The Experts below are selected from a list of 321 Experts worldwide ranked by ideXlab platform
Anthony G Cohn - One of the best experts on this subject based on the ideXlab platform.
-
Qualitative spatial representation and Reasoning
Foundations of Artificial Intelligence, 2008Co-Authors: Anthony G Cohn, Jochen RenzAbstract:Publisher Summary Early attempts at Qualitative spatial Reasoning within the Qualitative Reasoning (QR) community led to the poverty conjecture. The need for spatial representations and spatial Reasoning is ubiquitous in artificial intelligence (AI) from robot planning and navigation to interpreting visual inputs to understanding natural language. In all these cases, the need to represent and reason about spatial aspects of the world is of key importance. Related fields of research such as geographic information science (GIScience) have also driven the spatial representation and Reasoning community to produce efficient, expressive, and useful calculi. There has been considerable research in spatial representations that are based on metric measurements, in particular within the vision and robotics communities, and also on raster and vector representations in GIScience. This chapter focuses on symbolic and, in particular, Qualitative representations. The challenge of Qualitative spatial Reasoning (QSR) is to provide calculi that allow a machine to represent and reason with spatial entities without resort to the traditional quantitative techniques prevalent in, for example, computer graphics or computer vision communities.
-
representing moving objects in computer based expert systems the overtake event example
Expert Systems With Applications, 2005Co-Authors: Nico Van De Weghe, Anthony G Cohn, Philippe De Maeyer, Frank WitloxAbstract:Qualitative formalisms suited to express Qualitative temporal or spatial relationships between entities, have gained wide acceptance as a useful way of abstracting from the real world. The question remains how to describe spatio-temporal concepts, such as the interaction between moving objects, adequately within a Qualitative calculus and more specifically how to use this in expert systems. With this in mind, the Qualitative Trajectory Calculus (QTC) has been introduced. QTC enables comparisons between positions of objects at different time points to be made. By reducing the continuum to the Qualitative values -, 0 and +, continuous movements can be described Qualitatively. To illustrate the naturalness of QTC, the overtake event is studied. An overtake event is a typical example of objects moving in a particular domain and can become important, for example in the study of traffic engineering. A so-called conceptual animation is represented, being a sequence of QTC-relations, following the constraints imposed by Qualitative Reasoning. It is shown that different kinds of behaviour having certain common characteristics are reflected by the structure (e.g. symmetrical aspects) of the conceptual animations.
-
Qualitative spatial representation and Reasoning techniques
Lecture Notes in Computer Science, 1997Co-Authors: Anthony G CohnAbstract:The field of Qualitative Spatial Reasoning is now an active research area in its own right within AI (and also in Geographical Information Systems) having grown out of earlier work in philosophical logic and more general Qualitative Reasoning in AI. In this paper (which is an updated version of [25]) I will survey the state of the art in Qualitative Spatial Reasoning, covering representation and Reasoning issues as well as pointing to some application areas.
-
representing and Reasoning with Qualitative spatial relations about regions
1997Co-Authors: Anthony G Cohn, Brandon Bennett, J M Gooday, Nicholas Mark GottsAbstract:Qualitative Reasoning (QR) has now become a mature subfield of AI as its tenth annual international workshop, several books (e.g. (Weld and de Kleer, 1990; Faltings and Struss, 1992)) and a wealth of conference and journal publications testify. QR tries to make explicit our everyday commonsense knowledge about the physical world and also the underlying abstractions used by scientists and engineers when they create models. Given this kind of knowledge and appropriate Reasoning methods, a computer could make predictions and diagnoses and explain the behavior of physical systems in a Qualitative manner, even when a precise quantitative description is not available or is computationally intractable. Note that a representation is not normally deemed to be Qualitative by the QR community simply because it is symbolic and utilizes discrete quantity spaces but because the distinctions made in these discretizations are relevant to high-level descriptions of the system or behavior being modeled.
-
calculi for Qualitative spatial Reasoning
International Conference on Artificial Intelligence, 1996Co-Authors: Anthony G CohnAbstract:Although Qualitative Reasoning has been a lively subfield of AI for many years now, it is only comparatively recently that substantial work has been done on Qualitative spatial Reasoning; this paper lays out a guide to the issues involved and surveys what has been achieved. The papers is generally informal and discursive, providing pointers to the literature where full technical details may be found.
Bert Bredeweg - One of the best experts on this subject based on the ideXlab platform.
-
semantic techniques for enabling knowledge reuse in conceptual modelling
International Semantic Web Conference, 2010Co-Authors: Jorge Gracia, J Liem, Esther Lozano, Oscar Corcho, Michal Trna, Asuncion Gomezperez, Bert BredewegAbstract:Conceptual modelling tools allow users to construct formal representations of their conceptualisations. These models are typically developed in isolation, unrelated to other user models, thus losing the opportunity of incorporating knowledge from other existing models or ontologies that might enrich the modelling process. We propose to apply Semantic Web techniques to the context of conceptual modelling (more particularly to the domain of Qualitative Reasoning), to smoothly interconnect conceptual models created by different users, thus facilitating the global sharing of scientific data contained in such models and creating new learning opportunities for people who start modelling. This paper describes how semantic grounding techniques can be used during the creation of Qualitative Reasoning models, to bridge the gap between the imprecise user terminology and a well defined external common vocabulary. We also explore the application of ontology matching techniques between models, which can provide valuable feedback during the model construction process.
-
Towards a structured approach to building Qualitative Reasoning models and simulations
Ecological Informatics, 2008Co-Authors: Bert Bredeweg, Paulo Salles, J Liem, Anders Bouwer, Tim Nuttle, E. Cioaca, E. Nakova, R. A. A. Noble, A.l.r. Caldas, Y. UzunovAbstract: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.
-
garp3 a new workbench for Qualitative Reasoning and modelling
International Conference on Knowledge Capture, 2007Co-Authors: Bert Bredeweg, Anders Bouwer, Jelmer Jellema, Dirk Bertels, Floris Floris Linnebank, J LiemAbstract:Easy to use workbenches for Qualitative Reasoning (QR) and Modelling are virtually nonexistent. This has a limiting effect on the use and uptake of the technology by a larger audience. We present Garp3, a user-friendly workbench that allows modellers to build, simulate, and inspect Qualitative models. Garp3 can be used to, discover, capture, and share conceptual knowledge on how systems behave.
-
modelling population and community dynamics with Qualitative Reasoning
Ecological Modelling, 2006Co-Authors: Paulo Salles, Bert BredewegAbstract:Ecological knowledge has been characterised as incomplete, fuzzy, uncertain, sparse, empirical, and non-formalised. It is often expressed in Qualitative terms, verbally or diagrammatically. There is a need for new and efficient computer-based tools for making this knowledge explicit, well organised, processable, and integrated with quantitative knowledge. Qualitative Reasoning is an area of artificial intelligence that creates representations for continuous aspects of the world to support Reasoning with little information. Particularly relevant for our work are Qualitative representations of differential equations, (in-)equality Reasoning and the explicit representation of causal relationships between quantities. We present Qualitative models and simulations of population and community dynamics in the Brazilian Cerrado vegetation. The models are organised in clusters of predictive simulation models. The first cluster implements a general theory of population dynamics, with the explicit representation of processes such as natality, mortality, immigration, emigration, colonisation, and population growth. These models are the basis for more complex community models. The second cluster represents interactions between two populations, such as symbiosis, competition, and predation. The third cluster represents the Cerrado succession hypothesis, a commonsense theory of succession in the Cerrado vegetation. It is assumed that fire frequency influences a number of environmental factors. When fire frequency increases succession leads to open grasslands and when fire frequency decreases the vegetation becomes woody and denser. This article shows the potential of Qualitative Reasoning for ecological modelling, particularly for answering questions of interest and making scientifically valid predictions using only Qualitative terms.
-
current topics in Qualitative Reasoning
Ai Magazine, 2004Co-Authors: Bert Bredeweg, Peter StrussAbstract:In this editorial introduction to this special issue of AI Magazine on Qualitative Reasoning, we briefly discuss the main motivations and characteristics of this branch of AI research. We also summarize the contributions in this issue and point out challenges for future research.
Jochen Renz - One of the best experts on this subject based on the ideXlab platform.
-
towards explainable inference about object motion using Qualitative Reasoning
Principles of Knowledge Representation and Reasoning, 2018Co-Authors: Jochen Renz, Hua HuaAbstract:The capability of making explainable inferences regarding physical processes has long been desired. One fundamental physical process is object motion. Inferring what causes the motion of a group of objects can even be a challenging task for experts, e.g., in forensics science. Most of the work in the literature relies on physics simulation to draw such infer- ences. The simulation requires a precise model of the under- lying domain to work well and is essentially a black-box from which one can hardly obtain any useful explanation. By contrast, Qualitative Reasoning methods have the advan- tage in making transparent inferences with ambiguous infor- mation, which makes it suitable for this task. However, there has been no suitable Qualitative theory proposed for object motion in three-dimensional space. In this paper, we take this challenge and develop a Qualitative theory for the motion of rigid objects. Based on this theory, we develop a Reasoning method to solve a very interesting problem: Assuming there are several objects that were initially at rest and now have started to move. We want to infer what action causes the movement of these objects.
-
combining binary constraint networks in Qualitative Reasoning
European Conference on Artificial Intelligence, 2008Co-Authors: Tomasz Kowalski, Jochen RenzAbstract:Constraint networks in Qualitative spatial and temporal Reasoning are always complete graphs. When one adds an extra element to a given network, previously unknown constraints are derived by intersections and compositions of other constraints, and this may introduce inconsistency to the overall network. Likewise, when combining two consistent networks that share a common part, the combined network may become inconsistent. In this paper, we analyse the problem of combining these binary constraint networks and develop certain conditions to ensure combining two networks will never introduce an inconsistency for a given spatial or temporal calculus. This enables us to maintain a consistent world-view while acquiring new information in relation with some part of it. In addition, our results enable us to prove other important properties of Qualitative spatial and temporal calculi in areas such as representability and complexity.
-
Qualitative spatial representation and Reasoning
Foundations of Artificial Intelligence, 2008Co-Authors: Anthony G Cohn, Jochen RenzAbstract:Publisher Summary Early attempts at Qualitative spatial Reasoning within the Qualitative Reasoning (QR) community led to the poverty conjecture. The need for spatial representations and spatial Reasoning is ubiquitous in artificial intelligence (AI) from robot planning and navigation to interpreting visual inputs to understanding natural language. In all these cases, the need to represent and reason about spatial aspects of the world is of key importance. Related fields of research such as geographic information science (GIScience) have also driven the spatial representation and Reasoning community to produce efficient, expressive, and useful calculi. There has been considerable research in spatial representations that are based on metric measurements, in particular within the vision and robotics communities, and also on raster and vector representations in GIScience. This chapter focuses on symbolic and, in particular, Qualitative representations. The challenge of Qualitative spatial Reasoning (QSR) is to provide calculi that allow a machine to represent and reason with spatial entities without resort to the traditional quantitative techniques prevalent in, for example, computer graphics or computer vision communities.
S P Kuznetsov - One of the best experts on this subject based on the ideXlab platform.
-
example of a physical system with a hyperbolic attractor of the smale williams type
Physical Review Letters, 2005Co-Authors: S P KuznetsovAbstract:A simple and transparent example of a nonautonomous flow system with a hyperbolic strange attractor is suggested. The system is constructed on the basis of two coupled van der Pol oscillators, the characteristic frequencies differ twice, and the parameters controlling generation in both oscillators undergo a slow periodic counterphase variation in time. In terms of stroboscopic Poincare sections, the respective 4D mapping has a hyperbolic strange attractor of the Smale-Williams type. Qualitative Reasoning and quantitative data of numerical computations are presented and discussed, e.g., Lyapunov exponents and their parameter dependencies. A special test for hyperbolicity based on analysis of distributions of angles between stable and unstable subspaces of a chaotic trajectory is performed.
-
an example of physical system with hyperbolic attractor of smale williams type
arXiv: Chaotic Dynamics, 2005Co-Authors: S P KuznetsovAbstract:A simple and transparent example of a non-autonomous flow system, with hyperbolic strange attractor is suggested. The system is constructed on a basis of two coupled van der Pol oscillators, the characteristic frequencies differ twice, and the parameters controlling generation in both oscillators undergo a slow periodic counter-phase variation in time. In terms of stroboscopic Poincar\'{e} section, the respective four-dimensional mapping has a hyperbolic strange attractor of Smale - Williams type. Qualitative Reasoning and quantitative data of numerical computations are presented and discussed, e.g. Lyapunov exponents and their parameter dependencies. A special test for hyperbolicity based on statistical analysis of distributions of angles between stable and unstable subspaces of a chaotic trajectory has been performed. Perspectives of further comparative studies of hyperbolic and non-hyperbolic chaotic dynamics in physical aspect are outlined.
Nuria Agell - One of the best experts on this subject based on the ideXlab platform.
-
absolute order of magnitude Reasoning applied to a social multi criteria evaluation framework
Journal of Experimental and Theoretical Artificial Intelligence, 2016Co-Authors: Arayeh Afsordegan, Nuria Agell, Monica Sanchez, Juan Carlos Aguado, Gonzalo GamboaAbstract:A social multi-criteria evaluation framework for solving a real-case problem of selecting a wind farm location in the regions of Urgell and Conca de Barbera in Catalonia (northeast of Spain) is studied. This paper applies a Qualitative multi-criteria decision analysis approach based on linguistic labels assessment able to address uncertainty and deal with different levels of precision. This method is based on Qualitative Reasoning as an artificial intelligence technique for assessing and ranking multi-attribute alternatives with linguistic labels in order to handle uncertainty. This method is suitable for problems in the social framework such as energy planning which require the construction of a dialogue process among many social actors with high level of complexity and uncertainty. The method is compared with an existing approach, which has been applied previously in the wind farm location problem. This approach, consisting of an outranking method, is based on Condorcet's original method. The results ob...
-
a consensus model for delphi processes with linguistic terms and its application to chronic pain in neonates definition
Applied Soft Computing, 2015Co-Authors: Nuria Agell, Llorenç Roselló, Christ Jan Van Ganzewinkel, Monica Sanchez, Francesc Prats, Peter AndriessenAbstract:Graphical abstractDisplay Omitted This paper proposes a new model of consensus based on linguistic terms to be implemented in Delphi processes. The model of consensus involves Qualitative Reasoning techniques and is based on the concept of entropy. The proposed model has the ability to reach consensus automatically without the need for either a moderator or a final interaction among panelists. In addition, it permits panelists to answer with different levels of precision depending on their knowledge on each question. The model defined has been used to establish the relevant features for the definition of a type of chronic disease. A real-case application conducted in the Department of Neonatology of Maxima Medical Center in The Netherlands is presented. This application considers the opinions of stakeholders of neonate health-care in order to reach a final consensual definition of chronic pain in neonates.
-
a new approach for delphi processes based on group consensus with linguistic terms
IEEE International Conference on Fuzzy Systems, 2014Co-Authors: Nuria Agell, Llorenç Roselló, Christ Jan Van Ganzewinkel, Monica Sanchez, Francesc Prats, Peter AndriessenAbstract:A new approach for Delphi processes including a measure of consensus based on linguistic terms is introduced in this paper. The measure of consensus involves Qualitative Reasoning techniques and is based on the concept of entropy. In the proposed approach, consensus is reached automatically without the need for neither a moderator nor a final interaction among panelists. In addition, it permits panelists to answer with different levels of precision depending on their knowledge on each question. An illustrative example considering the opinions of stake holders in neonate health-care to reach a final consensual definition of chronic pain in neonates is presented.
-
Using L-fuzzy sets to introduce information theory into Qualitative Reasoning
Fuzzy Sets and Systems, 2014Co-Authors: Francesc Prats, Monica Sanchez, Llorenç Roselló, Nuria AgellAbstract:We formally construct the extended set of Qualitative labels L over a well-ordered set. The Qualitative descriptions of a given set are defined as L-fuzzy sets. In the case where the well-ordered set is finite, a distance between L-fuzzy sets is introduced based on the properties of the lattice L. The concept of the information contained in a Qualitative label is introduced, leading to a formal definition of the entropy of an L-fuzzy set as a Lebesgue integral. In the discrete case, this integral becomes a weighted average of the information of the labels, corresponding to the Shannon entropy in information theory.
-
using Qualitative Reasoning to measure discrepancy and consensus in group decision
Intelligent Systems Design and Applications, 2010Co-Authors: Llorenç Roselló, Monica Sanchez, Francesc Prats, Nuria AgellAbstract:The measurement of consensus and discrepancy among groups of evaluators is an important issue in group decision systems. These measurements will enable us to analyze the effort that should be made to obtain closer positions among subgroups. This paper presents a new approach, on the basis of the absolute order-of-magnitude Qualitative model, to decision-making problems. The concepts of Qualitative distance and entropy are defined in the framework of the distributive lattice of qualitativizations over a set of magnitudes or features.
