. The objective function of the model is built by quantifying the carbon emissions of mechanical parts. To improve the efficiency of carbon emissions analysis, a new concept “variable carbon emission” is proposed to identify part of carbon emissions, the amount of which will change with design variables. In this paper, variable carbon emissions of mechanical parts are quantified and parameterized by the relevant parameters of design variables. Two types of design variables (material variable and structural variable) are included in the model. The qualitative constraints and quantitative constraints for the low-carbon design optimization are both taken into consideration. Furthermore, an integrated numerical solving method is used to search for the optimal design scheme considering the coupling among material and structural parameters in low-carbon design. It foresees the synergic use of finite element model (FEM) and several numerical solution algorithms. The low-carbon design of a hydraulic slider is given as an example to demonstrate the approach. The results show that the approach has good potential to be applied in the low-carbon design of mechanical parts even with complex structural features.

Algorithm for Solving Two-Criteria Problem of Optimal Portfolio of Risky Assets

New mathematical problems statements are formulated and constructive algorithms for solving diversification problem of risky assets portfolio are developed. The optimal portfolio diversification problem is formulated based on the models of formation dynamics of the market value of one share and the portfolio of shares. Such statement enables us, by applying the allowable and effective sets, to solve the problem of optimal portfolio diversification constructively taking into account quantitative and qualitative constraints on the portfolio structure. Algorithms of this type are often applied for designing trading robots.

PQISEM: BN's structure learning based on partial qualitative influences and SEM algorithm from missing data

The structure learning of Bayesian Network (BN) is an important issue for probabilistic inference of BN. In this paper, for missing data, we have proposed a BN's structure learning algorithm by making full use of some partial qualitative influences and SEM algorithm. Specifically, firstly, we address the problem that how to modify BN's parameters based on partial qualitative influence knowledge, which makes these parameters meet the given qualitative constraint relationship. Then, based on qualitative influences, we give random search operators in hill climbing method, and then analyse the selection rule of the initial network and selection strategy of candidate networks. Further, the PQISEM algorithm is proposed based on partial qualitative influences and SEM algorithm. Its complexity and convergence are analysed. Finally, the experiment illustrates PQISEM's performance by comparing with other algorithms on standard networks and discussing on different sample sizes and different missing value proportion.

Constants and finite unary relations in qualitative constraint reasoning

Extending qualitative CSPs with the ability of restricting selected variables to finite sets of possible values has been proposed as an interesting research direction with important applications, cf. “Qualitative constraint satisfaction problems: an extended framework with landmarks” by Li, Liu, and Wang (2013) [48]. Previously presented complexity results for this kind of extended formalisms have typically focused on concrete examples and not on general principles. We propose three general methods. The first two methods are based on analysing the given CSP from a model-theoretical perspective, while the third method is based on directly analysing the growth of the representation of solutions. We exemplify the methods on temporal and spatial formalisms including Allen's algebra and RCC-5.

Ontology-Based Photogrammetry Survey for Medieval Archaeology: Toward a 3D Geographic Information System (GIS)

This paper presents certain reflections concerning an interdisciplinary project between medieval archaeologists from the University of Florence (Italy) and computer science researchers from CNRS, National Center for Scientific Research, (France), aiming towards a connection between 3D spatial representation and archaeological knowledge. We try to develop an integrated system for archaeological 3D survey and all other types of archaeological data and knowledge by incorporating observable (material) and non-graphic (interpretive) data. Survey plays a central role, since it is both a metric representation of the archaeological site and, to a wider extent, an interpretation of it (being also a common basis for communication between the two teams). More specifically, 3D survey is crucial, allowing archaeologists to connect actual spatial assets to the stratigraphic formation processes (i.e., to the archaeological time) and to translate spatial observations into historical interpretation of the site. It is well known that laser scanner, photogrammetry and computer vision are very useful tools for archaeologists, although the integration of the representation of space, as well as archaeological time has not yet found a methodological standard of reference. We propose a common formalism for describing photogrammetric survey and archaeological knowledge stemming from ontologies: indeed, ontologies are fully used to model and store 3D data and archaeological knowledge. We equip this formalism with a qualitative representation of time, starting from archaeological stratigraphy. Stratigraphic analyses (both of excavated deposits and of upstanding structures) are closely related to Edward Cecil Harris’s theory of the “Unit of Stratigraphication” (referred to as “US”, while a stratigraphic unit of an upstanding structure Unita Stratigrafica Murale, in Italian, will be referred to as “USM”). Every US is connected to the others by geometric, topological and, eventually, temporal links, and these are recorded by the 3D photogrammetric survey. However, the limitations of the Harris matrix approach led us to use another formalism for representing stratigraphic relationships, namely Qualitative Constraints Networks (QCN), which was successfully used in the domain of knowledge representation and reasoning in artificial intelligence for representing temporal relations.

Warship spare parts configuration optimization for stock control: Investigating the gap between qualitative and quantitative constraints

This paper investigates the gap between qualitative and quantitative constraints in spare parts stock control, with specific reference to warship spare parts support projects. A critical literature review of theoretical contributions about qualitative or quantitative factors for warship spare parts warehouse management is firstly provided, which allows to analyze the reasons for this qualitative-quantitative gap by addressing the limitations of spare parts models developed in the literature. Therefore a model including cloud model, marginal analysis and Lagrange multiplier method (CML) for study is proposed in this paper to bridge the gap. The model is used to solve the mix-constraints (both qualitative and quantitative constraints are considered) problem in a logic decision diagram particularly at the different decision nodes of the diagram. Finally, verifying test results show that the algorithm is feasible and its optimal support project meets the needs of engineering practices.

Dark-ages reionization and galaxy formation simulation – XII. Bubbles at dawn

Direct detection of regions of ionized hydrogen (HII) has been suggested as a promising probe of cosmic reionization. Observing the redshifted 21-cm signal of hydrogen from the epoch of reionization (EoR) is a key scientific driver behind new-generation, low-frequency radio interferometers. We investigate the feasibility of combining low-frequency observations with the Square Kilometre Array and near infra-red survey data of the Wide-Field Infrared Survey Telescope to detect cosmic reionization by imaging HII bubbles surrounding massive galaxies during the cosmic dawn. While individual bubbles will be too small to be detected, we find that by stacking redshifted 21-cm spectra centred on known galaxies, it will be possible to directly detect the EoR at $z \sim 9-12$, and to place qualitative constraints on the evolution of the spin temperature of the intergalactic medium (IGM) at $z \geq 9$. In particular, given a detection of ionized bubbles using this technique, it is possible to determine if the IGM surrounding them is typically in absorption or emission. Determining the globally-averaged neutral fraction of the IGM using this method will prove more difficult due to degeneracy with the average size of HII regions.

Government, enterprise and resident: Roles of local agents in regulating and simulating built-up land use and change in a mining city

Various agents are involved in decision-making when built-up land is allocated and regulated, but the roles of these agents must be clarified. To aid this process, the agents in Wu’an (a mining city in China) were classified as government, enterprise and resident, and then their roles were analyzed to establish a decision-making mechanism. The model of physical site selection for enterprises and residents was quantitatively simulated, taking into account the qualitative constraints from government and enterprises. Based on these rules, eight scenarios under economic development-driven scenario (EDdS) series and the environmental conservation-driven scenario (ECdS) series were set up, and vertical and horizontal comparisons were implemented for analysis. Our study showed that the established models could be well fitted for simulation, and that economic development could stimulate the allocation of more built-up land. The most important achievement is that the proposed comparison can effectively distinguish among the roles of various agents, as well as quantify government and enterprise incentives.

SURVEYING MEDIEVAL ARCHAEOLOGY: A NEW FORM FOR HARRIS PARADIGM LINKING PHOTOGRAMMETRY AND TEMPORAL RELATIONS

Abstract. The paper presents some reflexions concerning an interdisciplinary project between Medieval Archaeologists from the University of Florence (Italy) and ICT researchers from CNRS LSIS of Marseille (France), aiming towards a connection between 3D spatial representation and archaeological knowledge. It is well known that Laser Scanner, Photogrammetry and Computer Vision are very attractive tools for archaeologists, although the integration of representation of space and representation of archaeological time has not yet found a methodological standard of reference. We try to develop an integrated system for archaeological 3D survey and all other types of archaeological data and knowledge through integrating observable (material) and non-graphic (interpretive) data. Survey plays a central role, since it is both a metric representation of the archaeological site and, to a wider extent, an interpretation of it (being also a common basis for communication between the 2 teams). More specifically 3D survey is crucial, allowing archaeologists to connect actual spatial assets to the stratigraphic formation processes (i.e. to the archaeological time) and to translate spatial observations into historical interpretation of the site. We propose a common formalism for describing photogrammetrical survey and archaeological knowledge stemming from ontologies: Indeed, ontologies are fully used to model and store 3D data and archaeological knowledge. Xe equip this formalism with a qualitative representation of time. Stratigraphic analyses (both of excavated deposits and of upstanding structures) are closely related to E. C. Harris theory of “Stratigraphic Unit” (“US” from now on). Every US is connected to the others by geometric, topological and, eventually, temporal links, and are recorded by the 3D photogrammetric survey. However, the limitations of the Harris Matrix approach lead to use another representation formalism for stratigraphic relationships, namely Qualitative Constraints Networks (QCN) successfully used in the domain of knowledge representation and reasoning in artificial intelligence for representing temporal relations.

A Model-Theoretic View on Qualitative Constraint Reasoning

Qualitative reasoning formalisms are an active research topic in artificial intelligence. In this survey we present a model-theoretic perspective on qualitative constraint reasoning and explain some of the basic concepts and results in an accessible way. In particular, we discuss the significance of omega-categoricity for qualitative reasoning, of primitive positive interpretations for complexity analysis, and of Datalog as a unifying language for describing local consistency algorithms.