Knowledge Representation

This article is about nooplasis. That is, the article outlines a general model about the dynamic organization and development of mind and it draws the implications of this model for learning and instruction. This is done in terms of 10 postulates concerned with the architecture of mind, its development and dynamics, and the nature of learning, and a general postulate concerned with the dynamic relations between the various systems of mind and also mind and education. Specifically, the model postulates that the mind involves systems oriented to the understanding of the environment and of itself, in addition to general processing functions. It is also postulated that the development of each of the systems is partially autonomous and partially constrained by the development of the other systems, and that it involves both system-specific and systemwide mechanisms of development and learning. Finally, it is argued that these postulates suggest a model of constrained constructivism which leads to a conception of learning in schools which differs considerably from what is suggested by the Piagetian or the Vygotskian conception of constructivism.

9
th International Conference of Artificial Intelligence and Fuzzy Logic (AI & FL 2021)
provides a forum for researchers who address this issue and to present their work in a peerreviewed forum. Authors are solicited to contribute to the conference by submitting articles that
illustrate research results, projects, surveying works and industrial experiences that describe
significant advances in the following areas, but are not limited to these topics only.
Authors are solicited to contribute to this conference by submitting articles that illustrate
research results, projects, surveying works and industrial experiences that describe significant
advances in the areas of Artificial Intelligence & applications

The International Journal of Artificial Intelligence & Applications (IJAIA) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the Artificial Intelligence & Applications (IJAIA). It is an international journal intended for professionals and researchers in all fields of AI for researchers, programmers, and software and hardware manufacturers. The journal also aims to publish new attempts in the form of special issues on emerging areas in Artificial Intelligence and applications.

Authors are solicited to contribute to the journal by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the areas of Artificial Intelligence & applications.

A design environment for acquisition, analysis and validation ot information for automated mechanical assembly is proposed in this paper. The environment acts as a preprocessing step before the actual assembly operation. The working memory-consisting mainly ot instances of component and subassembly data objects and a B-rep based structured lace adjacency graph-is used to establish the assembly precedence relationship. The environment has been implemented in the objectoriented programming language Smalltalk-80. An objectbased approach has been preterred to a procedural language because of increased modularity, data abstraction, and efficiency ol the code. The hierarchical class (data type) structure of object-oriented programming has been used to define a hierarchical mating requirement module permitting knowledge inheritance from parent classes. computer-aided design, design environments, automated mechanical assemblies, data representation

This paper describes how we have developed a knowledge base for a heuristic application in a clinical domain. Firstly, the knowledge base has been modelled using CommonKADS. Later, the knowledge base has been represented in the language DAML+OIL. We will illustrate in some depth how objects, classes and relationships of a medical domain (modelled following an object-oriented approach) can be specified in the Web language DAML+OIL.

This paper proposes several speech technology improvements for increasing robustness, reliability and ergonomics in speech interfaces for controlling aerial vehicles. These improvements consist of including a statistical language model for increasing the robustness against spontaneous speech, incorporating confidence measures for evaluating the performance of on-line the speech engines (better reliability), and a flexible response generation for improving the interface ergonomics. This paper includes a detailed description of the speech control interface developed as a result of the collaboration between the GTH (Grupo de Tecnología del Habla or Speech Technology Group) at Universidad Politécnica de Madrid (UPM) and the company Boeing Research and Technology Europe under the contract No. 206/05. This interface includes modules that perform speech recognition, natural language understanding and response generation via a speech synthesizer. In the system evaluation, the final results reported a 96.4% Word Accuracy and a 92.2% Semantic Concept Accuracy. This paper also provides a state-of-art review of using Speech Technology for controlling aerial vehicles, comparing the main initiatives carried out. A significant conclusion of this work is that Speech Technology is now ready enough to be considered as a new modality (in parallel with traditional ones) for introducing high level commands while the controller is carrying out others actions when interacting with these control systems. In critical applications (such as this) the best performance of this technology is achieved when all the configuration possibilities of the speech engines are accessible and the speech interface is designed in collaboration with Speech Technology experts.

omputer-based information systems, connected to worldwide high-speed networks, provide increasingly rapid access to a wide variety of data resources.' This technology expands access to data, requiring capabilities for assimilation and analysis that greatly exceed what we now have in hand. Without intelligent processing, these advances will provide only a minor benefit to the user at a decision-making level. That brave user will be swamped with illdefined data of unknown origin. The problems. This article will expand on the two types of problems that exist: For single databases. a primary hindrance for end-user access is the volume of data that is becoming available. the lack of abstraction, and the need to understand the representation of the data. When information is combincc1 trom multiplc datahasch. thc major concc'rn is w-1 t hc mismatch c ncou n t crccl i n i n tor inn t io n re prcsc n I ;I t ion :i ncl h t r uct ti re. Mediators embody the administrative and technical knowledge to create information needed for user decision-making modules. The goal is to exploit the data technology puts within our reach. Volume and abstraction. The volume of data can be reduced by selection. It is not coincidental that Select is the principal operation of relational database management systems, but selected data is still at too fine a level of detail to be useful for decision making. Further reduction is achieved by abstracting data to higher levels. Aggregation operations such as Count, Average, Standard-Deviation, Maximum, and Minimum provide some computational facilities for abstraction. Today, such abstractions are formulated within the end user's application, using a variety of domain knowledge. For most base data, more than one abstraction must be supported: For the sales manager, the aggregation is by sales region. while aggregation by customer income is appropriate for marketing. Figure 1 presents examples of required abstraction types. Computations for abstraction may be extensive and complex. Collecting all instances that lead to an abstraction can involve recursion, say, locating all potentially useful flight segments for a trip. Such a computation cannot be specified with current database query languages. Hence, application programs are written by specialists to reduce the data. Using specific data-processing programs as intermediaries reduces flexibility and responsiveness for the end user. The knowledge that creates the abstractions is hidden and hard to share and reuse. Mismatch. As Figure 2 shows, data obtained from remote and autonomous sources often will not match in terms of name, scope, granularity of abstractions, temporal units, and domain definitions. The differences shown in the examples must be resolved before automatic processing can join these values. Without an extended processing paradigm, as proposed here, the information needed to initiate actions will be hidden in ever larger volumes of detail. scrollable on ever larger screens, in ever smaller fonts. In essence, the gap between information and data will be even wider than it is now. Knowing that information exists and is accessible gives end users expectations. Finding that it is not available in a useful form or that it cannot be combined with other data creates confusion and frustration. I believe the reason some users object to computer-based systems, saying they create information overload, is that they get too much of the wrong kind of data. (See the sidebar, "Knowledge versus data. ") Use of a model. To visualize the requirements we will place on future information systems, let's consider the activities carried out today when deci-Type of Mismatch Type of Abstraction Example Base Data Abstraction Granularity Sales detail 4 Product summaries Generalization Temporal Daily sales 4 Seasonally adjusted Relative Product cost + Inflation-adjusted trends Exception recognition Accounting detail 4 Evidence of fraud Path computation Airline schedules + Trip duration and cost Product data-+ Product type monthly sales Figure 1. Abstraction functions. Knowledge versus data I favor a pragmatic distinction between data and knowledge in this model. Data describes specific instances and events. It may be gathered automatically or clerically. The correctness of data can be checked versus the real world. Knowledge describes abstract classes. Each class can cover many instances. Experts are needed to gather and formalize knowledge. One item of knowledge can affect the use of many items of data; also, one item of new data can disprove or weaken existing knowledge.' 40 COMPUTER

An approach to high-level interaction with autonomous robots by means of schematic maps is outlined. Schematic maps are knowledge representation structures to encode qualitative spatial information about a physical environment. A scenario is presented in which robots rely on high-level knowledge from perception and instruction to perform navigation tasks in a physical environment. The general problem of formally representing a physical environment for acting in it is discussed. A hybrid approach to knowledge and perception driven navigation is proposed. Different requirements for local and global spatial information are noted. Different types of spatial representations for spatial knowledge are contrasted. The advantages of high-level / low-resolution knowledge are pointed out. Creation and use of schematic maps are discussed. A navigation example is presented.

I) By expressing the contents of a knowledge base as fomnal models, you can synthesize systems in real-time payallel-compu ting environments. The authors apply their technique t o a signalprocessing system. challenging problems in software engineering is to build high-performance reactive systemssystems characterized by continuous interaction with their environment. T h e behavior of such systems, which are found in process monitoring and control and real-time signal-processing, is typically complex, concurrent, and nondeterministic, tnaking them difficult to build.

In complex reasoning tasks it is often the case that there is no single, correct set of conclusions given some initial information. Instead, there may be several such conclusion sets, which we will call belief sets. In the present paper we introduce nonmonotonic belief set operators and selection operators to formalize and to analyze structural aspects of reasoning with multiple belief sets. We define and investigate formal properties of belief set operators as absorption, congruence, supradeductivity and weak belief monotony. Furthermore, it is shown that for each belief set operator satisfying strong belief cumulativity there exists a largest monotonic logic underlying it, thus generalizing a result for nonmonotonic inference operations. Finally, we study abstract properties of selection operators connected to belief set operators, which are used to choose some of the possible belief sets. Keywords : nonmonotonic inference, knowledge representation, belief sets. AMS(MO...

Nowadays, the use of domain ontologies in e-Learning applications is rapidly increasing due to the important role they play in knowledge representation, sharing of didactical material and content personalization. However, the ontology building processes is still extremely difficult to achieve. In this paper we present a semi-automatic process based on knowledge extraction from existing SCORM educational content aimed to speed up and facilitate the realization of domain ontologies and the breakdown of SCORM packages in fine-grained, rearrangeable learning objects appropriate for building personalized e-Learning experience.

We created an inference engine and query language for expressing temporal patterns in data. The patterns are represented by using temporally-ordered sets of data objects. Patterns are elaborated by reference to new objects inferred from original data, and by interlocking temporal and other relationships among sets of these objects. We found the tools well-suited to define scenarios of events that are evidence of inappropriate use of prescription drugs, using Medicaid administrative data that describe medical events. The tools' usefulness in research might be considerably more general.

Knowledge management (KM) represents a topic of considerable current interest. However, the majority of extant KM research treats knowledge as a static object that can be acquired, stored and retrieved through information technology. Two fundamental problems emerge from this current majority view: (1) many scholars view knowledge as distinct from information, and (2) very little attention is paid to the

Taking notes is of uttermost importance in academic and commercial use and success. Different techniques for note-taking utilise different cognitive processes and strategies. This experimental study examined ways to enhance cognitive performance via different note-taking techniques. By comparing performances of traditional, linear style note-taking with alternative non-linear technique, we aimed to examine the efficiency and importance of different ways of taking notes. Twenty-six volunteer adult learners from an information management course participated in this study. Cognitive performance scores from a traditional linear note-taking group were compared with another group by using a commercially available non-linear note-taking technique. Both groups were tested in two settings: after a classroom lecture and a panel forum discussion. Tasks included measures on story comprehension, memory, complexity of mental representations and metacognitive skills. Data analysis revealed that the non-linear note-takers were significantly better than the linear group both in terms of the quantity and the quality of the learned material. This study demonstrates the importance of using cognitively compatible note-taking techniques. It identifies the cognitive mechanisms behind effective note-taking and knowledge representation. Using such techniques enables deeper understanding and more integrated knowledge management.

In this paper we give a short introduction to logic programming approach to knowledge representation and reasoning. The intention is to help the reader to develop a 'feel' for the field's history and some of its recent developments. The discussion is mainly limited to logic programs under the answer set semantics. For understanding of approaches to logic programming built on well-founded semantics, general theories of argumentation, abductive reasoning, etc., the reader is referred to other publications.  (M. Gelfond), leone@unical.it (N. Leone). 0004-3702/02/$ -see front matter  2002 Elsevier Science B.V. All rights reserved. PII: S 0 0 0 4 -3 7 0 2 ( 0 2 ) 0 0 2 0 7 -2

c o m p u t e r m e t h o d s a n d p r o g r a m s i n b i o m e d i c i n e 1 1 3 ( 2 0 1 4 ) 133-143 Case based fuzzy cognitive maps Bayesian network Decision support system Knowledge representation Semantic web Practice guideline a b s t r a c t

We present a logical framework for modeling and reasoning about the evolution of requirements. We demonstrate how a sufficiently rich meta level logic can formally capture intuitive aspects of managing changes to requirements models, while maintaining completeness and consistency. We consider a theory as the deductive closure of a given set of axioms and conclude that software engineering is concerned, in essence, with, building and managing large theories. This theory construction commences with the development of the requirements model which we view as a theory of some nonmonotonic logic. Requirements evolution then involves the mapping of one such theory to another. Exploiting the deductive power of the theory of belief revision and nonmonotonic reasoning we develop a formal description of this mapping, as well as the requirements engineering process itself. This work thus offers a rigorous approach to reasoning about requirements evolution and a important focus for defining semantically well founded methods and tools for the effective management of changing requirements

Using the ideas from current investigations in Knowledge Representation we study the use of a class of logic programs for reasoning about infinite sets. Our programs reason about the codes for various infinite sets. Depending on the form of atoms allowed in the bodies of clauses we obtain a variety of completeness results for various classes of arithmetic sets of integers.

Within the scope of ANSWER, an EC-supported research project, a new artistic notation system, called DirectorNotation, is being developed for describing media content, much like notes are used to document a music composition. Our current research focuses on developing “notation-enabled ” offline authoring tools, but interactive applications are an anticipated extension. Here, we summarise the key aspects of DirectorNotation and the ANSWER project, from the point of view of a multimedia-technology audience interested in content-based manipulation of media. Categories and Subject Descriptors

There exists a large conceptual gap between symbolic models and emergent models for the mind. Many emergent models work on low-level sensory data, while many symbolic models deal with high-level abstract (i.e., action) symbols. There has been relatively little study on intermediate representations, mainly because of a lack of knowledge about how representations fully autonomously emerge inside the closed brain skull, using information from the exposed two ends (the sensory end and the motor end). As reviewed here, this situation is changing. A fundamental challenge for emergent models is abstraction, which symbolic models enjoy through human handcrafting. The term abstract refers to properties disassociated with any particular form. Emergent abstraction seems possible, although the brain appears to never receive a computer symbol (e.g., ASCII code) or produce such a symbol. This paper reviews major agent models with an emphasis on representation. It suggests two different ways to relate symbolic representations with emergent representations: One is based on their categorical definitions. The other considers that a symbolic representation corresponds to a brain's outside behaviors observed and handcrafted by other outside human observers; but an emergent representation is inside the brain.

The Spaceport Processing Systems Branch at NASA Kennedy Space Center has developed and deployed an agent based tool to monitor the Space Shuttle's ground processing telemetry stream. The application, the NASA Engineering Shuttle Telemetry Agent, increases situational awareness for system and hardware engineers during ground processing of the Shuttle's subsystems. The agent provides autonomous monitoring of the telemetry stream and automatically alerts system engineers when predefined criteria have been met. Efficiency and safety are improved through increased automation. Sandia National Labs' Java Expert System Shell is employed as the rule engine. The shell's predicate logic lends itself well to capturing the heuristics and specifying the engineering rules of this spaceport domain. The declarative paradigm of the rule-based agent yields a highly modular and scalable design spanning multiple subsystems of the Shuttle. Several hundred monitoring rules have been written thus far with corresponding notifications sent to Shuttle engineers. This paper discusses the rule-based telemetry agent used for Space Shuttle ground processing and explains the problem domain, development of the agent software, benefits of AI technology, and deployment and sustaining engineering of the product.

Abstract. Models based on neural and neuro-fuzzy structures are developed to represent knowledge about a large data set containing chemical descriptors of organic compounds, commonly used in industrial processes. The neuro-fuzzy models here proposed include ...

We propose a set of methods to classify vendors based on estimated CPU performance and predict CPU performance based on hardware components. For vendor classification, we use the highest and lowest estimated performance and frequency of occurrences of each vendor to create classification zones. These zones can be used to identify vendors who manufacture hardware that satisfy a given performance requirement. We use multi-layered neural networks for performance prediction, which account for nonlinearity in performance data. Various neural network architectures are analysed in comparison to linear, quadratic, and cubic regression. Experiments show that neural networks obtain low error and high correlation between predicted and published performance values, while cubic regression produces higher correlation than neural networks when more data is used for training than testing. An analysis of how the neural network architecture affects prediction is also performed. The proposed methods can be used to identify suitable hardware replacements.

A crucial task in Knowledge Representation is answering queries posed over a knowledge base, represented as a set of facts plus a set of rules. In this paper we address the problem of answering conjunctive queries posed over knowledge bases where rules are an extension of Datalog rules, called Datalog ∃ rules, that may have existentially quantified variables in the head; this kind of rules are traditionally called tuple-generating dependencies (TGDs) in the database literature, but they are broadly used in description logics and in ontological reasoning. In this setting, the chase algorithm is an important tool for query answering. So far, most of the research has concentrated on cases where the chase terminates. We define and study large classes of TGDs under which the query evaluation problems remain decidable even in case the chase does not terminate. We provide tight complexity bounds for such cases. Our results immediately extend to query containment.

Various relation-based systems, concerned with the qualitative representation and processing of spatial knowledge, have been developed in numerous application domains. In this article, we identify the common concepts underlying qualitative spatial knowledge representation, we compare the representational properties of the different systems, and we outline the computational tasks involved in relation-based spatial information processing. We also describe symbolic spatial indexes, relation-based structures that combine several ideas in spatial knowledge representation. A symbolic spatial index is an array that preserves only a set of spatial relations among distinct objects in an image, called the modeling space; the index array discards information, such as shape and size of objects, and irrelevant spatial relations. The construction of a symbolic spatial index from an input image can be thought of as a transformation that keeps only a set of representative points needed to define the relations of the modeling space. By keeping the relative arrangements of the representative points in symbolic spatial indexes and discarding all other points, we maintain enough information to answer queries regarding the spatial relations of the modeling space without the need to access the initial image or an object database. Symbolic spatial indexes can be used to solve problems involving route planning, composition of spatial relations, and update operations.

Rather than a document that is constantly being written as in the wiki approach, the Living Document (LD) is a document that also acts as a document router, operating by means of structured and organized social tagging and using existing ontologies. It offers an environment where users can manage papers and related information, share their knowledge with their peers and discover hidden associations among the shared knowledge. The LD builds upon both the Semantic Web, which values the integration of well-structured data, and the Social Web, which aims to facilitate interaction amongst people by means of user-generated content. In this vein, the LD is similar to a social networking system, with users as central nodes in the network, with the difference that interaction is focused on papers rather than people. Papers, with their ability to represent research interests, expertise, affiliations, and links to web based tools and databanks, are the central axis for interaction amongst user...

This article provides a theoretical and methodological framework for matching, and ultimately the comprehensibility and usability of CPGs. Cognitive methods of analysis can help guideline designers and system the use of cognitive analysis to support the representation of biomedical builders throughout the development process, from the conceptual knowledge and the design of clinical systems, using clinical-practice design of a computer-based system to its implementation phases. By guidelines (CPGs) as an example. We propose that propositional and studying how guideline creators and developers represent guidelines, semantic analyses, when used as part of the system-development proboth mentally and in text, and how end-users understand and make cess, can improve the validity, usability, and comprehension of the decisions with such guidelines, we can inform the development of resulting biomedical applications. The framework we propose is based technologies that seek to improve the match between the representations on a large body of research on the study of how people mentally of experts and practitioners. We urge informaticians to recognize the represent information and subsequently use it for problem solving. potential relevance of cognitive analysis methods and to begin more This research encompasses many areas of psychology, but the more extensive experimentation with the their use in biomedical informatics important ones are the study of memory and the study of comprehenresearch. ᭧ 2001 Academic Press sion. Of particular relevance is research devoted to investigating the Key Words: cognitive analysis; propositional analysis; semantic netcomprehension and memory of language, expressed verbally or in text.

The theory of fuzzy sets and the development of qualitative reasoning have had similar motivations: coping with complexity in reasoning about the properties of physical systems. An approach is described that utilizes fuzzy sets to develop a fuzzy qualitative simulation algorithm that allows a semiquantitative extension to qualitative simulation, providing three significant advantages over existing techniques. Firstly, it allows a more detailed description of physical variables, through an arbitrary, but finite, discretisation of the quantity space. The adoption of fuzzy sets also allows common-sense knowledge to be represented in defining values through the use of graded membership, enabling the subjective element in system modelling to be incorporated and reasoned with in a formal way. Secondly, the fuzzy quantity space allows more detailed description of functional relationships in that both strength and sign information can be represented by fuzzy relations holding against two or multivariables. Thirdly, the quantity space allows ordering information on rates of change to be used to compute temporal durations of the state and the possible transitions. Thus, an ordering of the evolution of the states and the associated temporal durations are obtained. This knowledge is used to develop an effective temporal filter that significantly reduces the number of spurious behaviors. Experimental results with the algorithm are presented and comparison with other recently proposed methods is made.

From networks to Description Logics 8 1.3 Knowledge representation in Description Logics 16 1.4 From theory to practice: Description Logics systems 20 1.5 Applications developed with Description Logics systems 24 1.6 Extensions of Description Logics 34 1.7 Relationship to other fields of Computer Science 40 1.8 Conclusion 43 Part one: Theory 45 2 Basic Description Logics F. Baader, W. Nutt 47 2.1 Introduction 47 2.2 Definition of the basic formalism 50 2.3 Reasoning algorithms 78 2.4 Language extensions 95 3 Complexity of Reasoning F. M. Donini 101 3.1

In this paper we survey the literature of both Knowledge Representation and Civil Engineering regarding the Applications of Knowledge Representation and Reasoning Techniques to Civil and Structural Engineering. The reviewed works are analyzed in terms of the scope, objectives and approach employed in the investigation, and compared to each other based on a preliminary analysis of the problems posed by the application fields. The conclusions of the review are fundamentally three: (1) the need for deeper investigations of the applicability of Artificial Intelligence approaches to Civil and Structural Engineering, especially in view of the new techniques and methods lately developed; (2) the need for new systematic analysis of the domain of Civil and Structural Engineering, in particular from an ontological point of view; (3) the expectation of the birth of a new discipline, as the junction of civil and structural engineering with artificial intelligence, whose specific AI point of view will be centered on Formal Ontology. For this new discipline we propose the name of Structurotics.

This paper explores the use of design patterns and anti-patterns in teaching human-computer interaction principles. Patterns are increasingly popular and are seen as an efficient knowledge transfer mechanism in many fields, including software development in the field of software engineering, and more recently in the field of human-computer interaction. In software engineering a concerted effort is also being made to identify and document anti-patterns for recording the experiences of expert software developers to caution novices against potential bad practices. It is, however, essential that we ensure compatibility with the learner's internal knowledge representation and acquisition processes, whether we are attempting to convey the knowledge in the form of a pattern or an anti-pattern. Since teaching with anti-patterns implies using negation, the primary aim of the research reported in this paper is to explore the efficacy of negative, rather than positive, teaching mechanisms. Evidence from theories of mental modelling and knowledge acquisition that highlight significant dangers in the use of anti-patterns to teach novices human-computer interaction principles is presented and supported with empirical findings. We started off by investigating the use of patterns (positive) in teaching, and then carried out experiments to test the use of anti-patterns (negative) in teaching HCI principles. This paper, whilst reporting mainly on our findings with respect to HCI design anti-patterns, will also identify some problems with the structure and use of patterns and anti-patterns in pedagogy.

Inductive inference allows humans to make powerful generalizations from sparse data when learning about word meanings, unobserved properties, causal relationships, and many other aspects of the world. Traditional accounts of induction emphasize either the power of statistical learning, or the importance of strong constraints from structured domain knowledge, intuitive theories or schemas. We argue that both components are necessary to explain the nature, use and acquisition of human knowledge, and we introduce a theory-based Bayesian framework for modeling inductive learning and reasoning as statistical inferences over structured knowledge representations.

The Galois (or concept) lattice produced from a binary relation has been proved useful for many applications. Building the Galois lattice can be considered as a conceptual clustering method since it results in a concept hierarchy. This article presents incremental algorithms for updating the Galois lattice and corresponding graph, resulting in an incremental concept formation method. Different strategies are considered based on a characterization of the modifications implied by such an update. Results of empirical tests are given in order to compare the performance of the incremental algorithms to three other batch algorithms. Surprisingly, when the total time for incremental generation is used, the simplest and less efficient variant of the incremental algorithms outperforms the batch algorithms in most cases. When only the incremental update time is used, the incremental algorithm outperforms all the batch algorithms. Empirical evidence shows that, on the average, the incremental update is done in time proportional to the number of instances previously treated. Although the worst case is exponential, when there is a fixed upper bound on the number of features related to an instance, which is usually the case in practical applications, the worst case analysis of the algorithm also shows linear growth with respect to the number of instances.

In this paper the fuzzy knowledge representation of a neural network-based fuzzy model is presented. The model is used to assess student's motivational state in a discovery learning environment. Student's observable behavior and motivational factors are described with linguistic variables. The inputs of the model are tailored from real students' data, with the assistance of a group of expert teachers. Results of our preliminary study were encouraging, since data obtained from real students' log files, have been successfully used to form the membership functions that assign membership degrees to the linguistic values of the linguistic variables.

An elevator configuration task, the VT task, is modelled within DESIRE as a design task. DESIRE is a framework within which complex reasoning tasks are modelled as compositional architectures. Compositional architectures are based on a task decomposition, acquired during task analysis. An earlier developed generic task model of design, based on a logical analysis and synthesis of task models devised for diverse applications, has been refined for the elevator configuration task. The resulting task model includes a description of the ontology of the elevator domain and a description of the task model.

Assessment in the context of education is the process of characterizing what a student knows. The reasons to perform assessment are quite varied, ranging from a need to informally understand student learning progress in a course to a need to characterize student expertise in a subject. Finding an appropriate assessment tool is a central challenge in designing an assessment approach. The focus of this paper is on the use of concept maps for both course-level and program-level assessment in engineering education. Concept maps, which are node and arc representations of the relationships among concepts, provide one means to represent student knowledge. This paper presents background on concept maps and describes uses of concept maps at both the course and program level.

— It is no surprise that the notion of situation is key to situation awareness. The development of the discipline can thus benefit from careful analysis of the notion. In this paper, we approach this by proposing an ontology of situations and scenes. The main contribution of this ontology is that it accounts for how situations progress in time changing qualitatively, constituting what we call scenes. The ontology is built by reusing basic elements from the Unified Foundational Ontology (UFO). It addresses objects, occurrences, and their formal relations to situations and scenes. We use the theory of embodiment proposed by the philosopher Kit Fine to explicate how scenes and situations form wholes constituted of parts.

Good analysis alone may not achieve the goals of decision analysis (DA) engagements. Good communication of the results of that analysis can help stakeholders understand, accept, and implement the recommended course of action. Practitioners can use decision analytic principles when considering the decision of how to communicate results themselves. From this perspective, we consider a range of questions to ask in preparing for communication with the client and other stakeholders. We review standard communication practices in DA engagements. The standard practice can be improved by drawing on insights from other areas of management practice. Decision analysis has both technical and organizational features, and we discuss ways to deal with the conceptual and expressive challenges this presents. This pragmatic tutorial provides a starting point for decision analysts to develop both technical communication skills and organizational communication skills.

Both symbolic knowledge representation systems and machine learning techniques, including artificial neural networks, play a significant role in Artificial Intelligence. Interweaving these techniques, in order to achieve adaptation and robustness in classification problems is of great importance. In this paper we present a novel architecture that can provide effective connectionist adaptation of ontological knowledge. The proposed architecture can be used to improve performance of multimedia analysis and man machine interaction systems.

The need for flexible and well understood knowledge representations which are capable of capturing clinical guidelines and protocols for decision support systems is widely recognised. The PROforma method for specifying clinical guidelines and protocols comprises a graphical notation for their design, and a formal knowledge representation language to enable them to be executed by a computer to support the management of medical procedures and clinical decision making. PROforma technology consists of a graphical knowledge editor for the creation of guidelines, and an enactment engine for testing and executing them. This paper provides an overview of the motivation and structure of PROforma, and illustrates its use in the development of clinical applications.