Qualitative Reasoning for Decision Synthesis

Chapter 9 Qualitative Modeling

This paper describes an Artificial Intelligence (AI) technique called Qualitative Reasoning (QR) for knowledge representation and simulation of organic reaction mechanisms. Even though there are many applications of AI technique in organic chemistry, none has involved QR approach in problem solving. The primary goal of QR research is to understand human-like commonsense reasoning. A software architecture has been designed which consists of two main components, the reasoning and the explanation modules. In qualitative modeling, chemical intuition and chemical commonsense that are required to understand the behavior of reaction mechanisms are represented using the modeling constructs of a QR ontology called Qualitative Process Theory (QPT). Several model fragments have been developed that served as embedded intelligence to the system. A simulation scenario based on these models is presented together with explanation generation for a particular aspect of the system behavior.

Hypothesis assessment with qualitative reasoning: Modelling the Fontestorbes fountain

A qualitative model for analyzing the effects of anthropogenic activities in the watershed on benthic macroinvertebrate communities

Many chemistry students have difficulty in understanding an organic chemistry subject called reaction mechanisms. Mastering the subject would require the application of chemical intuition and chemical commonsense adequately. This work discusses a novel framework using Qualitative Reasoning (QR) to provide means for learning reaction mechanisms through simulation. The framework consists of a number of functional components. These include substrate recognizer, qualitative model constructor, prediction engine, molecule update routine, explanation generator, and a knowledge base containing essential chemical facts and chemical theories. Chemical processes are represented as qualitative models using Qualitative Process Theory (QPT) ontology. The construction of these models is automated based on a set of QR algorithms. We have tested the framework on the SN1 and the SN2 reaction mechanisms. Representative cases of reaction simulation and causal explanation are also included to demonstrate how these models can serve as a cognitive tool fostering the acquisition of conceptual understanding via qualitative simulation.

The 28th International Workshop on Qualitative Reasoning (QR‐15) presented advances toward reasoning tractably with massive qualitative and quantitative models, automatically learning and reasoning about continuous processes, and representing knowledge about space, causation, and uncertainty.

A qualitative reasoning model of algal bloom in the Danube Delta Biosphere Reserve (DDBR)

Lehman's laws and the quantitative models based on them seek to encapsulate empirical generalizations about E‐type program evolution. Such modelling is hampered by insufficient knowledge about the mechanisms at work and their parameters. Qualitative reasoning is a body of work that handles a lack of precise knowledge by reasoning at a more abstract level than with quantitative models. This paper describes the introduction of qualitative reasoning to the study of software evolution. It reports on the derivation of qualitative versions of two existing quantitative models of the software evolution process, leading to identification of previously unrecognized behaviours. A third qualitative model is also discussed. The paper also shows how qualitative trend abstraction enables a high level of abstraction analysis of empirical data and that, at this level, the empirical patterns observed in several different software systems display similarities. Finally, we compare the qualitative simulation outputs of the three systems to the abstracted empirical trends. Copyright © 2003 John Wiley & Sons, Ltd.

In Group Decision Making based on argumentation, decisions are made considering the diverse points of view of the different partakers in order to decide which course of action a group should follow. However, knowledge and belief are normally incomplete, contradictory, or error sensitive, being desirable to use formal tools to deal with the problems that arise from the use of uncertain and even not precise information. On the other hand, qualitative models and qualitative reasoning have been around in Artificial Intelligence research for some time, in particular due the growing need to offer support in decision-making processes, a problem that in this work will be addressed in terms of an extension to the logic programming language and based on an evaluation of the Quality-of-Information (QoI) that stems out from those extended logic programs or theories. We present a computational model to address the problem of decision making, in terms of a multitude of scenarios, also defined as logic programs or theories, where the more appropriate ones stand for the higher QoIs values.

RedesignIT is a computer program that uses model-based reasoning to generate and evaluate proposals of redesign plans for engineered devices. These proposals describe how the design parameters could be changed to achieve a specified performance goal. Equally important, the program proposes complementary modifications that may be necessary to counteract the undesirable side effects of the primary changes. RedesignIT is intended for use during the first stages of a redesign project, when engineers need to make a quick, yet accurate assessment of the overall effects of a particular design change. The program uses qualitative device models, which allow it to compute redesign plans efficiently. With its ability to predict the collateral, and probably undesirable, effects of a design change, the program is well suited to aid product designers in deciding on the feasibility of introducing design changes to a product. RedesignIT employs methods of artificial intelligence, especially qualitative reasoning, causal reasoning, and heuristic search.

Understanding physical relations between objects, especially their support relations, is crucial for robotic manipulation. In this paper, we propose a method for extracting more detailed physical knowledge from a set of RGB-D images taken from the same scene but from different views using qualitative reasoning and intuitive physical models. Rather than providing a simple contact relation graph and approximating stability over convex shapes, our method is able to provide a detailed supporting relation analysis based on a volumetric representation. Specifically, true supporting relations between objects (e.g., if an object supports another object by touching it on the side or if the object above contributes to the stability of the object below) are identified. We apply our method to real-world structures captured in warehouse scenarios and show our method works as desired.

Major depressive disorder causes more human suffering than any other disease affecting humankind. It has a high prevalence and it is predicted that it will be among the three leading causes of disease burden by 2030. The prevalence of depression, all of its social and personal costs, and its recurrent characteristics, put heavy constraints on the ability of the public healthcare system to provide sufficient support for patients with depression. In this research, a model for continuous monitoring and tracking of depression in both short-term and long-term periods is presented. This model is based on a new qualitative reasoning approach. This paper describes the patient assessment unit of a major depression monitoring system that has three modules: a patient progress module, based on a qualitative reasoning model; an analysis module, based on expert knowledge and a rules-based system; and the communication module. These modules base their reasoning mainly on data of the patient's mood and life events that are obtained from the patient's responses to specific questionnaires (PHQ-9, M.I.N.I. and Brugha). The patient assessment unit provides synthetic and useful information for both patients and physicians, keeps them informed of the progress of patients, and alerts them in the case of necessity. A set of hypothetical patients has been defined based on clinically possible cases in order to perform a complete scenario evaluation. The results that have been verified by psychiatrists suggest the utility of the platform. The proposed major depression monitoring system takes advantage of current technologies and facilitates more frequent follow-up of the progress of patients during their home stay after being diagnosed with depression by a psychiatrist.

Qualitative system identification with the use of on-line genetic algorithms

Qualitative Knowledge in a Diagnostic Expert System for Nuclear Power Plant Safety

Among qualitative direction relation models, Oriented point relation algebra (OPRAm) is a remarkable model for robot navigation with uncertain direction information. It has great advantages in providing powerful expressions with very limited information compared with other point-based spatial relation models. The original OPRAm is defined in 2D space, and its model and reasoning algorithm are found not applicable in 3D space. We proposed a novel direction relation model named OPRA3Dm to extend the original OPRAm to 3D space, and presented a new reasoning algorithm on Oriented point relation algebra in three dimension (OPRA3Dm). A further study was carried out for composition reasoning on OPRA3Dm. The proposed reasoning algorithm will deduce new information which cannot be directly detected by hardware. The experiment showed the algorithm had some practical significance, it can be applied to the Unmanned aerial vehicle (UAV) navigation and similar scenarios.