Spatio-temporal Inference

This brief presents some further results on the finite-time stability of probabilistic logical networks (PLNs). By semi-tensor product technique routinely, the dynamic behavior of a PLN is characterized by its corresponding state transition graph (STG). Then, an irradiative result is found. That is, a PLN is globally stable within finite time, if and only if, its STG is acyclic, except for the self loop at the pre-designated vertex. Based on this observation, some properties of STG, which is associated with a finite-time stable PLN, are formulated. The most significant finding is that the determinant of its anti-adjacency matrix is compactly related to the existence of a Hamilton path and is only equal to 0 or 1. Afterwards, the topological sort of all the vertices in STG is defined. As a consequence, two topological sorting algorithms are presented to analyze the stability of PLNs applicably and efficiently. Finally, a simulation example is employed to illustrate the applicability of the obtained results.

Investigation of functional properties of optical ceramics TlCl0.74Br0.26 – AgI systems

In this paper, we consider stabilization and reconstruction of sampled-data Boolean control networks (BCNs) under noisy sampling interval. A sampled-data BCN under noisy sampling interval is first converted into a probabilistic Boolean network (PBN). We then obtain some necessary and sufficient conditions for global stochastic stability of the considered sampled-data BCN under two types of noisy sampling intervals. However, in analyzing the stochastic stability of large-scale sampled-data BCNs under noisy sampling interval, using the above necessary and sufficient conditions leads to huge computational cost. Therefore, for a large-scale sampled-data BCN, we have to transform it into a size-reduced probabilistic logical network. Then by studying the stochastic stability of the probabilistic logical network, some sufficient conditions for global stochastic stability of the large-scale sampled-data BCN are obtained. Moreover, based on the given steady-state probabilities of the transformed PBN, the reconstruction problem of sampled-data BCNs under noisy sampling interval can be well solved as a linear programming (LP) problem.

Set stability of probabilistic multi-valued logical networks (PMLNs) is investigated in this paper. Different from the existing concepts of set stability, the set stability considered in this paper is starting from a given initial set to a given destination set. Firstly, an algebraic expression of PMLNs is established by using the semi-tensor product method. Then, a matrix method is proposed to obtain the largest invariant subset contained in the given destination set. Based on the invariant subset, a necessary and sufficient condition for set stability from a given initial set to a destination set is presented. Finally, application to state-based games is considered, and the criterion proposed for PMLNs is applied to verify the stability of recurrent state equilibrium.

Asymptotic stability of probabilistic logical networks with random impulsive effects

Now we turn to CogPrime’s methods for handling declarative knowledge—beginning with a series of chapters discussing the Probabilistic Logic Networks (PLN) [GMIH08] approach to uncertain logical reasoning, and then turning to chapters on pattern mining and concept creation.

Writing is considered as an important skill in learning process which is needed to be mastered by the students. However, in teaching learning process at schools or universities, the assessment of writing skill is not becoming the focus of learning process and the assessment is administered inappropriately. In this present study, the researcher undertook the study which dealt with assessing descriptive paragraph writing ability of the students through picture description by employing an ex post facto as the research design. The present study was intended to answer the research problem dealing with the extent of the students’ achievement of descriptive paragraph writing ability which is assessed through picture description. The samples under the study were 40 students determined by means of random sampling technique with lottery system. The data were collected through administering picture description as the research instrument. The obtained data were analyzed by using norm-reference measure of five standard values. The results of the data analysis showed that there were 67.50% samples of the study were successful in writing descriptive paragraph, while there were 32.50% samples were unsuccessful in writing descriptive paragraph which was assessed by administering picture description test

Knowledge of spatial movement patterns in soccer occurring on a regular basis can give a soccer coach, analyst or reporter insights in the playing style or tactics of a group of players or team. Furthermore, it can support a coach to better prepare for a soccer match by analysing (trained) movement patterns of both his own as well as opponent players. We explore the use of the Qualitative Trajectory Calculus (QTC), a spatiotemporal qualitative calculus describing the relative movement between objects, for spatial movement pattern recognition of players movements in soccer. The proposed method allows for the recognition of spatial movement patterns that occur on different parts of the field and/or at different spatial scales. Furthermore, the Levenshtein distance metric supports the recognition of similar movements that occur at different speeds and enables the comparison of movements that have different temporal lengths. We first present the basics of the calculus, and subsequently illustrate its applicability with a real soccer case. To that end, we present a situation where a user chooses the movements of two players during 20 seconds of a real soccer match of a 2016–2017 professional soccer competition as a reference fragment. Following a pattern matching procedure, we describe all other fragments with QTC and calculate their distance with the QTC representation of the reference fragment. The top-k most similar fragments of the same match are presented and validated by means of a duo-trio test. The analyses show the potential of QTC for spatial movement pattern recognition in soccer.

A method to extend ontologies with the assistance of automated reasoning systems and preserving a kind of completeness with respect to their associate conceptualizations is presented. The use of such systems makes feasible the ontological insertion of new concepts, but it is necessary to re-interpret the older ones with respect to new ontological commitments. We illustrate the method extending a well-known ontology about spatial relationships, the called Region Connection Calculus.

Continuously moving objects are prevalent in many domains. Although there have been attempts to combine both spatial and temporal relationships from a reasoning, a database, as well as from a logical perspective, the question remains how to describe motion adequately within a qualitative calculus. In this paper, a Qualitative Trajectory Calculus (QTC) for representing and reasoning about moving objects in two dimensions is presented. Specific attention is given to a central concept in qualitative reasoning, namely the composition of relations. The so-called composition-rule table is presented, which is a neat way of representing a composition table. The usefulness of QTC and the composition-rule table is illustrated by an example.

Representing moving objects in computer-based expert systems: the overtake event example

The analysis and understanding of human-robot joint spatial behaviour (JSB) - such as guiding, approaching, departing, or coordinating movements in narrow spaces - and its communicative and dynamic aspects are key requirements on the road towards more intuitive interaction, safe encounter, and appealing living with mobile robots. This endeavours demand for appropriate models and methodologies to represent JSB and facilitate its analysis. In this paper, we adopt a qualitative trajectory calculus (QTC) as a formal foundation for the analysis and representation of such spatial behaviour of a human and a robot based on a compact encoding of the relative trajectories of two interacting agents in a sequential model. We present this QTC together with a distance measure and a probabilistic behaviour model and outline its usage in an actual JSB study. We argue that the proposed QTC coding scheme and derived methodologies for analysis and modelling are flexible and extensible to be adapted for a variety of other scenarios and studies.

The idea of Conceptual Neighbourhood Diagram (CND) has proved its relevance in the areas of qualitative reasoning about time and qualitative reasoning about space. In this work, a CND is constructed for the Qualitative Trajectory Calculus (QTC), being a calculus for representing and reasoning about movements of objects. The CND for QTC is based on two central concepts having their importance in the qualitative approach: the theory of dominance and the conceptual distance between qualitative relations. Some examples are given for illustrating the use and the potentials of the CND for QTC from the point of view of GIScience.KeywordsTopological RelationCardinal DirectionQualitative ReasoningConceptual DistanceQualitative SimulationThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Reasoning on the context of human beings is crucial for many real-world applications especially for those deploying autonomous systems (e.g. robots). In this paper, we present a new approach for context reasoning to further advance the field of human motion prediction. We therefore propose a neuro-symbolic approach for human motion prediction (NeuroSyM), which weights differently the interactions in the neighbourhood by leveraging an intuitive technique for spatial representation called Qualitative Trajectory Calculus (QTC). The proposed approach is experimentally tested on medium and long term time horizons using two architectures from the state of art, one of which is a baseline for human motion prediction and the other is a baseline for generic multivariate time-series prediction. Six datasets of challenging crowded scenarios, collected from both fixed and mobile cameras, were used for testing. Experimental results show that the NeuroSyM approach outperforms in most cases the baseline architectures in terms of prediction accuracy.

RCC8 is a set of eight jointly exhaustive and pairwise disjoint binary relations representing mereotopological relationships between ordered pairs of individuals. Although the RCC8 relations were originally presented as defined relations of Region Connection Calculus (RCC), virtually all implementations use the RCC8 Composition Table (CT) rather than the axioms of RCC. This raises the question of which mereotopology actually underlies the RCC8 composition table. In this paper, we characterize the algebraic and mereotopological properties of the RCC8 CT based on the metalogical relationship between the first-order theory that captures the RCC8 CT and Ground Mereotopology (MT) of Casati and Varzi. In particular, we show that the RCC8 theory and MT are relatively interpretable in each other. We further show that a nonconservative extension of the RCC8 theory that captures the intended interpretation of the RCC8 relations is logically synonymous with MT, and that a conservative extension of MT is logically synonymous with the RCC8 theory. We also present a characterization of models of MT up to isomorphism, and explain how such a characterization provides insights for understanding models of the RCC8 theory.