Such applications In general, there is a many-to-many relationship between provide an intuitive and easy-to-navigate user interface signs and symptoms, so attempting to accurately correlate through which the patient selects a symptom or set signs with symptoms can be computationally expensive. of symptoms and through which detailed information is displayed However, clustering in the (topological) product of sign and about the probable causes. Valuable advice can be symptom space should enhance performance.

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.

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.

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.

Argumentation is now a very fertile area of research in Artificial Intelligence. Yet, most approaches to reasoning with arguments in AI are based on a normative perspective, relying on intuition as to what constitutes correct reasoning, sometimes aided by purpose-built hypothetical examples. For these models to be useful in agent-human argumentation, they can benefit from an alternative, positivist perspective that takes into account the empirical reality of human reasoning. To give a flavour of the kinds of lessons that this methodology can provide, we report on a psychological study exploring simple reinstatement in argumentation semantics. Empirical results show that while reinstatement is cognitively plausible in principle, it does not yield full recovery of the argument status, a notion not captured in Dung's classical model. This result suggests some possible avenues for research relevant to making formal models of argument more useful.

There are two aspects of practical reasoning which present particular difficulties for current approaches to modelling practical reasoning through argumentation: temporal aspects, and the intrinsic worth of actions. Time is important because actions change the state of the world, we need to consider future states as well as past and present ones. Equally, it is often not what we do but the way that we do it that matters: the same future state may be reachable either through desirable or undesirable actions, and often also actions are done for their own sake rather than for the sake of their consequences. In this paper we will present a semantics for practical reasoning, based on a formalisation developed originally for reasoning about commands, in which actions and states are treated as of equal status. We will show how using these semantics facilitates the handling of the temporal aspects of practical reasoning, and enables, where appropriate, justification of actions without reference to their consequences.

Conceptual Blending through the process of Double Scope Blending provides an account for human creativity. We show how computational creativity can be modeled after Double Scope Blending for machine generation of scenarios, stories, hypotheses, etc. This paper describes an application of this process to the generation of novel and creative scenarios in the maritime security domain.

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.

This paper advocates the use of a portfolio of representations for problem solving in complex domains. It describes an approach that decouples efficient storage mechanisms called descriptives from the decision-making procedures that employ them. An architecture that takes this approach can learn which representations are appropriate for a given problem class. Examples of search with a portfolio of representations are drawn from a broad set of domains.

We propose a method for learning the intrinsic topology of a point set sampled from a curve embedded in a high-dimensional ambient space. Our approach does not rely on distances in the ambient space, and thus can recover the topology of sparsely sampled curves, a situation where extant manifold learning methods are expected to fail. We formulate a loss function based on the smoothness of a curve, and derive a greedy procedure for minimizing this loss function. We compare the efficacy of our approach with representative manifold learning and hierarchical clustering methods on both real and synthetic data.