Language in the Arab world presents a complex diglossic and multilingual setting, involving the use of Modern Standard Arabic, various dialects and sub-dialects, as well as multiple European languages. This diverse linguistic landscape has given rise to code-switching, both within Arabic varieties and between Arabic and foreign languages. The widespread occurrence of code-switching across the region makes it vital to address these linguistic needs when developing language technologies. In this paper, we provide a review of the current literature in the field of code-switched Arabic NLP, offering a broad perspective on ongoing efforts, challenges, research gaps, and recommendations for future research directions.

Data sparsity is one of the main challenges posed by code-switching (CS), which is further exacerbated in the case of morphologically rich languages.For the task of machine translation (MT), morphological segmentation has proven successful in alleviating data sparsity in monolingual contexts; however, it has not been investigated for CS settings.In this paper, we study the effectiveness of different segmentation approaches on MT performance, covering morphology-based and frequency-based segmentation techniques.We experiment on MT from code-switched Arabic-English to English.We provide detailed analysis, examining a variety of conditions, such as data size and sentences with different degrees of CS.Empirical results show that morphology-aware segmenters perform the best in segmentation tasks but under-perform in MT.Nevertheless, we find that the choice of the segmentation setup to use for MT is highly dependent on the data size.For extreme low-resource scenarios, a combination of frequency and morphology-based segmentations is shown to perform the best.For more resourced settings, such a combination does not bring significant improvements over the use of frequency-based segmentation.

Visual prostheses have been introduced to partially restore vision to the blind via visual pathway stimulation. Despite their success, some challenges have been reported by the implanted patients. One of those challenges is the difficulty of object recognition due to the low resolution of the images perceived through these devices. In this paper, a deep learning-based approach combined with image pre-processing is proposed to allow visual prostheses' users to recognize objects in a given scene. The approach simplifies the objects in the scene by displaying the objects in clip art form to enhance object recognition. These clip art images are generated by, first, identifying the objects in the scene using the You Only Look Once (YOLO) deep neural network. The clip art corresponding to each identified object is then retrieved via Google Images. Three experiments were conducted to measure the success of the proposed approach using simulated prosthetic vision. Our results reveal a remarkable decrease in the recognition time, increase in the recognition accuracy and confidence level when using the clip art representation as opposed to using the actual images of the objects. These results demonstrate the utility of object simplification in enhancing the perception of images in prosthetic vision.

The introduction of word embedding models has remarkably changed many Natural Language Processing tasks. Word embeddings can automatically capture the semantics of words and other hidden features. Nonetheless, the Arabic language is highly complex, which results in the loss of important information. This paper uses Madamira, an external knowledge source, to generate additional word features. We evaluate the utility of adding these features to conventional word and character embeddings to perform the Named Entity Recognition (NER) task on Modern Standard Arabic (MSA). Our NER model is implemented using Bidirectional Long Short Term Memory and Conditional Random Fields (BiLSTM-CRF). We add morphological and syntactical features to different word embeddings to train the model. The added features improve the performance by different values depending on the used embedding model. The best performance is achieved by using Bert embeddings. Moreover, our best model outperforms the previous systems to the best of our knowledge.

Users have different expectations while interacting with chatbots. However, current chatbots do not consider the users’ preferences and needs, creating unpleasant biases towards certain user groups. User characteristics play a vital role in predicting preferences and thus eliciting better responses as advocated by Character Computing. A framework was proposed in the literature to build character-based chatbots with adjustable traits. In this paper, we customize the framework modules to build two chatbots for different domains using the same set of chatbot characters. First, we present a character-based chatbot for academic support. It provides students with information regarding the content, dates, timings, locations, and deadlines of their courses. Second, we present a character-based chatbot for COVID-19. It provides users with answers to frequently asked questions, advice and precautions, cases per country, and symptoms checking. Users can select their preferable chatbot character to interact with, and receive personalized responses accordingly. Moreover, we investigated the prediction of a user’s favorite chatbot character based on their personality traits to create adaptable chatbots in the future. Finally, we investigated the effect of a chatbot’s character on its likability and trustworthiness.

With the increasing popularity of e-commerce and the coronavirus situation, an increasing number of shoppers are opting for online shopping because of store closures and their fear of contracting the coronavirus in public. While conventional retail provides consumers with a full spectrum of interaction, online shopping has been deficient in these types of experiences. Therefore, the virtual reality technology is used to bridge the gap between the two shopping techniques and create a more natural and intuitive shopping environment. The aim of this research is to investigate how, in a virtual store environment, consumers’ interaction affect their shopping experience. A virtual supermarket with the interaction facility was designed. An experiment was conducted to track the effect of the social interaction on the consumers using different metrics. The results showed that consumers’ social interaction in the form of avatar- mediated communication has a beneficial impact on their social presence. It also demonstrates that consumers felt more immersed and socially engaged to the shopping environment via the social interaction among avatars.

Drivable area segmentation is an important feature for autonomous driving. Currently, state of the art techniques in this area focus on segmenting roads in urban areas with near perfect conditions. Roads with deteriorating conditions have received much less attention, even though they are common and present a unique set of challenges to the road segmentation tasks. These challenges include detecting obstacles (manholes and potholes) and determining whether or not it is safe to drive over them, and detecting road boundaries while lacking proper markings. This paper proposes a new method for drivable area segmentation in roads with deteriorating conditions based on a 3D LiDAR. Our framework represents the LiDAR point cloud data in an angular grid object which splits the data into smaller point cloud objects based on the laser scan number and the projection angle of each point. We apply multiple filtration steps in our framework in order to accurately detect the road boundaries and to detect and classify any road irregularities. The experiments on our collected datasets demonstrate the performance of our framework in detecting and classifying road drivable regions accurately and robustly. We reached a maximum precision of 92.78% in detection road boundaries and a max precision of 99.38% in detection and classifying road irregularities.

This Research to Practice Full Paper presents an application (AutoNotes) that improves the students' learning outcomes by using natural language processing techniques (NLP) such as speech recognition and keywords extraction. AutoNotes is an android application that converts the speech of the instructor to text in real time during the lecture and then it synchronizes this text to the original lecture slides to produce new slides annotated with the explanation of the instructor. These new lecture slides can be displayed in a classroom. Moreover, it can be accessed by the students on their mobile phones. AutoNotes also provides a feature for the students who like to write their notes with their own way. A focus group was conducted to gather target audience opinions. The results have shown that students liked the idea of AutoNotes. They saw it beneficial for them especially if the original lecture slides are not detailed. They also like the idea of putting the notes with its corresponding slide. To evaluate AutoNotes, a between group design setup of two identical lectures with different groups of participants was conducted. In the first lecture, the lecturer used the original lecture slides. However, in the second one the annotated lecture slides was used. The participants were given several tests to compare their overall learning experiences. The results of the tests reveal that the participants that were exposed to the annotated lecture slides had a higher engagement level and learning gain than the other group exposed to the original lecture slides. Additionally, they had a workload level less than the other group. For the system usability test, the average usability scale that AutoNotes got is 78 which can be considered above average. Finally, the results of the accuracy testing reveal that the accuracy of the speech recognition was 83.2%. The precision and recall metrics were used to evaluate the synchronization of the speech with the slides. The average precision score was 75.6%. However, the average recall score was 51.1%.

Ontologies represent the backbone of the semantic web since they represent knowledge in a machine-understandable way. The more complex the ontologies are, the greater is the need for visualization tools to ease the understanding of the large amount of knowledge represented by these ontologies. Visualizing large ontologies is a still under-investigated interesting challenge. In this paper, we present VisCOnto, a visualization tool of complex ontologies by extracting sub-ontologies that correspond to excerpts of a large ontology focusing on some concepts of interest. VisCOnto combined with WEBVOWL can create sub-ontologies to create expressive information visualizations by harnessing the semantic reasoning capabilities provided by ontologies. A proof of concept use case for applying VisCOnto to visualize an existing complex ontology of human behavior and character is presented.

Code-switching has become a prevalent phenomenon across many communities. It poses a challenge to NLP researchers, mainly due to the lack of available data needed for training and testing applications. In this paper, we introduce a new resource: a corpus of Egyptian- Arabic code-switch speech data that is fully tokenized, lemmatized and annotated for part-of-speech tags. Beside the corpus itself, we provide annotation guidelines to address the unique challenges of annotating code-switch data. Another challenge that we address is the fact that Egyptian Arabic orthography and grammar are not standardized.

In this paper, we present our ArzEn corpus, an Egyptian Arabic-English code-switching (CS) spontaneous speech corpus. The corpus is collected through informal interviews with 38 Egyptian bilingual university students and employees held in a soundproof room. A total of 12 hours are recorded, transcribed, validated and sentence segmented. The corpus is mainly designed to be used in Automatic Speech Recognition (ASR) systems, however, it also provides a useful resource for analyzing the CS phenomenon from linguistic, sociological, and psychological perspectives. In this paper, we first discuss the CS phenomenon in Egypt and the factors that gave rise to the current language. We then provide a detailed description on how the corpus was collected, giving an overview on the participants involved. We also present statistics on the CS involved in the corpus, as well as a summary to the effort exerted in the corpus development, in terms of number of hours required for transcription, validation, segmentation and speaker annotation. Finally, we discuss some factors contributing to the complexity of the corpus, as well as Arabic-English CS behaviour that could pose potential challenges to ASR systems.

Programming has become a crucial skill in today’s world. Technology and computers are basically part of anything now. President Obama once referred to the importance of children learning programming: “I do not want our young people to just be consumers, I want them to be producers of this technology and to understand it, to feel like they are controlling it, as opposed to it controlling them.” It is thus an increasing need for an individual to know how to write their own programs. With the increasing dependency on computation, one can anticipate such skills being a requirement for most jobs in the coming years. There is a lot of effort done in this area, however most of them focused on either software or hardware track. The aim of this work is to test if using a hybrid approach for teaching K-12 students programming by combining both software and hardware techniques is more effective than using a singleton approach.

Infographics is a tool for data visualization. It makes data easy to understand and interpret. An infographic is defined as a visual representation for data like a chart or a diagram. Infographics can help in many fields including education. This is due to the fact that information can be easily memorized and understood if was given in a visual form. Infographics are found everywhere and used in many different fields. Facebook timeline is considered as an infographic. On another hand, online journalism is increasingly gaining popularity. It is also considered as a source of big data that is rapidly expanding. Online newspapers and magazines provide a large population with daily important information. The aim of the work is to use data visualization, infographics and Natural Language Processing (NLP) techniques in online journalism. The aim of the work is to automatically visualize the information in an article in the form of infographics.

In the recent years, intelligent vehicles became the most common everyday task that attracts a great interest from the industry. This work is dedicated to enhance the vehicles' intelligence through an adaptable four dimensional model capable of impersonating the vehicle driver by accurately predicting the time and location of destinations. Existing research efforts have addressed this challenge through prediction models that consider one or two aspects of human behavior, such as the social network or location semantics. However, the accuracy of these models remains rather limited. Accordingly, this paper addresses the shortfalls of the existing work by incorporating most aspects of human mobility. Moreover, we study the impact of each of the model dimensions on the prediction accuracy, individually and combined. We further propose an optimization algorithm to calculate the best blend of the model dimensions, that maximizes the prediction accuracy. The performance of the model is evaluated using a dataset that is collected from users in the city of UIm, Germany, as well as through computer simulations. The results show that the model can achieve a prediction accuracy of 95%, outperforming the state-of-the-art counterparts.

Mid-Air haptic devices have become an active area of research because of their potential impact to augmented/virtual reality. In this work, we develop an electromagnetic-based haptic interface to provide controlled magnetic forces on a wearable orthopedic finger splint with a single magnetic dipole. We model the electromagnetic forces exerted on the finger splint, optimize the design of the electromagnetic coils, and develop an impedance-type haptic rendering algorithm using position feedback. This rendering algorithm capitalizes on minimizing the error between the exerted magnetic force and the desired constraint force of a virtual three-dimensional (3D)object based on the position of the finger splint. In order to investigate the influence of incorporating position feedback, we conduct a comparative study for the same group of participants with (Case I)and without (Case II)position feedback. Our experimental results show that position feedback enables participants to achieve success rate of 66.87 ± 15.0% (n=160) in distinguishing between the geometry of four 3D virtual objects. This rate is decreased to 55.15±15.8% (n=160) in the absence of position feedback. Our analysis shows statistical evidence to conclude that the mean success rate for Case I is greater than that of Case II, at α = 0.1 and 90% confidence level.

Computations usually handle excessive amounts of data with different types. The simpler the data is displayed, the faster actions might be performed. With big data, it is important to be able to find out what is really happening through the details of the data. This could be done with visualization through flexible displaying of data with efficient forms using advanced visualization technologies.

This paper discusses a rule-based approach for visualizing proofs using tableaux techniques. Semantic Tableau is usually used to prove by refutation. In addition, tableaux techniques are commonly studied in different courses. Visualization is an effective teaching methodology. The availability of visualization methods that could be used by instructors is thus important.

The aim of the tool provided in the paper is to provide a virtual reality enabled platform for teaching programming. The focus is on the concepts of objects and classes. The platform allows users to customize and see almost everything to see their effects.

Ubiquitous computing is the growing trend of establishing a place for technology in the background, rather than the foreground, of our lives through embedding computational capability into everyday objects to make them effectively communicate and perform useful tasks. Hence, the goal of pervasive computing is to make devices "smart". Standing on this ground, the model subject of this work enhances the vehicles intelligence through enabling them to accurately predict the drivers destination without their intervention through an optimized four dimensional destination prediction model. Moreover, it takes the lead in leveraging the semantic properties of both the locations of interest and the social relations affecting the visiting activities of the drivers. In line with this, our proposed model simultaneously utilizes an optimization algorithm to blend the different dimensions and to accommodate the evolving human mobility behavior. The experimental results demonstrate that the optimized model significantly outperforms the basic model achieving an average prediction accuracy of 95% when compared to the real trace. Show the XML Only

Now that most daily endeavors have migrated to computers and the Internet, having systems that adapt to users is a rising trend. By detecting and adapting to the user's traits and character instead of only the current states, there is great potential for novel user experiences. Character Computing which was introduced in [3] is now extended to the field of experimental psychology and psychological theorizing. Our approach integrates psychological and computational aspects of advanced sensing and processing technologies to detect the building blocks of a user's character traits and states which will allow computer systems to adapt to the user's behavior and his/her feelings more reliably and mimic them according to situational demands. Accordingly, in this workshop the different aspects of Character Computing are to be discussed from an interdisciplinary perspective taking computational and psychological accounts into consideration in order to strengthen already investigated and potential applications as well as the theoretical foundation and challenges of Character Computing.

With the expansion of multicultural communities, raising awareness to diminish cultural misconceptions became a must. We investigate different research probes using interactive Virtual Reality (VR) applications to increase such awareness. VR environments provide a high sense of immersion, which enhance the user's experience. In this work, we present three VR interactive research probes that allow users to understand cultural differences and to reduce cultural misconceptions.

Most of the current adaptive systems support single task activities. The rise in the number of daily interactive devices and sources of information made multitasking an integral activity in our daily life. Affect-aware systems show exciting potential to support the user, however, they focus on the induced effect of an additional task in terms of cognitive load and stress, rather than the influence of the number of tasks i.e. multitasking. This paper presents indicators of the number of tasks being performed by the user using a set of bio-sensors. A preliminary user study was conducted with two follow-up explorations. Our findings imply that we can distinguish between the number of tasks performed based on high-end as well as cheap Heart Rate sensors. Additionally, tasks number correlates with other signals, namely wrist and forehead temperature. We provide empirical evidence showing how to differentiate between single- and dual-tasking activities.

Adaptive educational games have shown potential in enhancing the learning experience in educational setups. Most of the conducted research used the content and level of difficulty as the adaptive aspect. However, in this work we aim to evaluate the effect of changing the game interface including; background music and color as well as dynamics represented by the timer and scoring technique. We built adaptive educational games using subjective measures, where the adaptive features are manipulated based on reported emotions by the participants. We conducted a between subject study with 30 participants using adaptive and non-adaptive version of the game to assess the effectiveness of the proposed adaptive aspects on the learning experience, namely, learning gain and the engagement level. Our findings showed a significant increase in the learning gain and the engagement level of the participants who used the adaptive version of the system over the other. Finally, we discuss the implication of our findings and potential added value of our findings.

In this study, we develop an electromagnetic-based haptic interface to provide controlled magnetic forces to the operator through a wearable haptic device (an orthopedic finger splint with single dipole moment) without position feedback. First, we model the electromagnetic forces exerted on a single magnetic dipole attached to the wearable haptic device, and derive magnetic force-current mapping for the dipole moment. Second, this mapping is used as basis for parameter selection of the electromagnetic coils of the haptic interface, dipole moment of the wearable haptic device, and the operating workspace of the system. The electromagnetic-based haptic interface enables three-dimensional (3D) virtual object rendering in mid-air within a workspace of 150 mm × 150 mm × 20 mm, using magnetic forces in excess of 50 mN. Participants experimentally demonstrate a 61% success rate in distinguishing the geometry of 4 representative 3D virtual objects. However, our statistical analysis shows that the ability of the participants to distinguish between geometries is not statistically significant, for 95% confidence level.

The paper introduces an approach that allows for detecting interaction with a graphical display in a rule-based declarative approach. It also introduces the possibility of connecting the interaction to a specific animation step to be performed. The two approaches were embedded into an animation system to aid in teaching algorithms.

Trees and graphs are two data structures that are commonly used in representing different kinds of data. They also have many associated algorithms taught in different courses. It is thus beneficial to have a tool that could be used by students, teachers and programmers to visually trace how their algorithms work. The work in this paper presents, CHR-Graph, an easy-to-use platform for animating trees and graphs and their correlated algorithms using Constraint Handling Rules (CHR).

Most of the typical digital systems are not fully aware of the users' affect states. Adapting to the users' state showed great potential for enhancing user experiences. However, most approaches for sensing affective states, specifically arousal and valence, involve expensive and obtrusive technologies, such as physiological sensors attached to users' bodies. This paper present an indicator of the users' affect based on eye tracking. We use a commercial eye tracker to monitor the user's pupil size to estimate their arousal and valence in response to videos of different content. To assess the effect of different content (namely pleasant and unpleasant) influencing the arousal and valence on the pupil diameter, we conducted a user study with 25 participants. The study showed that different content of videos affect the pupil diameter, thereby giving an indicator about the user's state. We provide empirical evidence showing how to unobtrusively detect changes in users' state. Our initial investigation gives rise to eye-based user's tracking, which introduces the potential of new applications in the field of affect-aware computing.

One of the challenging problems in Artificial Intelligence and Multi-Agent systems is the RoboCup Rescue project that was established in 2001. The Rescue Simulation provides a broad test bench for many algorithms and approaches in the field of AI. The Simulation presents three types of agents: police agents, firebrigade agents and ambulance agents. Each of them has a crucial role in the rescuing problem. The work presented in this paper focuses on the task planning of the ambulance team whose main role is rescuing the maximum number of civilians. It is obvious that this target is a complicated one due to the number of problems that the agent is faced with. One of the problems is estimating the time each civilian takes to die; the Estimated Time of Death (ETD). Realistic estimations of the ETD will lead to a better performance of the ambulance agents by planning their tasks accordingly. Supervised learning is our approach to learn and predict the ETD civilians leading to an optimized planning of the agents tasks.

Unobtrusive and mobile activity monitoring using ubiquitous, cheap and widely available technology is the key requirement for human activity recognition supporting novel applications, such as health monitoring. With the recent progress in wearable technology, pervasive sensing and computing has become feasible. However, recognizing complex activities on light-weight devices is a challenging task. In this work, a platform to combine off-the-shelf sensors of smartphones and smartwatches for recognizing human activities in real-time is proposed. In order to achieve the best tradeoff between the system’s computational complexity and recognition accuracy, several evaluations were carried out to determine which classification algorithm and features to be used. Therefore, a data set from 16 participants was collected that includes normal daily activities and several fitness exercises. The analysis results showed that naive Bayes performs best in our experiment in both the accuracy and efficiency of classification, while the overall classification accuracy is 87% ± 2.4.

Over the past years, visualization of programs has been widely applied. Algorithm animation was proven to aid in teaching and learning. It provides a convenient medium for beginners to a programming language by giving them the ability to visually discover how their programs are running. It also provides experts of a language with a means to have a visual trace utility. Lately, a new approach for adding visualization features into Constraint Handling Rules (CHR) programs was proposed. The new methodology was a dynamic one able to animate different types of algorithms. The work in this paper aims at introducing a revised extension that is able to embed visualization features into Java programs. With the new extension, Java algorithms could be animated without the need of doing any modifications to the code. In addition, the provided technique is still a general one able to animate different kinds of algorithms.

Simulation is a key feature in validating the protocols dedicated for VANET applications. Since the vehicles mobility is driven by the human mobility characteristics and is controlled by the geographical restrictions of the roads, the work presented in this paper is aimed to having a realistic mobility model that incorporates both the social aspects of human mobility together with the geographical restrictions that governs the movement of the mobile nodes. This mobility model is dedicated for testing the protocols related to the delay tolerant applications where unicasting/multicasting is used for data dissemination rather than broadcasting. Thus location awareness and destination prediction is essential. The model is based on using realistic data sets rather than randomly generated data. Evaluating the technique using the SUMO simulator have proven a range of 80% to 90% of match between the predicted traces and real life data available for evaluation.

Recently, a new approach for embedding visualization features into Constraint Handling Rules (CHR) programs has been proposed. It allows CHR programmers to animate and visualize different algorithms implemented in CHR. Such features have become essential with CHR being a general purpose language. In this paper, a new diagrammatic representation for CHR programs is presented. The representation is also able to account for the newly embedded visual features.

There has been a recent increasing demand to digitize Arabic books and documents, due to the fact that digital books do not lose quality over time, and can be easily sustained. Meanwhile, the number of Arabic-speaking Internet users is increasing. We propose AreCAPTCHA, a system that digitizes Arabic text by outsourcing it to native Arabic speakers, while offering protective measures to online web forms of Arabic websites. As users interact with AreCAPTCHA, we collect possible digitizations of words that were not recognized by OCR programs. We explain how the system works, the challenges we faced, and promising preliminary evaluation results.

We propose the Arabic Chat Alphabet (ACA) as naturally written in everyday life for dialectal Arabic speech transcription. Our assumption is that ACA is a natural language that includes short vowels that are missing in traditional Arabic orthography. Furthermore, ACA transcriptions can be rapidly prepared. Egyptian Colloquial Arabic was chosen as a typical dialect. Two speech recognition baselines were built: phonemic and graphemic. Original transcriptions were re-written in ACA by different transcribers. Ambiguous ACA sequences were handled by automatically generating all possible variants. ACA variations across transcribers were modeled by phonemes normalization and merging. Results show that the ACA-based approach outperforms the graphemic baseline while it performs as accurate as the phoneme-based baseline with a slight increase in WER.

Amajor problem with dialectal Arabic acoustic modeling is due to the very sparse available speech resources. In this paper, we have chosen Egyptian Colloquial Arabic (ECA) as a typical di-alect. In order to benefit from existing Modern Standard Arabic (MSA) resources, a cross-lingual acoustic modeling approach is proposed that is based on supervised model adaptation. MSA acoustic models were adapted using MLLR and MAP with an in-house collected ECA corpus. Phoneme-based and grapheme-based acoustic modeling were investigated. To make phoneme-based adaptation feasible, we have normalized the phoneme sets of MSA and ECA. Since dialectal Arabic is mainly spo-ken, graphemic form usually does not match actual spelling as in MSA, a graphemic MSA acoustic model was used to force align and to choose the correct ECA spelling from a set of au-tomatically generated spelling variants lexicon. Results show that the adapted MSA acoustic models outperformed acoustic models trained with only ECA data.

Corpus annotation is an important aspect in speech applications where stochastic models need to be trained and evaluated. Multimodal corpora are also annotated. Moreover, corpus annotation is an essential phase in the construction of emotion recognizer engines. Large corpora, as they are essential to construct representative knowledge bases, have been a problem for corpus annotators. Time consumed for labeling such corpora is very significant. Furthermore, manageability becomes more arduous and tedious. In this paper, we propose a semi-automatic tool, called BECAM tool, that will help corpus annotators in managing and annotating large sample emotion corpora. Index Terms: Corpus annotation, emotion recognition, bootstrap 1.

During the last decade, Constraint Handling Rules (CHR) have become a major specification and implementation language for logical, constraint-based algorithms and intelligent applications (as witnessed for example by several hundred publications available online that mention CHR). Algorithms are often specified using inference rules, rewrite rules, sequents, proof rules, or logical axioms that can be almost directly written in CHR.

In this paper, we present a framework for automatic generation of CHR solvers given the logical specification of the constraints. This approach takes advantage of the power of tabled resolution for constraint logic programming, in order to check the validity of the rules. Compared to previous work (Apt and Monfroy 1999; Ringeissen and Monfroy 2000; Abdennadher and Rigotti 2000; Abdennadher and Rigotti 2001a), where different methods for automatic generation of constraint solvers have been proposed, our approach enables the generation of more expressive rules (even recursive and splitting rules) that can be used directly as CHR solvers.

A general approach to implement propagation and simplification of constraints consists of applying rules over these constraints. However, a difficulty that arises frequently when writing a constraint solver is to determine the constraint propagation algorithm. In this article, we propose a method for generating propagation and simplification rules for constraints over finite domains defined extensionally by, for example, a truth table or their tuples. The generation of rules is performed in two steps. First, propagation rules are generated. Propagation rules do not rewrite constraints but add new ones. Thus, the constraint store may contain superfluous constraints. Removing these constraints not only allows saving of space but also decreases the cost of constraint solving. Constraints can be removed using simplification rules. Thus, in a second step, some propagation rules are transformed into simplification rules.Furthermore, we show that our approach performs well on various examples, including Boolean constraints, multivalued logic, and Allen's qualitative approach to temporal logic. Moreover, an application taken from the field of digital circuit design shows that our approach is of practical use.

A general approach to implement propagation and simplification of constraints consists of applying rules over these constraints. However, a difficulty that arises frequently when writing a constraint solver is to determine the constraint propagation algorithm. In previous work, different methods for automatic generation of propagation rules for constraints defined over finite domains have been proposed. We present a method for generating propagation rules for constraint predicates defined by means of a constraint logic program.

A general approach to implement propagation and simplification of constraints consists of applying rules over these constraints. Recently, several proposals have been made to solve finite constraint satisfaction problems by means of automatically generated propagation rules [4, 9, 3]. Since propagation rules do not rewrite constraints but add new ones, the constraint store may contain superfluous constraints. Removing these constraints not only allows saving of space but also decreases the cost of constraint solving. Constraints can be removed using simplification rules. In this paper, we present a method to transform propagation rules into simplification rules using the concept of confluence. The generated rules are implemented in the language Constraint Handling Rules (CHR). An example tak en from the field of digital circuit design shows that our approach is of practical use.

Most cities in Germany regularly publish a booklet called the Mietspiegel. It basically contains a verbal description of an expert system. It allows the calculation of the estimated fair rent for a flat. By hand, one may need a weekend to do this task. With our computerized version, the Munich Rent Advisor, the user just fills in a form in a few minutes, and the rent is calculated immediately. We also extended the functionality and applicability of the Mietspiegel so that the user need not answer all questions on the form. The key to computing with partial information using high-level programming was to use constraint logic programming. We rely on the Internet, and more specifically the World Wide Web, to provide this service to a broad user group, the citizens of Munich and the people who are planning to move to Munich. To process the answers from the questionnaire and return its result, we wrote a small simple stable special-purpose web server directly in ECLiPSe. More than 10,000 people have used our service in the last three years. This article describes the experiences in implementing and using the Munich Rent Advisor. Our results suggest that logic programming with constraints can be an important ingredient in intelligent internet systems.

Integrity constraint and abduction are important in query-answering systems for enhanced query processing and for expressing knowledge in databases. A straightforward characterization of the two is given in a subset of the language CHRv, originally intended for writing constraint solvers to be applied for CLP languages. This subset has a strikingly simple computational model that can be executed using existing, Prolog-based technology. Together with earlier results, this confirms CHRv as a multiparadigm platform for experimenting with combinations of top-down and bottom-up evaluation, disjunctive databases and, as shown here, integrity constraint and abduction