An artificial intelligence approach to language instruction

This paper describes an implemented, prototype system for a sophisticated, intelligent tutor for instruction in a foreign language. The system is an application of artificial intelligence research in natural language, but it implements several ideas that depart from standard approaches to natural language understanding.

For instance, the semantic analyzer diagnoses several kinds of comprehension problems and semantic errors that a student might make. Some fine distinctions in meaning are represented to detect misuse of words. Not only is a model of good syntax included in the tutor, but also a model of incorrect forms, rich enough to pinpoint specific syntactic mistakes. Finding the intended interpretation is complicated by the likelihood of student errors. Therefore, perfect syntactic form is not necessary for semantic analysis of the student's input.

The problems discussed and solutions presented are closely related to the more general problem of how to respond to a natural language input that surpasses the computer's model of language or of context.     

An artificial life approach to dense stereo disparity

This article presents an adaptive approach to improving the infection algorithm that we have used to solve the dense stereo matching problem. The algorithm presented here incorporates two different epidemic automata along a single execution of the infection algorithm. The new algorithm attempts to provide a general behavior of guessing the best correspondence between a pair of images. Our aim is to provide a new strategy inspired by evolutionary computation, which combines the behaviors of both automata into a single correspondence problem. The new algorithm will decide which automata will be used based on the transmission of information and mutation, as well as the attributes, texture, and geometry, of the input images. This article gives details about how the rules used in the infection algorithm are coded. Finally, we show experiments with a real stereo pair, as well as with a standard test bed, to show how the infection algorithm works.     

A quantitative approach to belief revision in structured probabilistic argumentation

Many real-world knowledge-based systems must deal with information coming from different sources that invariably leads to incompleteness, overspecification, or inherently uncertain content. The presence of these varying levels of uncertainty doesn’t mean that the information is worthless – rather, these are hurdles that the knowledge engineer must learn to work with. In this paper, we continue work on an argumentation-based framework that extends the well-known Defeasible Logic Programming (DeLP) language with probabilistic uncertainty, giving rise to the Defeasible Logic Programming with Presumptions and Probabilistic Environments (DeLP3E) model. Our prior work focused on the problem of belief revision in DeLP3E, where we proposed a non-prioritized class of revision operators called AFO (Annotation Function-based Operators) to solve this problem. In this paper, we further study this class and argue that in some cases it may be desirable to define revision operators that take quantitative aspects into account, such as how the probabilities of certain literals or formulas of interest change after the revision takes place. To the best of our knowledge, this problem has not been addressed in the argumentation literature to date. We propose the QAFO (Quantitative Annotation Function-based Operators) class of operators, a subclass of AFO, and then go on to study the complexity of several problems related to their specification and application in revising knowledge bases. Finally, we present an algorithm for computing the probability that a literal is warranted in a DeLP3E knowledge base, and discuss how it could be applied towards implementing QAFO-style operators that compute approximations rather than exact operations.     

An artificial intelligence planning approach to manufacturing feature recognition

Within manufacturing, features have been widely accepted as useful concepts, and in particular they are used as an interface between CAD and CAPP systems. Previous research on feature recognition focus on the issues of intersecting features and multiple interpretations, but do not address the problem of custom features representation. Representation of features is an important aspect for making feature recognition more applicable in practice. In this paper a hybrid procedural and knowledge-based approach based on artificial intelligence planning is presented, which addresses both classic feature interpretation and also feature representation problems. STEP designs are presented as case studies in order to demonstrate the effectiveness of the model.     

An artificial ant colonies approach to medical image segmentation

The success of image analysis depends heavily upon accurate image segmentation algorithms. This paper presents a novel segmentation algorithm based on artificial ant colonies (AC). Recent studies show that the self-organization of ants is similar to neurons in the human brain in many respects. Therefore, it has been used successfully for understanding biological systems. It is also widely used in many applications in robotics, computer graphics, etc. Considering the features of artificial ant colonies, we present an extended model for image segmentation. In our model, each ant can memorize a reference object, which will be refreshed when it finds a new target. A fuzzy connectedness measure is adopted to evaluate the similarity between target and the reference object. The behavior of an ant is affected by the neighbors and the cooperation between ants is performed by exchanging information through pheromone updating. Experimental results show that the new algorithm can preserve the detail of the object and is also insensitive to noise.     

An approach to the software quality management

This article is devoted to an engineering approach to software quality management. The approach is oriented toward the attainment of the software maturity, which is a key software quality characteristic. A model of quality and a conceptual model for making decisions on software quality management are developed. They are based on methods of specification of quality requirements to the maturity of software components, defect prediction, and systematic quantitative reliability control during software development. A graphical defect prediction model (a Bayesian Belief Network) is presented. __________ Translated from Kibernetika i Sistemnyi Analiz, No. 5, pp. 174–185, September–October 2006.     

A network-based approach to the multi-activity combined timetabling and crew scheduling problem: Workforce scheduling for public health policy implementation

This paper introduces the multi-activity combined timetabling and crew scheduling problem. The goal of this problem is to schedule the minimum number of workers required in order to successfully visit a set of customers characterized by services needed matched against schedule availability. Two solution strategies are proposed. The first is based on mathematical programming whilst the second uses a heuristic procedure in order to reduce computational time. The proposed model combines timetabling with crew scheduling decisions in one mixed integer programming model which considers multiple activities. The algorithms are tested on randomly generated and real instances provided by the Health to School Initiative, a program based at Bogotá’s local Health Department. The results show that the Initiative can increase its coverage by up to 68% using the proposed heuristic approach as a planning process tool.     

An intelligence-based route choice model for pedestrian flow in a transportation station

This study proposes a method that uses an artificial neural network (ANN) to mimic human decision-making about route choice in a crowded transportation station. Although ANN models have been developed rapidly and widely adopted in various fields in the last three decades, their application to predict human decision-making in pedestrian flows is limited, because the video clip technology used to collect pedestrian movement data in crowded conditions is still primitive. Data collection must be carried out manually or semi-manually, which requires extensive resources and is time consuming. This study adopts a semi-manual approach to extract data from video clips to capture the route choice behaviour of travellers, and then applies an ANN to mimic such decision-making. A prediction accuracy of 86% (ANN model with ensemble approach) is achieved, which demonstrates the feasibility of applying the ANN approach to decision-making in pedestrian flows.     

针对无线网络的入侵检测系统设计方法研究

从实际应用出发,提出了一种针对无线网络的入侵检测方法,给出了入侵检测系统的设计方案,扩展了系统管理员的安全管理能力(包括安全审计、监视、攻击识别和响应),具有较强的经济效益和借鉴意义.     

An integrated approach for simulation of mechatronic systems applied to a hexapod robot

Mechatronics is the integration of mechanism, electronics and computer control to produce a functional system. The design process involves application of many engineering areas and various approaches are possible. Computer programs are available in different engineering areas. Engineers define systems and inputs, and user-friendly programs establish mathematical models, solve them and give simulation outputs. In this study, SolidWorks is used for solid modeling and assembly, CosmosMotion is used for rigid body dynamics, CosmosWorks is used for finite element vibration and strength analyses, and Adlink module is used for actuator control. The integration of the design process is achieved with a main program developed in Visual Basic, which uses the application programming interface (API) capabilities. The procedure is applied to a hexapod robot. The robot has been produced to develop and test the procedure. CosmosMotion results are verified by the analytical results obtained from the dynamic equations of the hexapod. Besides known kinematic workspace definition of robots, kinetic and rigidity workspace concepts are introduced. Mechatronic systems can be designed and evaluated easily and effectively by using the design process developed in this work.     

EXSHOF: An artificial intelligence approach to high-order filter synthesis

A two-tier expert-system-based synthesis of high-order filters is developed. Expert-system techniques have been used for choosing a particular biquad structure and for selection of the high-order filter structure as well. The data inputs will be either filter specifications or transfer functions and the output will be a detailed filter circuit with all its element values. EXSHOF ( pert System based ynthesis of igh rder ilter) is totally menu-driven and highly interactive. It assumes little knowledge of computers or filters from the user. The complete package is implemented in Turbo PASCAL and Turbo PROLOG languages.     

An artificial immune system approach to CNC tool path generation

Reduced machining time and increased accuracy for a sculptured surface are both very important when producing complicated parts, so, the step-size and tool-path interval are essential components in high-speed and high-resolution machining. If they are too small, the machining time will increase, whereas if they are too large, rough surfaces will result. In particular, the machining time, which is a key factor in high-speed machining, is affected by the tool-path interval more than the step size. The present paper introduces a ‘system software’ developed to reduce machining time and increased accuracy for a sculptured surface with Non-Uniform Rational B-Spline (NURBS) patches. The system is mainly based on a new and a powerful artificial intelligence (AI) tool, called artificial immune systems (AIS). It is implemented using C programming language on a PC. It can be used as stand alone system or as the integrated module of a CNC machine tool. With the use of AIS, the impact and power of AI techniques have been reflected on the performance of the tool path optimization system. The methodology of the developed tool path optimization system is illustrated with practical examples in this paper.     

Individual difference of artificial emotion applied to a service robot

In order to enable personalized natural interaction in service robots, artificial emotion is needed which helps robots to appear as individuals. In the emotion modeling theory of emotional Markov chain model (eMCM) for spontaneous transfer and emotional hidden Markov model (eHMM) for stimulated transfer, there are three problems: 1) Emotion distinguishing problem: whether adjusting parameters of the model have any effects on individual emotions; 2) How much effect the change makes; 3) The problem of different initial emotional states leading to different resultant emotions from a given stimuli. To solve these problems, a research method of individual emotional difference is proposed based on metric multidimensional scaling theory. Using a dissimilarity matrix, a scalar product matrix is calculated. Subsequently, an individual attribute reconstructing matrix can be obtained by principal component factor analysis. This can display individual emotion difference with low dimension. In addition, some mathematical proofs are carried out to explain experimental results. Synthesizing the results and proofs, corresponding conclusions are obtained. This new method provides guidance for the adjustment of parameters of emotion models in artificial emotion theory.     

An approach to novelty detection applied to the classification of image regions

We present a new framework for novelty detection. The framework evaluates neural networks as adaptive classifiers that are capable of novelty detection and retraining on the basis of newly discovered information. We apply our newly developed model to the application area of object recognition in video. We detail the tools and methods needed for novelty detection such that data from unknown classes can be reliably rejected without any a priori knowledge of its characteristics. The rejected data is postprocessed to determine which samples can be manually labeled of a new type and used for retraining. We compare the proposed framework with other novelty detection methods and discuss the results of adaptive retraining of neural network to recognize further unseen data containing the newly added objects.     

An approach to efficient planning with numerical fluents and multi-criteria plan quality

Dealing with numerical information is practically important in many real-world planning domains where the executability of an action can depend on certain numerical conditions, and the action effects can consume or renew some critical continuous resources, which in pddl can be represented by numerical fluents. When a planning problem involves numerical fluents, the quality of the solutions can be expressed by an objective function that can take different plan quality criteria into account.We propose an incremental approach to automated planning with numerical fluents and multi-criteria objective functions for pddl numerical planning problems. The techniques in this paper significantly extend the framework of planning with action graphs and local search implemented in the lpg planner. We define the numerical action graph (NA-graph) representation for numerical plans and we propose some new local search techniques using this representation, including a heuristic search neighborhood for NA-graphs, a heuristic evaluation function based on relaxed numerical plans, and an incremental method for plan quality optimization based on particular search restarts. Moreover, we analyze our approach through an extensive experimental study aimed at evaluating the importance of some specific techniques for the performance of the approach, and at analyzing its effectiveness in terms of fast computation of a valid plan and quality of the best plan that can be generated within a given CPU-time limit. Overall, the results show that our planner performs quite well compared to other state-of-the-art planners handling numerical fluents.     

Safeguards design for a plutonium concentrator: An applied estimation approach

In this paper we consider the design of a nonlinear estimator to be used in conjunction with on-line detectors for a plutonium nitrate concentrator. Using a complex state-of-the-art process model to simulate ‘realistic’ data, we show that the estimator performance using a simplified process model is adequate over a wide range of operation. The estimator is used to simulate and characterize some on-line diversion detectors, i.e. detectors designed to indicate if some of the critical special nuclear material in process is stolen or diverted from the unit. Simulation results indicate that the estimators and detectors yielded reasonable performance for the scenarios investigated.     

Cognitive robotics: a new approach to artificial intelligence

This paper describes a research program about how to achieve artificial intelligence by building robots. It is part of the behavior-oriented AI approach, but differs in some of its hypotheses and methodological approach. This work was presented, in part, at the Third International Symposium on Artificial Life and Robotics, Oita, Japan, January 19–21, 1998     

An approach for sub-ontology evolution in a distributed health care enterprise

In response to the changing nature of health issues, standardized health ontologies such as SNOMED CT and UMLS incline to change more frequently than most other domain ontologies. Yet, semantic interoperability shared among institutions within a distributed health care enterprise relies heavily on the availability of a valid and up-to-date standardized ontology. In this paper, we propose the creation and preservation of sub-ontologies to deal with the frequent changes in health ontologies. Our approach focuses on the nature and characteristics of standard health ontologies, however it can also be applied to other domain ontologies with similar characteristics. Our sub-ontology evolution approach defines ways to create valid sub-ontologies for each specific health application, and to effectively develop a series of propagation mechanism when the main ontology changes. Our approach will (i) isolate the required change propagation to the relevant health applications that utilized the changing concepts only, and (ii) optimize the propagation mechanism to include the minimum number of operations only. Since a sub-ontology should be a valid ontology by itself, the change propagation approach used in this process should contain the rules to assure the validity of the produced sub-ontology while keeping the consistency of the sub-ontology to the evolved base ontology. A change identification process, which considers the nature of the health ontology change logs, is conducted to identify the semantics of the changes. From the evaluation, it is shown that the content of the evolved sub-ontologies produced using our approach is consistent to the evolved base ontology. Moreover, the propagation process can be performed more efficiently because the number of operations required for our change propagation method is lower than the number of operations required for direct re-extraction from the evolved base ontology.