The evolution in health care to greater support for self-managed care is escalating the demand for e-health systems in which patients can access their personal health information in order to ultimately partner with providers in the management of their health and wellness care. At present, unfortunately, patients are seldom able to easily access their own health information so, as a result, it is often difficult for patients to enter into a dialogue with their healthcare providers about treatment and other options. One truism seems to be constantly ignored: it is not possible for patients to actively manage their health without the requisite information. Health information should be made available through "any time, anywhere" delivery: outside the physician's office or hospital, in the home or other personal setting, on a variety of multimedia information devices. We believe that personalization of health information will be a key element in effective self-managed healthcare.

Learning about complex and challenging science topics with advanced learning technologies requires students to regulate their learning. The deployment of key cognitive and metacognitive regulatory processes is key to enhancing learning in open-ended learning environments such as hypermedia. In this paper, we propose a metaphor—Computers as MetaCognitive tools—to characterize the complex nature of the learning context, self- regulatory processes, task conditions, and features of advanced learning technologies. We briefly outline the theoretical and conceptual assumptions of self-regulated learning (SRL) underlying MetaTutor, a hypermedia environment designed to train and foster students’ SRL processes in biology. Lastly, we provide preliminary learning outcome and SRL process data on the deployment of SRL processes during learning with MetaTutor.

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.

This extended abstract outlines my invited keynote presentation of the recent work on neuromorphic networks ("CrossNets") based on hybrid CMOS/nanoelectronic ("CMOL") circuits, in the space-saving Q/A format.

Some proteins, such as homeodomain transcription factors, contain highly conserved regions of sequence that cannot be attributed to the constrains imposed by any single function. It has recently been suggested that multiple conserved functional domains overlap and together explain the high conservation of these regions. However, because these highly conserved domains are part of much larger proteins, we are still left with the question why so many functional domains cluster together. Here we have modeled an evolutionary mechanism that can produce this kind of clustering. Due to adaptive competition between different protein functions for control over amino acid residue identity, conserved functional domains get displaced from regions undergoing adaptive evolution. At first they undergo a steady random walk within the sequence for an indefinite amount of time; however, a threshold is reached when two functional domains happen to come into contact, at which point there is a dramatic shift in the adaptive dynamics such that the domains rapidly converge, lengthen, and evolve overlap — stabilizing at a fully overlapped state. We also studied the evolution of single amino acid tandem repeats (a.k.a. homopeptides), which are especially prevalent in transcription factors. Homopeptides that are encoded by nonhomogenous mixtures of synonymous codons cannot be explained by the neutral process of replication slippage. Our model provides two ways to explain the origin and maintenance of such repeats, and their over-representation in highly conserved proteins: competition between multiple functional domains for space within a sequence, or reuse of a sequence for many functions over time. Both processes depend on reaching certain critical thresholds, however they both deterministically cause the evolution of repeats once these thresholds are reached. Further, both of these processes are characteristic of multi-functional proteins such as homeodomain transcription factors. We conclude that our model can explain two widely recognized features of transcription factor proteins: conserved domains and a tendency to accumulate homopeptides.

Individuals with high-functioning autism spectrum disorders (HFASD) have very individualistic needs, abilities, and are surrounded by very different social contexts. Consequently, special education and therapeutic interventions often need to be adapted to a particular individual. We are interested in developing systems that can help adolescents with HFASD rehearse and learn social skills with reduced aide from parents, guardians, teachers, and therapists. We describe a social skill learning game that utilizes social scenarios. Because of the individualistic needs and abilities of our target users, we describe ongoing work on AI to assist caregivers with the authoring of tailored social scenarios.

By moving from a health communication perspective, this paper addresses the issue of how to enhance consumers’ health literacy through virtual health environments. More specifically, the paper is structured in two parts. Firstly, we present a conceptualization of health literacy which takes into consideration the complexity of its components. Secondly, we show how this concept was used to design the website ONESELF targeted to consumers affected by chronic low back pain. Findings from our paper are expected to highlight important dimensions of health literacy that virtual healthcare systems – designed to enhance health literacy – will have to operationalise. ONESELF works through a bottom-up approach where users can ask for all information to build or reinforce their level of health literacy. This approach presupposes the physical presence of the content manager who assures the delivery of the information requested through the website. Here the main question arises of how AI systems can assure the same level of tailored information by standing, however, from a genuinely human-computer perspective

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.

Agent adaptability — the ability of agents to change behavioral strategies when it is beneficial to do so — is presumed to be an important part of the robustness of complex adaptive systems (CAS). But, determining when changing behaviors is advantageous for agents has proven quite challenging in CAS research, as sometimes behavioral change is necessary, but other times it can impose costs that exceed benefits. I present the results from experiments using an agent-based model (ABM) designed to discover thresholds after which behavioral flexibility leads to improved societal-level outcomes in groups of agents in dynamic environments. The first major result is that there are thresholds in both levels of flexibility in agent behavior and in levels of turbulence in the environment below and above which there are marked differences in utility gains for agents. In particular, relatively high flexibility leads to lower overall utility scores, as well as, surprisingly, decreased diversity and increased inequality between agents. The second result is that at very high levels of environmental turbulence, the effects of the environment alone on agent utility overshadow any benefits to agents from flexible behavior strategies. This suggests, counter-intuitively, that the best strategy for agents in very dynamic environments is simply to keep behavior constant. The third major result is that there is an interaction between agent behavior and the environment: high flexibility of other agents can effectively make an environment more "dynamic", which just fuels more flexibility, and leads to a scramble between different strategies with no utility gain. A final theoretical contribution of the paper is that the model is able to show drawbacks to flexibility without relying on costs to changing behavior, as is done in much of the literature on strategy change.

We argue that transfer of spatial and conceptual knowledge between tasks and domains is an essential benchmark for multi-representational architectures aimed at human-level intelligence. The underlying hypothesis is that spatial relationships provide a natural level of abstraction, highlighting the similarities and differences between situations and domains. Therefore, not only will spatial representations improve domain reasoning and learning, they will also facilitate the transfer of knowledge across domains. The simulated environments of real-time strategy (RTS) games provide an excellent test-bed for exploring this hypothesis for two reasons: many different RTS domains have been constructed and RTS requires a wide range of reasoning tasks.