Search

Previous studies have shown that the cholinergic nucleus basalis of Meynert and its white matter projections are affected in Alzheimer’s disease dementia and mild cognitive impairment. However, it is still unknown whether these alterations can be found in individuals with subjective cognitive decline, and whether they are more pronounced than changes found in conventional brain volumetric measurements. To address these questions, we investigated microstructural alterations of two major cholinergic pathways in individuals along the Alzheimer’s disease continuum using an in vivo model of the human cholinergic system based on neuroimaging. We included 402 participants (52 Alzheimer’s disease, 66 mild cognitive impairment, 172 subjective cognitive decline and 112 healthy controls) from the Deutsches Zentrum für Neurodegenerative Erkrankungen Longitudinal Cognitive Impairment and Dementia Study. We modelled the cholinergic white matter pathways with an enhanced diffusion neuroimaging pipeline that included probabilistic fibre-tracking methods and prior anatomical knowledge. The integrity of the cholinergic white matter pathways was compared between stages of the Alzheimer’s disease continuum, in the whole cohort and in a CSF amyloid-beta stratified subsample. The discriminative power of the integrity of the pathways was compared to the conventional volumetric measures of hippocampus and nucleus basalis of Meynert, using a receiver operating characteristics analysis. A multivariate model was used to investigate the role of these pathways in relation to cognitive performance. We found that the integrity of the cholinergic white matter pathways was significantly reduced in all stages of the Alzheimer’s disease continuum, including individuals with subjective cognitive decline. The differences involved posterior cholinergic white matter in the subjective cognitive decline stage and extended to anterior frontal white matter in mild cognitive impairment and Alzheimer’s disease dementia stages. Both cholinergic pathways and conventional volumetric measures showed higher predictive power in the more advanced stages of the disease, i.e. mild cognitive impairment and Alzheimer’s disease dementia. In contrast, the integrity of cholinergic pathways was more informative in distinguishing subjective cognitive decline from healthy controls, as compared with the volumetric measures. The multivariate model revealed a moderate contribution of the cholinergic white matter pathways but not of volumetric measures towards memory tests in the subjective cognitive decline and mild cognitive impairment stages. In conclusion, we demonstrated that cholinergic white matter pathways are altered already in subjective cognitive decline individuals, preceding the more widespread alterations found in mild cognitive impairment and Alzheimer’s disease. The integrity of the cholinergic pathways identified the early stages of Alzheimer’s disease better than conventional volumetric measures such as hippocampal volume or volume of cholinergic nucleus basalis of Meynert.

Nowadays, longevity studies have become a distinguished multidisciplinary field merging with cutting-edge computer science technologies to outline innovative ideas to cater to the needs of seniors. Since the global geriatric population is anticipated to rise, the number of people seeking to obtain caregiving services and wishing to be more actively engaged in life will be more apparent. Therefore, seniors aspiring day-to-day special care in home settings, interested in improving their living standards can likewise benefit from an amiable companion. A comprehensive search strategy was developed and selected databases were looked through with relevant keywords. This scoping review qualitatively examines the literature on the use of companion robots, including both pet-like and humanoid robots, and Internet-of-Things (IoT) security, coupled with the new 5G technology for the home-based elder care. From 355 full-text articles that were found, 90 articles were selected to be investigated respectively. In order to ascertain their operation in the future, we discuss remaining challenges, unused opportunities, security risks and suggested remedies and suggest a dementia-centred concept and an implementation framework.

This study is a presentation of our ongoing process of obtaining body composition and electrical parameters from sufficient cohort of normal healthy children in Czech Republic that are currently not known. At the moment, 37 children have been fully measured and their parameters evaluated. The measured group consisted of 17 boys and 20 girls and their age was 5 to 6 years. Results are showing that hydration status and electrical parameters of children are approximately the same in the time of leave and in the time of arrival kindergarten. However, changes that are reported through all measurements indicate some specific behavior of bioelectrical values during children day cycle. Bioimpedance seems to act as a possible marker, that can describe in future body composition in another perspective than classical body composition parameters. Further work is needed to obtain sufficient cohort of normal healthy children and to be able to estimate new electrical parameters that may be used as reliable predictors of a health status of the child without using empirical equations or BMI.

This paper developes a novel telemedicine solution for rehabilitation of balance disorders based on analysis and visualization of patient physiological data gathered by a set of sensors during the rehabilitation process. The training and physiological data sensing are based on two home-grown systems called Homebalance and Scope. The kernel functionality is implemented by the data stream management technology provided by the Esper system. Moreover, it supports training at homes integrated together and to specialized rehabilitation centers by the Dew/Cloud technology. A small core of early adopters is currently successfully conducting balance disorder rehabilitation according to methodology relying on the proposed approach.

Our presented idea is to integrate artificial neural network (probably of BICA type) with a real biological network (ideally in the future with the human brain) in order to extend or enhance cognitive- and sensory- capabilities (e.g. by associating existing and artificial sensory inputs). We propose to design such neuro-module using Hierarchical Temporal Memory (HTM) which is a biologically-inspired model of the mammalian neocortex. A complex task of contextual anomaly detection was chosen as our case-study, where we evaluate capabilities of a HTM module on a specifically designed synthetic dataset and propose improvements to the anomaly model. HTM is framed within other common AI/ML approaches and we conclude that HTM is a plausible and useful model for designing a direct brain-extension module and draft a design of a neuromorphic interface for processing asynchronous inputs. Outcome of this study is the practical evaluation of HTM's capabilities on the designed synthetic anomaly dataset, a review of problems of the HTM theory and the current implemen- tation, extended with suggested interesting research direction for the future.

Brain disorders occur when our brain is damaged or negatively influenced by injury, surgery, or health conditions. This chapter shows how the combination of novel biofeedback-based treatments producing large data sets with Big Data and Cloud-Dew Computing paradigms can contribute to the greater good of patients in the context of rehabilitation of balance disorders, a significant category of brain damage impairments. The underlying hypothesis of the presented original research approach is that detailed monitoring and continuous analysis of patient´s physiological data integrated with data captured from other sources helps to optimize the therapy w.r.t. the current needs of the patient, improves the efficiency of the therapeutic process, and prevents patient overstressing during the therapy. In the proposed application model, training built upon two systems, Homebalance—a system enabling balance training and Scope—a system collecting physiological data, is provided both in collaborating rehabilitation centers and at patient homes. The preliminary results are documented using a case study confirming that the approach offers a viable way towards the greater good of a patient.

Seizure semiology has always been an important part of seizure classification. Value of the most common ictal signs for localization and lateralization of a seizure focus, as well as their sensitivity and specificity for certain focal epilepsies, is well known. All over it, there still remain many signs and poorly described patient behaviours during a seizure whose relation to a seizure focus have yet to be specified and confirmed. Some new signs have been introduced recently but all of them have been based on data provided from just a few dozens of patients. This is no surprise since checking for presence of a specific ictal sign in a patient requires lengthy manual review of video records documenting his/her seizures. We suggest a novel approach toward identification/verification of new ictal signs based on computer supported systematic review of unique extensive dataset of Na Homolce Hospital containing approximately 1.000 seizures (representing data of 400 patients with up to 5 seizures annotated). This requires transforming the original set of patient records into a database consisting of annotated ictal video-EEG recordings in a structured form suitable for statistical analysis as well as for analysis of sequence patterns. This contribution describes our SW tool ASTEP designed and developed for this purpose and demonstrates some properties of ASTEP database, namely advantages of the used seizure description as a sequence of considered ictal signs complemented by detailed information on timing, duration, repetition and mode of appearance of these signs. Finally, some preliminary results are reported.

We examine the spatial structure of the melanoma in tissue sections. The pigs melanoma was examined in 10 tissue samples obtained from animals of age between 4 and 22 weeks. The tissue sections were measured by light microscopy and Laser Ablation Ion Inductively Coupled Plasma Mass Spectrometry to obtain spatial metal (Cu, Zn) distribution. The exploratory analysis of the tissue sections indicates there is clearly a pattern in the spatial structure. Different projections of the spatial structure of the melanoma are obtained by the different measurement methods. The spectral clustering on the data was utilized to describe the structure in the data. According to the clustering results, there are distinct clusters of observations in the histological data. The spatial elemental distribution of the metals Cu and Zn cannot be clustered—the data form one compact cluster. The clustering of the histological images produces clusters which are related to the annotation of the biological samples in broader terms—the differences between fibrous and cancerous tissue.

Brain disorders include any conditions or disabilities that affect the brain. Some frequently occurring brain disorders (caused by e.g., injury or stroke) are associated with balance disorders that can be to some extent compensated by highly personalized and long term rehabilitation. This paper presents basic principles and reports the first results of our project focused on design and development of new generation data-centric approach to treatment of balance disorders through exergames offering many levels of difficulties that can be adjusted through choice of their parameters. The suggested approach paves the way to future precision rehabilitation models, in which the significant part of the training can be ensured through game like exercise supervised by an intelligent adaptive system called Intelligend Therapy Assistant (ITA) providing the patient with biofeedback. Further, the supervising ITA iteratively modifies parameters of the next run of the game (adapts them) according to the current needs of the individual patient in order to keep the patient motivated — this is ensured by introduction of a feedback loop between the ITA system and thorough evaluation of results achieved by the patient during the therapy process.

We describe a new procedure for the parallel mapping of selected metals in histologically characterized tissue samples. Mapping is achieved via image registration of digital data obtained from two neighbouring cryosections by scanning the first as a histological sample and subjecting the second to laser ablation inductively coupled plasma mass spectrometry. This computer supported procedure enables determination of the distribution and content of metals of interest directly in the chosen histological zones and represents a substantial improvement over the standard approach, which determines these values in tissue homogenates or whole tissue sections. The potential of the described procedure was demonstrated in a pilot study that analysed Zn and Cu levels in successive development stages of pig melanoma tissue using MeLiM (Melanoma-bearing-Libechov-Minipig) model. We anticipate that the procedure could be useful for a complex understanding of the role that the spatial distribution of metals plays within tissues affected by pathological states including cancer.

Papillomaviruses, small non-enveloped DNA viruses, are considered as the cause of a number of cancers, such as a cervical, skin, anal, and penile cancers. Roe deer papillomavirus infection can be easily characterized by typical warts on the skin. The aim of this study was to characterize differences of amino acid and protein composition in healthy and tumour tissue of roe deer using methods including ion-exchange chromatography, SDS-PAGE, two-dimensional gel electrophoresis and MALDI-TOF MS. The obtained data were statistically evaluated. Cluster analysis of all variables showed biggest differences in proline in control and papilloma samples. Further, we aimed at electrophoretic analysis of the samples. Discriminant analysis projections to latent structures (Projection to the latent structure Discrimination analysis, syn. Discrimination Partial least squares analysis, PLS DA) was used to evaluate the obtained results. Examining model PLS DA, there was found that the greatest effect on whether the individual is classified as healthy, or with papilloma, is the latent variable that best correlates with the molecular mass from 108.8 to 128.1 kDa and from 231.2 to 290.4 kDa in the original signal. Finally, using mass spectrometry three proteins, pyridoxal kinase, myoblast determination protein and leucine zipper transcription factor-like protein 1, most likely related to cancer were found.

The Brdicka curve proved to be a good source of biomarkers, especially of cancer, due to its relation to the concentration of metalothionein protein. Various approaches for the automatic processing of the Brdicka curve were proposed and showed promising results. The Brdicka curve was examined from various points of view, but there are still missing pieces to the whole image of the multi-stage process of the Brdicka curve utilization. This paper presents a first attempt to the understanding of the content of Brdicka curve by the means of regression modeling. A radial basis function model was used to fit the Brdicka curve. This model proved to be complex enough to precisely fit the Brdicka curve. Unfortunately, in a follow-up experiment designed to validate the appropriateness of the model, the radial basis function model was found not to model the phenomena taking place during the Brdicka curve measurement.

Presented exploratory pilot study was aimed at evaluation of proteins present in urinary specimens collected from prostate cancer suffering subjects after radical prostatectomy, divided into two experimental cohorts: positive (n=15) and negative (n=15) surgical margins (PSM/NSM). The presence of PSM suggests inadequate cancer clearance and the possible need for additional treatment. Proper identification of these risk-patients is therefore of a paramount importance. Total protein profiles were firstly identified by using SDS-PAGE and compared by using partial least square discrimination analysis (PLS-DA), which revealed differences in molecular weights of 80-99 and 150-235 kDa between the experimental groups. For further identification of proteins, comparative proteomic technologies were employed. Two-dimensional gel electrophoresis with subsequent identification of protein spots by using MALDI-TOF mass fingerprinting revealed differential expression of proteins between NSM/PSM cohorts. Moreover, in PSM group, three uniquely identified proteins (cyclin-dependent kinase 6, galectin-3-binding protein and L-lactate dehydrogenase C chain) were found, which show tight connection with prostate cancer and presence of all of them was previously linked to certain aspects of prostate cancer. These proteins may be associated with the molecular mechanisms of prostate cancer development; hence, their identification may be helpful for the assessment of disease progression risk after radical prostatectomy, but also for possible early diagnosis.

This paper discusses the possibility of utilizing a multiagent system as a platform for development of a smart adaptive system in medicine. A knowledge-based model of the agents’ mutual awareness (social knowledge) is presented. The tri-base acquaintance (3bA) model formalizing the agent’s social behaviour knowledge and co-operation neighbourhood knowledge is enhanced to support attention focussing and to ensure a holistic complex perception of the problem. The paper focuses on an analysis of the content of 3bA knowledge bases for applications in medical decision support. An architecture for a multiagent system, ADAM, for medical decision support and monitoring is proposed.

The paper presents briefly our educational activities in the area of assistive technologies. We have developed a special course on assistive technologies that uses in practical part project-based approach. We have established a specialized facility where the students can work on their projects. The facility simulates real environment of a smart home and a hospital room. The students can select a topic for a project from our proposals or they can come with their own ideas. In the paper we present results of several student projects that can be in the future used as practical applications for people with special needs. Some of them can be additionally used as games both for children and adults.

The so-called ''tribase'' acquaintance model of the agent's behavior is presented in this paper. This represents an extension of the twin-base model (Cao et al., 1997). Based on practical experience, the new model tries to cope with parallel processing, precedence constraints, and sparse resources. The idea of substituting the interagent negotiation processes by the periodical internal planning activity of the agents is stressed. A multiagent system, ProPlanT, as an application of the tribase model for the project-oriented production planning developed for TESLA TV company is described in detail. Three types of agents production planning agent (PPA), production management agent (PMA), and production agent (PA) are distinguished. The corresponding tribase models and potential role of metaagents are discussed.

For sometime now agent-based and multi-agent systems (MASs) have attracted the interest of researchers far beyond traditional computer science and artificial intelligence (AI). In this article we try to identify focal points of interest for researchers working in the area of distributed AI (DAI) and MAS as well as application-oriented researchers coming from related disciplines, e.g. electrical and mechanical engineering. We do this by presenting key research topics in DAI and MAS research and by identifying application domains in which the DAI and MAS technologies are most suitable. The research topics we discuss are separated into agent architectures and organisations, negotiation among agents, and self-adaptation of MAS using learning techniques. Regarding the application domains for these techniques we distinguish the application domains according to whether the agents control a physical or virtual body (Gestalt) or not. This separation of the application domains is not strict; it represents two ends of a continuum. On the one end of this continuum we have autonomous robot systems which act in a physical environment (sometimes referred to as hardware agents), and on the other end, we have abstract environments, such as in workflow systems, which rarely display the geometrical and physical aspects of the environment we are used to living in.

In this paper we would like to suggest a modification of data vases visualization that can help understanding complex data where each subject is described by a vector of synchronized signals taken in the same time intervals. The advantages of our new approach were verified when visualizing set of Brdicka curves and answering an interesting question raised in proteomics about the Brdicka curves proportionality.

The first part of this chapter reviews the design, implementation, and customer experience with the OLDES SW tele-care platform developed within the EU project Older people's e-services at home. The OLDES solution has been successfully tested at two different locations: in Italy with the participation of a group of 100 seniors (including 10 senior citizens suffering from heart disease), and in the Czech Republic, with the involvement of a group of 10 diabetic patients. The suggested OLDES approach proved to be an effective solution for municipalities, hospitals, and their contact centres for providing health and social services. The project partners therefore decided to develop a second generation of the system called SPES (Support to Patients through E-Service Solutions), which started in April 2011. The SPES project aims at transferring the original approach and results achieved in implementing the OLDES focusing on new target problem domains: dementia, mobility-challenged persons, respiratory problems, and social exclusion. © 2013, IGI Global.

In this study, we determined serum levels of metallothioneins (MTs) and zinc in children with solid tumours (neuroblastoma, Hodgkin lymphoma, medulloblastoma, osteosarcoma, Ewing sarcoma and nephroblastoma) by differential pulse voltammetry Brdicka reaction and ELISA. Zn(II) level in patients sera was 40% compared to controls, contrariwise, MT level was 4.2 × higher in patients. No significant differences among single diagnoses were found both for Zn(II) and MT. When determined Zn(II)/MT ratio, in controls its value was 24.6, but it was 2.6 in patients. After Western-blotting with anti-MT and anti-Zn chicken antibodies, variable intensities of the bands within the samples were observed. The brightness curve obtained for each sample both for MT- and Zn blots was further analysed to produce a list of band positions together with some complementary information related to the intensity of the observed bands by the optimised algorithm. We constructed from those profiles decision trees that enable to distinguish different groups of tumours. The blood samples were heat-treated, in which we supposed mainly MT, but samples contained other thermostable Zn-containing proteins that were helpful for identification of embryonal tumours with 88% accuracy and for identification of sarcomas with 78% accuracy. In MT blots the accuracies were 53 and 45%, respectively. Simultaneous analysis of MT and Zn blots did not increased accuracy of identification neither in embryonal tumours (80%) nor in sarcomas. Those results are promising not only from diagnostic point of view but particularly in the area of studying of individual MT isoforms and their aggregates in malignant tumours.

BACKGROUND: Proteomics and metalloproteomics are rapidly developing interdisciplinary fields providing enormous amounts of data to be classified, evaluated and interpreted. Approaches offered by bioinformatics and also by biostatistical data analysis and treatment are therefore of extreme interest. Numerous methods are now available as commercial or open source tools for data processing and modelling ready to support the analysis of various datasets. The analysis of scientific data remains a big challenge, because each new task sets its specific requirements and constraints that call for the design of a targeted data pre-processing approach. METHODOLOGY/PRINCIPAL FINDINGS: This study proposes a mathematical approach for evaluating and classifying datasets obtained by electrochemical analysis of metallothionein in rat 9 tissues (brain, heart, kidney, eye, spleen, gonad, blood, liver and femoral muscle). Tissue extracts were heated and then analysed using the differential pulse voltammetry Brdicka reaction. The voltammograms were subsequently processed. Classification models were designed making separate use of two groups of attributes, namely attributes describing local extremes, and derived attributes resulting from the level=5 wavelet transform. CONCLUSIONS/SIGNIFICANCE: On the basis of our results, we were able to construct a decision tree that makes it possible to distinguish among electrochemical analysis data resulting from measurements of all the considered tissues. In other words, we found a way to classify an unknown rat tissue based on electrochemical analysis of the metallothionein in this tissue.

This paper is devoted to analysis of voltammograms resulting from Brdicka reaction - the graphs that are currently used for determination of content of metallothioneins (MT) in tissue samples most often. We describe our search for typical patterns in the considered curves that would make it possible to distinguish among voltammograms produced by samples taken from different body parts. We suggest a rather compact representation of information contained in the considered graphs that is based on Haar's Simple Wavelet transformation. The resulting representation is successfully tested for classification of real data obtained from 8 rats and their 9 body parts. The preliminary experiments confirm that the suggested derived attributes of Brdicka curves seem to be good candidates for becoming numerical biomarkers exhibiting an important advantage: the process leading to their calculation can be fully automated.

EU project OLDES (Older People’s e-services at home) develops easy to use and low cost ICT platform in order to offer a better quality of life to elderly people directly in their homes through innovative systems of tele-accompany, tele-assistance and tele-medicine. The elderly are able to access the services and send relevant medical data from their home by being connected to the central server via a low cost PC which is based on Negroponte paradigm. The OLDES platform interface uses television screens controlled through a remote control customized for the elderly. The feasibility of OLDES project is evaluated by the pilot study concentrating on compensation of diabetic patients. Compensation of diabetes is achieved by monitoring glucose glycemia level, blood pressure and weight. Moreover, the patient feeds into OLDES system daily consumption of food using interactive food scales and obtains advice if necessary.

Chinese and Western medicines have a different understanding and approach to life, health, and illness - joining their complementary work and support them by an advanced information technology could result in an improved health system. The Non-Invasive Blood Glucose Measurement (NIGM) Service is a grid based implementation of a novel non-invasive method for measuring human blood glucose values exploiting Chinese meridian theory. In this paper, we describe the implementation of the NIGM service in detail, present an initial performance evaluation and discuss an extension towards other non-invasive long term diabetic relevant measurement. Additionally, the adaption of the ontology-based Medical records Annotation Tool (MedAT) framework towards usage in NIGM trails is elaborated.

Agent's meta-reasoning is a computational process that implements agent's capability to reason on a higher level about another agents or a community ofagen ts. There is a potential for meta-reasoning in multi-agent systems. Meta-reasoning can be used for reconstructing agents' private knowledge, their mental states and for prediction of their future courses of action. Meta-agents should have the capability to reason about incomplete or imprecise information. Unlike the ordinary agents, the meta-agent may contemplate about the community of agents as a whole and is expected to contribute to agent's operation efficiency improvement. This contribution suggests a theoretical specification of an abstract reasoning and knowledge representation architecture for the meta-reasoning agents and discusses/categorizes the related computational processes.

The paper discusses what the research community in the field of information technologies can do to improve quality of life by offering more alternatives both to individuals and to the society. Moreover, attention is given to the present IT tools which can be used to support decision making and choice among possible alternatives in complex settings. The role of ITS in achieving this goal is explained and illustrated by examples in different fields, including environmental decision-making and health care. Enhanced possibilities of choice in collaborative settings supported by new media and computer networks are also shown. Besides the role of ITS that support this enhancement, the importance of non-technological aspects is presented with special emphasis on responsibility and network intelligence.

The paper addresses problems of efficient knowledge representation and exploration of social knowledge enabling agents' integration into the multi-agent community. The proposed solutions are based on task decomposition, organization of negotiations, responsibility delegation and other aspects of social behavior in multi-agent systems aimed at integration of already existing software components. It is supposed that all the social knowledge is kept separated from the problem solving knowledge and agents' specific internal intelligence and that it is organized and administered in the acquaintance models located in the agents' wrappers. A specific tri-base acquaintance model is formalized and discussed throughout the paper. This model helps to optimize the communication traffic and to implement meta-reasoning processes. Practical applications of the acquaintance model in different fields are presented, and acquired experience is discussed.

We present a system for decision support in telecommunications. History data describing the operation of a telephone exchange are analyzed by the system to reconstruct understandable event descriptions. The event descriptions are processed by an algorithm inducing rules describing regularities in the events. The rules can be used as decision support rules (for the exchange operator) or directly to automate the operation of the exchange.

We suggest the Distributed Branch and Bound Algorithm intended for solving problems with exponential complexity e.g. coalition planning. This technique consists of three parts: the state space distribution, effective search algorithm and partial solution expansion. We will describe CPlanT - an OOTW (operations other than war) coalition planning multi-agent system where this approach has been exploited.

This paper presents a case study concerning scheduling and resource allocation issues in a spa. The paper is data-mining oriented. It discusses and describes how the history data can be used as a source for data-mining leading to discovery of rules or algorithms useful for prediction of resources requirements. In particular, we focused to identify groups of patients which appear frequently in the training set and which exhibit characteristic behavior or requirements of spa utilities. Then we predicted a set of health procedures to be passed for each member of such group. This approach resulted in a health procedure prediction algorithm satisfactory for early and convenient scheduling.

The problems of re-configuration and re-planning in multi-agent systems exploring acquaintance models of their behavior are discussed throughout this paper. Both the autonomous and supervised methods of replanning are distinguished. The advantages of the tri-base acquaintance models used in the ProPlanT production planning system for solving the reconfiguration and re-planning tasks are sketched.

This paper comments the role of acquaintance models in agent-based engineering solutions. We present a specific methodology,a tri-base acquaintance model, as formal model of agents’ mutual awareness. The model contains three separate knowledge structures for representing agents’ permanent, semi-permanent and temporary knowledge, respectively, and mechanism for administering, maintenance and exploration of the knowledge. The paper explains how utilisation of an acquaintance model contributes to communication savings and to reduction of overall distributed problem solving complexity. Utilisation of the tri-base acquaintance model is illustrated on ProPlanT multi-agent system for project-oriented production planning. The system architecture exploits several differenttypes of agents exploring the tri-base mechanism including the meta-agents who are used to adjust and tune the agents’ acquaintance models.

The paper discusses various problems related to feeding the data warehouse with data originated in various data sources. The main focus is concentrated on describing and handling of uncertainty, which comes from merging imperfect data sources. Uncertainty measures and their implementation approaches are suggested.

The tri-base acquaintance model of the agent's behaviour is presented in this paper. This represents an extension of the twin-base model [Cao 97]. Based on practical experience, the new model tries to cope with parallel processing, precedence constraints, and sparse resources. The idea of substituting the interagent negotiation processes by the internal planning activity of the agents is stressed. This strategy is enabled by periodic revisions of the knowledge and data contained in the tri-base knowledge/data bases.

The paper studies the possibility to use qualitative reasoning technology for improving behaviour of multi-agent systems. For this purpose, the twin-base communication model [Cao 97] is enriched to contain the state information (a tri-base model is defined) and it is combined with the idea of a super-agent. The super-agent exploring the qualitative reasoning methods acts as a global observer and state evaluator with limited reconfiguration rights. The impact and efficiency of the proposed approach are analysed.

In complex domains, the decisions have to be based on information from various sources. Distributed environment DISCIM, based on multi-agent ideas, has been developed to support the necessary information exchange and distributed processing. This paper describes DISCIM specific methods allowing to create new agents from programs developed as stand-alone, originally. This is the case of a neural network emulator, of a qualitative reasoning system and of a data-base system POSTGRES — the “agentification” of all of them is treated in detail. Attention is devoted to comparison of the DISCIM system with other existing systems.

The parts of AI which seems to be of substantial importance for the CIM tasks are overviewed in this contribution. The stress is aimed mainly at impacts of Knowledge Engineering at the global CIM software architectures, at Distributed AI as a basis for system integration, at AI-centered view on Planning and Scheduling, at the role of Declarative Programming and Machine Learning in CIM tasks. The potential contributions of the stressed topics is discussed.

Qualitative reasoning has been suggested and studied in artificial intelligence as a tool for knowledge representation, mental simulation and simulation of behavior of physical systems. This paper is an attempt to formalize various aspects of human thought covering common sense reasoning about physical reality. We try to point to those CIM tasks, which are of qualitative nature. This review helps us to identify those existing qualitative techniques, which are ready to be used in CIM. This paper introduces readers to some techniques of qualitative simulation on examples.

The first section tries to indicate the reasons for study of qualitative reasoning within the field of Artificial Intelligence. The second section solves an example from physics as an illustartion of usefulness of qualitative arguments. The third section briefly reviews formal systems for qualitative reasoing. The last section offers some suggestions for further reading.