Verification that agent communication protocols have desirable properties or do not have undesirable properties is an important issue in agent systems where agents intend to communicate using such protocols. In this paper we explore the use of model checkers to verify properties of agent communication protocols, with these properties expressed as formulae in temporal logic.  We illustrate our approach using a recently-proposed protocol for agent dialogues over commands, a protocol that permits the agents to present questions, challenges and arguments for or against compliance with a command.

Formal and computational models of argument are ideally suited for education in ill-defined domains such as law, public policy, and science.  Open-ended arguments play a central role in these areas but students of the domains may not have been taught an explicit model of argument.  Computational models of argument may be ideally suited to act as argument tutors guiding students in the formation of arguments and argument analysis according to an explicit model.  In order to achieve this it is important to establish that the models can be understood and evaluated reliably, an empirical question.  In this paper we report ongoing work on the diagnostic utility of argument diagrams produced in the LARGO tutoring system.

We are working on a temporal reasoning web tool for 7-11 olds. The acquisition of temporal relations and reasoning with them depends on age and experience, as well as linguistic factors. We conducted a preliminary evaluation with 6–8 olds in order to assess whether and how they would visually represent “while” temporal relations of a story. In this paper, we present and discuss our experimental evaluation, which paves the way for the visual representation of such relations in our e-tool.

The web forum is a key tool in the building of new knowledge among students in Learning Management Systems. Students’ posted messages, in fact, build up a relationship network which supports a collaborative reflection about the forum topic. In this network two interaction levels can be distinguished. The former is the interaction between peers (the students), the latter between students and instructors (teachers and tutors). The role of the second interaction is particularly important as a feedback mechanism in the discussion dynamic but it is subjected to two kinds of limitations. The first one is the huge number of messages that makes difficult, for tutors and teachers, to quickly evaluate the progress of their students and the second one is the subjective bias of the tutors that influence the evaluation. In order to limit these two inefficiencies a multiagent system can be used to monitor such evolution and recognize the state in which the forum is. Such system is based on metrics derived from the textual and social network analysis that, feeding a rule engine, gives the instructor a more objective view of the forum evolution.

Today an immense volume of electronic health records (EHRs) is being produced. These health records contain abundant information, in the form of both structured and unstructured data. It is estimated that EHRs contain on average around 60 percent structured information, and 40 percent unstructured information that is mostly free text (Dalianis et al., 2009). A modern health record is very complex and contains a large and diverse amount of data, such as the patient’s chief complaints, diagnoses and treatment, and very often an epicrisis, or discharge letter, together with ICD-10 codes, (ICD-10, 2009). Moreover, the health record also contains information about the patient’s gender, age, times of health care visits, medication, measure values, general condition as well as social situation, drinking and eating habits. Much of this information is written in natural language. All this information in a health record is currently almost never re-used, in particular the parts that are written in free text. We believe that the information contained in EHR data sets is an invaluable source for the development and evaluation of a number of applications, useful both for research purposes as well as health practitioners. For instance, text mining tools for finding new or hidden relations between diagnoses/treatments and social situation, age and gender could be very useful for epidemiological or medical researchers. Moreover, information concerning the health process over time, per patient, clinic or hospital, can be extracted and used for further research. Another application is the use of this data as input for simulation of the health process and for future health needs. Also, such huge health record databases can be used as corpora for the generation of generalized synonyms from specialized medical terminology constitutes another exciting application. We can also foresee a text summarization system applied to an individual patient’s health record, but using knowledge from all text records and conveying the information in the health record at the right level to the specific patient. The data can also be used for developing methods where clinicians in their daily work get automatic assistance and proposals of ICD-10 codes for assigning symptoms or diagnoses, or for validating the already manually assigned ICD-10 codes.

The current "Cognitive Systems" initiative under the Euro­pean Commission's "7th Framework Programme for Re­search and Technological Development (FP7)" originated shortly after the turn of the millenium when, under the heading of "Cognitive Vision", a set of nine projects was selected for funding. Its general aim is to give a new im­petus to (1) strengthening the scientific foundation for en­gineering artificial cognitive systems - i.e., artificial sys­tems that perceive and (inter-) act, based on a suitable un­derstanding of their environment; and thus to provide the ground for (2) advancing or creating enabling technologies for a variety of applications involving interaction within all sorts of environment. These pertain to, for instance, but not exclusively, robotics, assistive technologies, and language and vision based man-machine interfaces. As of November 2009 the "Cognitive Systems, Interaction, and Robotics" portfolio comprises some 100 projects, finished and ongo­ing ones, representing almost 300 MEuro in funding. This talk addresses the background, rationale and context of the European "Cognitive Systems" initiative. It high­lights key issues, current achievements and possible future directions.

The goal of this paper is to present four key assumptions regarding the measurement of cognitive and metacognitive regulatory processes used during learning with hypermedia. First, we assume it is possible to detect, trace, model, and foster SRL processes during learning with hypermedia. Second, understanding the complex nature of the regulatory processes during learning with hypermedia is critical in determining why certain processes are used throughout a learning task. Third, it is assumed that the use of SRL processes can dynamically change over time and that they are cyclical in nature (influenced by internal and external conditions and feedback mechanisms). Fourth, capturing, identifying, and classifying SRL processes used during learning with hypermedia is a rather challenging task.

The present historical epoch is unique in the sense that now The present work takes a shot at this target. The author's people may have the opportunity to create something equal answer to the first question should be clear from the above to them, if not greater: machines capable of humanlike and can be formulated concisely as follows: the goal is to intellectual and cultural development. The reason is not design a human-level learner. Yet, this statement needs a only that the hardware available today is compatible in its further clarification. Its limited interpretation could be, raw computational capacities with the human brain. The e.g.: "The goal of a human-level learner is to take complex, main reason is the emergent understanding of how the noisy information from multiple modalities and distill this human mind works. It appears that implementing the same experience into a representation that supports prediction principles of the human mind in a machine would not take about and manipulation of the world" (Shrobe et al., 2006, yet unavailable today computer resources.

In this paper a new completely unsupervised mesh segmentation algorithm is proposed, which is based on the PCA interpretation of the Laplacian eigenvectors of the mesh and on parametric clustering using Gaussian mixtures. We analyse the geometric properties of these vectors and we devise a practical method that combines single-vector analysis with multiple-vector analysis. We attempt to characterize the projection of the graph onto each one of its eigenvectors based on PCA properties of the eigenvectors. We devise an unsupervised probabilistic method, based on one-dimensional Gaussian mixture modeling with model selection, to reveal the structure of each eigenvector. Based on this structure, we select a subset of eigenvectors among the set of the smallest non-null eigenvectors and we embed the mesh into the isometric space spanned by this selection of eigenvectors. The final clustering is performed via unsupervised classification based on learning a multi-dimensional Gaussian mixture model of the embedded graph.

This paper describes some functionalities and features of the Maryland Virtual Patient (MVP) environment. MVP models the process of disease progression, diagnosis and treatment in virtual patients who are endowed with a “body,” a simulation of their physiological and pathological processes, and a “mind,” a set of capabilities of perception, reasoning and action that allow the virtual patient to exhibit independent behavior, participate in a natural language dialog, remember events, hold beliefs about other agents and about specific object and event instances, make decisions and learn.