Qualitative Reasoning Approach Research Articles

An Equity-based Model of Vandalism in Information and Communication for Development Tele-Centre’s: Reflections from the Siyakhula Living Laboratory, Dwesa-Cwebe, South Africa

This study looks at tele-center related vandalism from an equity-based lens. It is noticed that these uncivil acts are common occurrences in information and communication for development (ICT4D) related tele-centers around the globe. Paradoxically, there is very little investigation on the issue. Here an equity-based model of vandalism proposed by Fisher & Baron is used to investigate incidences of vandalism and theft in a rural based ICT4D initiative known as the Siyakhula Living Lab (SLL). The model provides explanation value for the empirical investigation. The study employs a qualitative abductive reasoning approach to gain insight from stakeholders of the ICT4D initiative. For this study four stakeholders of the ICT4D initiative are interviewed, with the help of a semi-structured interview guide. Their responses are thematically analyzed and as a result a narrative emerges about the relationship between inequity and vandalism in the SLL context. The findings reveal that noticed occurrences of vandalism to publicly installed ICT infrastructure as well as a theft incident to an SLL established tele-center, were highly likely as a result of perceived inequity by certain groups in the surrounding communities. It is revealed that due to their existing low level of control at the time of the study, these groups felt compelled to resort to vandalism and theft in order to address the perceived inequity. It is concluded that inequity can indeed spur acts of vandalism especially for groups that perceive themselves as disenfranchised and powerless.

A multi-attribute group decision model based on unbalanced and multi-granular linguistic information: An application to assess entrepreneurial competencies in secondary schools

Advances in multi-attribute group decision making require the development of structures flexible enough to deal with unbalanced and multi-granular linguistic information. New distances between linguistic terms are needed to aggregate opinions and measure consensus among decision makers with different profiles. In this paper, firstly, based on the lattice structure of hesitant fuzzy linguistic terms sets, a perceptual-based distance able to capture differences between unbalanced linguistic assessments is developed. Secondly, a projected algebraic structure is defined to deal with multi-perceptual group decision-making contexts where each decision maker has its own qualitative reasoning approach. To this end, a methodology to aggregate unbalanced linguistic information based on different perceptual maps is developed. This methodology can also deal with different multi-granularity linguistic environments. Finally, through an illustrative example based on real data provided by the Andorra Government in a pilot test, the proposed framework is applied to classify and rank a set of secondary students according to their degree of entrepreneurial competency.

Hypothesis assessment with qualitative reasoning: Modelling the Fontestorbes fountain

This paper demonstrates the utility of the Qualitative Reasoning approach for hypothesis testing in the domain of ecology regarding the functioning of ‘black box’ systems. As a test case, we refer to the study performed by Mangin (1969) with scale models to investigate the hidden mechanism of the Fontestorbes fountain, a spring that exhibits a periodic flow situated in the south of France. In our approach, a Qualitative Reasoning method (and hence a qualitative model) is used to test the ‘siphon-hypothesis’, which traditionally explains the oscillations of the flow rate of a periodic spring by the principle of filling and emptying an underground reservoir through a siphon action. Parts of the simulation results show that the hypothesis is qualitatively accurate; in particular the model produces a cyclic behaviour that matches with the observed one. However, the qualitative model also exhibits a contradictory behaviour (true negative) that challenges the hypothesis consistency. The causal account of this true negative denotes and explains a flaw in the siphon-hypothesis. The paper concludes that, with the Qualitative Reasoning method, models can be constructed for hypothesis testing. Such models should generate the desired behaviour as a first and necessary step to support the viability of the hypothesis. However, the occurrence of unexpected behaviours provides information that challenges the hypothesis, and may lead to having to discard it.

Spatial recognition of activities for cognitive assistance: realistic scenarios using clinical data from Alzheimer’s patients

The evolution of consumer electronics, telecommunications and computing has empowered ambient intelligence into an emerging field of research bringing new possible solutions to many problems of human life. One of them is the technological assistance of elders who are suffering from a cognitive deficit with the execution of their everyday life activities inside what is called a smart home. To enable this technology, the first challenge to overcome is the recognition of the resident’s activities of daily living (ADLs). This problem consists of inferring the minimal set of possible ongoing ADLs using templates (plans) of activities defined in a library. To successfully achieve that goal, we must exploit constraints of different natures (logical, temporal, etc.) in order to reject a maximal number of hypotheses. However, only a minority of works exploited the elementary spatial aspects related to objects and to their relations in the smart environment. In this paper, we propose a novel recognition model exploiting the fundamental qualitative spatial reasoning approach of Egenhofer to discriminate implausible ongoing activities. Furthermore, the model is validated through extensive testing of realistic scenarios based on clinical trials conducted at our laboratory with both normal and impaired subjects.

A Logic of Spatial Qualification Using Qualitative Reasoning Approach

A Logic of Spatial Qualification Using Qualitative Reasoning Approach

How do different agricultural management strategies affect bird communities inhabiting a savanna-forest mosaic? A qualitative reasoning approach

Most of the world's biodiversity is threatened by agricultural intensification. Agricultural intensification also is considered as major human thread to one of the world's most threatened ecosystems, the Cerrado in Brazil. The Cerrado can be characterized as a natural mosaic of grasslands, woodlands, dry savannas and forests forming a naturally heterogeneous landscape representing the home for an enormous amount of endangered bird species. In this paper, we present a qualitative reasoning model to compare impacts of Intensive farming and traditional management on bird communities inhabiting a mosaic of savanna-forest landscape in the Brazilian Cerrado ecoregion, by using non-numerical knowledge and explicit representation of causal relations. Three groups of bird species were represented, based on their distinct use of the forest-savanna mosaic: forest specialists, forest generalists and non-forest inhabitants. The qualitative model was built in Garp3, a qualitative simulation engine. The qualitative model was successful in representing the impacts of both agricultural systems on the bird groups, addressing advantages and disadvantages of this method. The model shows that intensive agriculture leads to non-forest and forest specialists decline, whereas forest generalists are kept stable. On the other hand, traditional management may lead to either a decline or maintenance of the non-forest and maintenance of both forest groups. Replacement of traditional management by intensive agriculture may negatively affect birds. Heterogeneity is a key process to maintain diversity in agricultural landscapes.

Qualitative Reasoning for Biological Network Inference from Systematic Perturbation Experiments

The systematic perturbation of the components of a biological system has been proven among the most informative experimental setups for the identification of causal relations between the components. In this paper, we present Systematic Perturbation-Qualitative Reasoning (SPQR), a novel Qualitative Reasoning approach to automate the interpretation of the results of systematic perturbation experiments. Our method is based on a qualitative abstraction of the experimental data: for each perturbation experiment, measured values of the observed variables are modeled as lower, equal or higher than the measurements in the wild type condition, when no perturbation is applied. The algorithm exploits a set of IF-THEN rules to infer causal relations between the variables, analyzing the patterns of propagation of the perturbation signals through the biological network, and is specifically designed to minimize the rate of false positives among the inferred relations. Tested on both simulated and real perturbation data, SPQR indeed exhibits a significantly higher precision than the state of the art.

Evaluating the potential of qualitative reasoning models to contribute to sustainable catchment management

Due to the world wide degradation of river catchments and their related aquatic resources the development of integrated management strategies has become an important issue. Tools and processes are required that support the integration of science, the needs of stakeholders and the local population, within existing political frameworks to achieve a sustainable catchment development. In this paper the potential of qualitative reasoning (QR) models for sustainable catchment management is evaluated by students and domain experts. This evaluation yields promising results. The evaluated QR models were found to represent complex knowledge in an understandable manner. Most people ‘largely or fully agreed’ that the presented QR models may significantly contribute to the understanding of students and stakeholders, concerning which entities and processes drive a sustainable development of a riverine landscape, and therefore enhances their decision-making capabilities. Due to its potential to integrate quantitative and qualitative knowledge, to build causal models, and to run dynamic simulations, the presented QR approach has great potential to become an important contribution to integrated catchment management at multiple levels of the implementation process thereof (such as education, decision-making, social learning, integration of different scientific disciplines, and communication).

Towards a structured approach to building qualitative reasoning models and simulations

Successful transfer and uptake of qualitative reasoning technology for modelling and simulation in a variety of domains has been hampered by the lack of a structured methodology to support formalisation of ideas. We present a framework that structures and supports the capture of conceptual knowledge about system behaviour using a qualitative reasoning approach. This framework defines a protocol for representing content that supports the development of a conceptual understanding of systems and how they behave. The framework supports modellers in two ways. First, it structures and explicates the work involved in building models. Second, it facilitates easier comparison and evaluation of intermediate and final results of modelling efforts. We show how this framework has been used in developing qualitative reasoning models about three case studies of sustainable development in different river systems.

Decision making using time‐dependent knowledge: knowledge augmentation using qualitative reasoning

AbstractIn this paper we propose a method to enhance the performance of knowledge‐based decision‐support systems, knowledge of which is volatile and incomplete by nature in a dynamically changing situation, by providing meta‐knowledge augmented by the Qualitative Reasoning (QR) approach. The proposed system intends to overcome the potential problem of completeness of the knowledge base. Using the deep meta‐knowledge incorporated into the QR module, along with the knowledge we gain from applying inductive learning, we then identify the ongoing process and amplify the effects of each pending process to the attribute values. In doing so, we apply the QR models to enhance or reveal the patterns which are otherwise less obvious. The enhanced patterns can eventually be used to improve the classification of the data samples. The success factor hinges on the completeness of the QR process knowledge base. With enough processes taking place, the influences of each process will lead prediction in a direction that can reflect more of the current trend. The preliminary results are successful and shed light on the smooth introduction of Qualitative Reasoning to the business domain from the physical laboratory application. © 2001 John Wiley & Sons, Ltd.

A qualitative spatial reasoning approach in knowledge discovery in spatial databases

Programa de Desenvolvimento Educativo para Portugal (PRODEP) II - Accao 5.2 – Concurso no3/98 Doutoramentos.

A Fuzzy Qualitative Reasoning Approach for Real Time Multiple Fault Diagnosis

A computer assisted multiple faults detection and diagnosis, based on a fuzzy qualitative simulation algorithm, is presented. The system has a hierarchical structure consisting of a process simulator with control of some process variables in a lower layer and a supervisory module in an upper layer whose main goal is to detect and diagnose single and multiple simultaneous faults introduced in the lower layer. The adoption of fuzzy sets allows a more detailed description of physical variables, through an arbitrary, but finite, discretisation of the quantity space. Moreover, the fuzzy representation of qualitative values is more general than ordinary interval representation, since it can represent not only the information stated by a well defined real interval but also the knowledge embedded in the soft boundaries of the interval. The overall system has been implemented through a TURBO C++ program and successful applied in simulation studies of a continuous stirred tank reactor.

A semi-quantitative reasoning methodology for filtering and ranking HAZOP results in HAZOPExpert

Hazard and Operability (HAZOP) analysis is the most widely used and recognized as the preferred Process Hazards Analysis (PHA) approach in the chemical process industry. Recently, a diagraph-model based framework and an expert system called HAZOPExpert was developed for automating this analysis. Upon testing the performance of the system on various industrial case studies. HAZOPExpert was found to successfully mimic the human expert's reasoning and identify the hazards. But, with the increasing complexity of the processes, the HAZOPExpert system generated a large number of consequences compared to those identified by a team of experts. This is mainly due to the strict qualitative reasoning approach implemented in the HAZOPExpert system. In order to filter and rank the consequences generated by the HAZOPExpert system, a semi-quantitative reasoning methodology is proposed using additional quantitative knowledge in the form of design and operating specifications of the process units, and process material property values. This filtering approach combines the qualitative digraph-based HAZOP models and the quantitative knowledge to eliminate the unrealizable consequences. Significant reduction in the number of consequences was obtained using this approach on an ethylene process plant HAZOP case study.

Temporal multiresolution analysis for a quantitative/qualitative interpretation of complex dynamic processes

The paper describes a qualitative reasoning approach aimed at representing and interpreting a dynamic process evolution. The problem specifically addressed is the presence of multiple timescales in complex systems. Definitions of a temporal granularity as well as related concepts are provided. For the representation of a single process, a segmentation and abstraction method is described. The identification of dynamic features at any level of abstraction then supplies a help to better choose relevant sampling frequencies of the simulated process and helps in interpreting its outputs. An example is given for a complex crop growth model, whose interpretation is tested against expert knowledge.

Experience with an expert system for automated HAZOP analysis

Hazard and Operability (HAZOP) analysis is the most widely used and recognized as the preferred safety analysis approach in the chemical process industry. A model-based framework and an expert system called HAZOPExpert has been developed recently for automating this analysis. The performance of HAZOPExpert was evaluated on a sour water stripper plant and a hydrotreator plant case studies. The expert system was found to successfully emulate the human experts reasoning and identify the hazards similar to the HAZOP team. But, it generated a large number of unrealizable hazardous consequences compared to the team, due to the strict qualitative reasoning approach implemented. In order to filter and rank the results from HAZOPExpert using additional quantitative knowledge similar to the team, a semi-quantitative reasoning methodology is developed using the quantitative design and operating specifications of the process units, and process material property values. Significant reduction in the number of consequences was obtained using this approach on an ethylene plant case study.

An Application of Qualitative Reasoning to Derive Behavior from Structure of Quantitative Models

Mathematical modeling and analysis are a valuable tool in decision support contexts. Consequently, model management system(s) have become a key component of decision support system generators. Model management systems support modelers in various phases of the modeling life cycle including model representation, formulation, selection, integration, and execution. An important phase of the modeling life cycle involves analyzing and explaining the behavior of the formulated model. Such an analysis is necessary to understand the structure and behavior of the model, and to verify the appropriateness of the model to the problem. Traditional computer-based methods of analysis use the model solution as a basis to explain the model structure that caused the solution. This article presents an alternate approach that explains model behavior using the model structure and commonsense mathematical rules. The approach builds upon the qualitative reasoning methodology, developed in the artificial intelligence area to explain the behaviors of physical devices. A benefit of the qualitative reasoning approach is that it may describe the causes of modeling errors in terms of model structure. I also describe an implemented prototype model preprocessor that uses qualitative reasoning to provide qualitative explanations of the model behavior prior to solving the model. While I do not claim the sufficiency of the qualitative reasoning approach to detect and describe all types of modeling behavior, the article presents several examples demonstrating the utility of the approach.

Modeling the alcoholic fermentation of xylose by Pichia stipitis using a qualitative reasoning approach

Qualitative Reasoning is a set of Artificial Intelligence theories, methods, and techniques that provide an answer to modeling problems in domains in which one can have a clear notion of how a system is functioning without being able to express it as classical mathematical equations, and where is posed the problem of using jointly quantitative and qualitative data, as well as processing a big amount of complex knowledge. SIMAO (‘a System to Interpret Measurements And Observations’) is an attempt to deal with such problems. Although primarily devised for heterogeneous data interpretation in hydroecology, it was thought possible to use SIMAO in a wider context, like biotechnological processes. Starting from specific problems posed by a batch fermentation, the D-xylose conversion into ethanol by the yeast Pichia stipitis, this paper describes how was built and used a SIMAO model aimed at predicting the fermentation issue from initial conditions, i.e. set-points values and substrate concentration.

Modeling the alcoholic fermentation of xylose by Pichia stipitis using a qualitative reasoning approach

Modeling the alcoholic fermentation of xylose by Pichia stipitis using a qualitative reasoning approach

Qualitative reasoning about an ecological process: interpretation in hydroecology

Abstract Because of the frequent inefficiency of classical mathematical modelling to help the human operators in the supervision of biological processes, we present here a method based on qualitative reasoning concepts for simulating the interpretation of measurements, analyses, and observations, commonly done on aquatic ecosystems for management purposes. Once the domain variables are identified, their cause-effect dependences are represented as a directed graph. Each variable takes its value in a five-symbol set called quantity space (QS). These symbols … pp, p, m, f, ff…, correspond to expert qualifiers, like respectively: very low, low, medium, high, very high. A synthetic formalism is proposed to encode four types of knowledge rules: (1) translation of the numerical input values (i.e. measurements and results of analyses) into qualitative values; (2) translation of the linguistic observations into qualitative values; (3) formal calculus on QS, using six empirically defined operators, for propagating a top-down form of reasoning throughout the causal network, enabling the determination of the qualitative values of unmeasured variables from the values of their causes; (4) control of the execution of the reasoning. The software prototype, implemented in Prolog, has four main functions: short-term prediction of management parameters, causal explanation of the reasoning, state memorization, and choice of control variables in the causal network. These capabilities are illustrated by examples from an application enabling the interpretation of data in hydroecology. The relevance of a qualitative reasoning approach is emphasized, particularly for making empirical knowledge, typical of biological process control, operational.

Reasoning composite beliefs using a qualitative approach

A Bayesian network is a knowledge representation technique for use in expert system development. The probabilistic knowledge encoded in a Bayesian network is a set of composite hypotheses expressed over the permutation of a set of variables (propositions). Ordering these composite hypotheses according to their a posteriori probabilities can be exponentially hard. This paper presents a qualitative reasoning approach which takes advantage of certain types of topological structures and probability distributions of a Bayesian network to derive the partial ordering of composite hypotheses. Such an approach offers an attractive alternative to reduce the computational complexity of deriving a partial ordering in which consistency is guaranteed.