Embedding defaults into terminological knowledge representation formalisms

We consider the problem of integrating Reiter's default logic into terminological representation systems. It turns out that such an integration is less straightforward than we expected, considering the fact that the terminological language is a decidable sublanguage of first-order logic. Semantically, one has the unpleasant effect that the consequences of a terminological default theory may be rather unintuitive, and may even vary with the syntactic structure of equivalent concept expressions. This is due to the unsatisfactory treatment of open defaults via Skolemization in Reiter's semantics. On the algorithmic side, we show that this treatment may lead to an undecidable default consequence relation, even though our base language is decidable, and we have only finitely many (open) defaults. Because of these problems, we then consider a restricted semantics for open defaults in our terminological default theories: default rules are applied only to individuals that are explicitly present in the knowledge base. In this semantics it is possible to compute all extensions of a finite terminological default theory, which means that this type of default reasoning is decidable. We describe an algorithm for computing extensions and show how the inference procedures of terminological systems can be modified to give optimal support to this algorithm.This is a revised and extended version of a paper presented at the3rd International Conference on Principles of Knowledge Representation and Reasoning, October 1992, Cambridge, MA.     

A hybrid scheme for action representation

Strong deficiencies are present in symbolic models for action representation and planning, regarding mainly the difficulty of coping with real, complex environments. These deficiencies can be attributed to several problems, such as the inadequacy in coping with incompletely structured situations, the difficulty of interacting with visual and motorial aspects, the difficulty in representing low-level knowledge, the need to specify the problem at a high level of detail, and so on. Besides the purely symbolic approaches, several nonsymbolic models have been developed, such as the recent class of subsym-bolic techniques. A promising paradigm for the modeling of reasoning, which combines features of both symbolic and analogical approaches, is based on the construction of analogical models of the reference for the internal representations, as introduced by Johnson-Laird. In this work, we propose a similar approach to the problem of knowledge representation and reasoning about actions and plans. We propose a hybrid approach, symbolic and analogical, in which the inferences are partially devolved to measurements on analogical models generated starting from the symbolic representation. the interaction between the symbolic and the analogical level is due to the fact that procedures are connected to some symbols, allowing generating, updating, and verifying the mental model. the hybrid model utilizes, for the symbolic component, a representation system based on the distinction between terminological and assertional knowledge. the terminological component adopts a SI-Net formalism, extended by temporal primitives. the assertional component is a subset of first-order logics. the analogical representation is a set of concurrent procedures modeling parts of the world, action processes, simulations, and metaphors based on force fields concepts. A particular case study, regarding the problem of the assembly of a complex object from parts, is taken as an experimental paradigm. © 1993 John Wiley Sons, Inc.     

Applicability of various formalisms for representation of excess free energy in binary metallic solutions

Some important equations with a small number of parameters such as the subregular-, the Krupkowski-, the Hajra-, the Wilson-, and the Hucke-quations were applied quantitatively and statistically to the selected metallic systems in which the thermodynamic data have been experimentally known to a high degree of accuracy. The applicability of a given equation could be shown by investigating the nature of the equation, that is, the significance of the parameters, the ratio of both excess partials at infinite dilution, stability function, and the extent of strong interation.     

Gamification in theory and action: A survey

Gamification has drawn the attention of academics, practitioners and business professionals in domains as diverse as education, information studies, human–computer interaction, and health. As yet, the term remains mired in diverse meanings and contradictory uses, while the concept faces division on its academic worth, underdeveloped theoretical foundations, and a dearth of standardized guidelines for application. Despite widespread commentary on its merits and shortcomings, little empirical work has sought to validate gamification as a meaningful concept and provide evidence of its effectiveness as a tool for motivating and engaging users in non-entertainment contexts. Moreover, no work to date has surveyed gamification as a field of study from a human–computer studies perspective. In this paper, we present a systematic survey on the use of gamification in published theoretical reviews and research papers involving interactive systems and human participants. We outline current theoretical understandings of gamification and draw comparisons to related approaches, including alternate reality games (ARGs), games with a purpose (GWAPs), and gameful design. We present a multidisciplinary review of gamification in action, focusing on empirical findings related to purpose and context, design of systems, approaches and techniques, and user impact. Findings from the survey show that a standard conceptualization of gamification is emerging against a growing backdrop of empirical participants-based research. However, definitional subjectivity, diverse or unstated theoretical foundations, incongruities among empirical findings, and inadequate experimental design remain matters of concern. We discuss how gamification may to be more usefully presented as a subset of a larger effort to improve the user experience of interactive systems through gameful design. We end by suggesting points of departure for continued empirical investigations of gamified practice and its effects.     

A survey on vision-based human action recognition

Vision-based human action recognition is the process of labeling image sequences with action labels. Robust solutions to this problem have applications in domains such as visual surveillance, video retrieval and human–computer interaction. The task is challenging due to variations in motion performance, recording settings and inter-personal differences. In this survey, we explicitly address these challenges. We provide a detailed overview of current advances in the field. Image representations and the subsequent classification process are discussed separately to focus on the novelties of recent research. Moreover, we discuss limitations of the state of the art and outline promising directions of research.     

A survey on deep geometry learning: From a representation perspective

Computational Visual Media - Researchers have achieved great success in dealing with 2D images using deep learning. In recent years, 3D computer vision and geometry deep learning have gained ever...     

A tutorial survey of job-shop scheduling problems using genetic algorithms-I. representation

Job-shop scheduling problem (abbreviated to JSP) is one of the well-known hardest combinatorial optimization problems. During the last three decades, the problem has captured the interest of a significant number of researchers and a lot of literature has been published, but no efficient solution algorithm has been found yet for solving it to optimality in polynomial time. This has led to recent interest in using genetic algorithms (GAs) to address it. The purpose of this paper and its companion (Part II: Hybrid Genetic Search Strategies) is to give a tutorial survey of recent works on solving classical JSP using genetic algorithms. In Part I, we devote our attention to the representation schemes proposed for JSP. In Part II, we will discuss various hybrid approaches of genetic algorithms and conventional heuristics. The research works on GA/JSP provide very rich experiences for the constrained combinatorial optimization problems. All of the techniques developed for JSP may be useful for other scheduling problems in modern flexible manufacturing systems and other combinatorial optimization problems.     

A survey on still image based human action recognition

Recently still image-based human action recognition has become an active research topic in computer vision and pattern recognition. It focuses on identifying a person?s action or behavior from a single image. Unlike the traditional action recognition approaches where videos or image sequences are used, a still image contains no temporal information for action characterization. Thus the prevailing spatiotemporal features for video-based action analysis are not appropriate for still image-based action recognition. It is more challenging to perform still image-based action recognition than the video-based one, given the limited source of information as well as the cluttered background for images collected from the Internet. On the other hand, a large number of still images exist over the Internet. Therefore it is demanding to develop robust and efficient methods for still image-based action recognition to understand the web images better for image retrieval or search. Based on the emerging research in recent years, it is time to review the existing approaches to still image-based action recognition and inspire more efforts to advance the field of research. We present a detailed overview of the state-of-the-art methods for still image-based action recognition, and categorize and describe various high-level cues and low-level features for action analysis in still images. All related databases are introduced with details. Finally, we give our views and thoughts for future research.     

Dynamic interpretations of constraint-based grammar formalisms

We present a rendering of some common grammatical formalisms in terms of evolving algebras. Though our main concern in this paper is on constraint-based formalisms, we also discuss the more basic case of context-free grammars. Our aim throughout is to highlight the use of evolving algebras as a specification tool to obtain grammar formalisms.     

Uncertainty representation in software models: a survey

Software and Systems Modeling - This paper provides a comprehensive overview and analysis of research work on how uncertainty is currently represented in software models. The survey presents the...     

On the evaluation of argumentation formalisms

Argumentation theory has become an important topic in the field of AI. The basic idea is to construct arguments in favor and against a statement, to select the “acceptable” ones and, finally, to determine whether the original statement can be accepted or not. Several argumentation systems have been proposed in the literature. Some of them, the so-called rule-based systems, use a particular logical language with strict and defeasible rules. While these systems are useful in different domains (e.g. legal reasoning), they unfortunately lead to very unintuitive results, as is discussed in this paper. In order to avoid such anomalies, in this paper we are interested in defining principles, called rationality postulates, that can be used to judge the quality of a rule-based argumentation system. In particular, we define two important rationality postulates that should be satisfied: the consistency and the closure of the results returned by that system. We then provide a relatively easy way in which these rationality postulates can be warranted for a particular rule-based argumentation system developed within a European project on argumentation.     

Exploring probabilistic localized video representation for human action recognition

In recent years, the bag-of-words (BoW) video representations have achieved promising results in human action recognition in videos. By vector quantizing local spatial temporal (ST) features, the BoW video representation brings in simplicity and efficiency, but limitations too. First, the discretization of feature space in BoW inevitably results in ambiguity and information loss in video representation. Second, there exists no universal codebook for BoW representation. The codebook needs to be re-built when video corpus is changed. To tackle these issues, this paper explores a localized, continuous and probabilistic video representation. Specifically, the proposed representation encodes the visual and motion information of an ensemble of local ST features of a video into a distribution estimated by a generative probabilistic model. Furthermore, the probabilistic video representation naturally gives rise to an information-theoretic distance metric of videos. This makes the representation readily applicable to most discriminative classifiers, such as the nearest neighbor schemes and the kernel based classifiers. Experiments on two datasets, KTH and UCF sports, show that the proposed approach could deliver promising results.     

A new disk-based technique for solving the largeness problem of stochastic modeling formalisms

Stochastic modeling formalisms such as stochastic Petri nets, generalized stochastic Petri nets, and stochastic reward nets can be used to model and evaluate the dynamic behavior of realistic computer systems. Once we translate the stochastic system model to the underlying corresponding Markov Chain (MC), the developed MC grows wildly to several hundred thousands states. This problem is known as the largeness problem. To tolerate the largeness problem of Markov models, several iterative and direct methods have been proposed in the literature. Although the iterative methods provide a feasible solution for most realistic systems, a major problem appears when these methods fail to reach a solution. Unfortunately, the direct method represents an undesirable numerical technique for tolerating large matrices due to the fill-in problem. In order to solve such problem, in this paper, we develop a disk-based segmentation (DBS) technique based on modifying the Gauss Elimination (GE) technique. The proposed technique has the capability of solving the consequences of the fill-in problem without making assumptions about the underlying structure of the Markov processes of the developed model. The DBS technique splits the matrix into a number of vertical segments and uses the hard disk to store these segments. Using the DBS technique, we can greatly reduce the memory required as compared to that of the GE technique. To minimize the increase in the solution time due to the disk accessing processes, the DBS utilizes a clever management technique for such processes. The effectiveness of the DBS technique has been demonstrated by applying it to a realistic model for the Kanban manufacturing system.     

Hardness of preorder checking for basic formalisms

We investigate the complexity of preorder checking when the specification is a flat finite-state system whereas the implementation is either a non-flat finite-state system or a standard timed automaton. In both cases, we show that simulation checking is Exptime-hard, and for the case of a non-flat implementation, the result holds even if there is no synchronization between the parallel components and their alphabets of actions are pairwise disjoint. Moreover, we show that the considered problems become Pspace-complete when the specification is assumed to be deterministic. Additionally, we establish that comparing a synchronous non-flat system with no hiding and a flat system is Pspace-hard for any relation between trace containment and bisimulation equivalence, even if the flat system is assumed to be fixed.