Thus, design knowledge and a description of the desired product existing product environmental impact assessment to automatically synthesize potential solutions. This work approaches are most beneficial to implementing changes focuses on a morphological matrix based approach that during the detailed design phase. In addition, impacts due operates on information stored in a design repository to to materials choices, manufacturing processes utilized, and output high-level descriptions of possible solutions. The transportation of an existing product can be evaluated and following section describes the data source and concept reduced. It has been recognized, however, that generation algorithm.

The research project here described aims to contribute to the issue of sustainability of buildings by improving the architectural design process with the development of a decision support tool for the architect. In particular, the research adopts the improvement of existing designs, namely encouraging energy-efficient redesigns while improving indoor environmental quality as its strategy to promote sustainability. Redesign strategy is considered not only to extend the life cycle of a building but also to contribute to the realization of the overall transition towards an efficient and clean climate. The starting point for this research is the question of how to develop an integral framework which enables the modelling of design knowledge through more energy-efficient dwellings with acceptable indoor comfort in the sustainability context so that it would be possible to deal with qualitative, quantitative, complex and contradictory information at the same time and integrate these into design decision-making processes. This modelling approach is considered to provide a link to developing a tool or a link to be embedded in an existing tool. In the development of such an approach, how Artificial Intelligence (AI) can facilitate an integral understanding of the aspects is raised as a methodological question in terms of information processing and knowledge integration in the form of a design decision support tool. By this way it will be possible to assess the performance of the end result with respect to design choices, beforehand.

In recent years, numerous methods to aid designers in conceptualizing new products have been developed. These methods intend to give structure to a process that was, at one time, considered to be a purely creative exercise. Resulting from the study, implementation, and refinement of design methodologies is the notion that both the structure of the development process and the structure of the developed product are key factors in creating value in a firm’s product line. With respect to the latter key factor, product architecture, but more specifically, modular product architecture has been the subject of much study. This research is focused on two tasks: advancing the notion of a modular product architecture in which modules can be incorporated into a product ‘post-market,’ and creating a method that aids designers leverage knowledge of natural symbiotic relationships to synthesize these post-market modules. It adds to prior work by first, defining the terms ‘derivative product’ and ‘host product’ to describe the post-market module and the product that the module augments, respectively. Second, by establishing three guidelines that are used to assess the validity of potential derivative products, giving the newly termed host and derivative product space defined boundaries. And lastly, by developing a 7-step, biomimetic-based methodology that can be used to create derivative product concepts (post-market modules). By using this methodology, the engineered products are designed on symbiotic principles found in nature.

The paper describes the first phase of development of a Case-Base Reasoning (CBR) system to support early conceptual design of buildings. As specific context of application, the research focuses on energy performance of commercial buildings, and the early identification of energy-related features that contribute to its outcomes. The hypothesis is that bringing knowledge from relevant precedents may facilitate this identification process, thus offering a significant contribution for early analysis and decision-making. The paper introduces a proof-of-concept for such a system, proposing a novel integration of Case-Based Reasoning, Parametric Modeling (Building Information Modeling), and Ontology Classification. Potential advantages and limitations of this three-level integration approach are discussed along with recommendations for future development.

GENE_ARCH is a Generative Design System that combines Pareto Genetic Algorithms with an advanced energy simulation engine. This work explores its integration with a Shape Grammar, acting as GENE_ARCH’s shape generation module. The islamic patio house typology is readdressed in a contemporary context, by improving its energy-efficiency, and rethinking its role in the genesis of high-density urban areas, while respecting its specific spatial organization and cultural grounding. Field work was carried out in Marrakesh, surveying a number of patio houses, becoming the Corpus of Design, from where a shape grammar was generated. The computational implementation of the patio house grammar was done within GENE_ARCH. The resulting program was able to generate new, alternative patio houses designs that were more energy efficient, while respecting the traditional rules captured from the analysis of existing houses. After the computational system was fully implemented, it was possible to realise a large number of experiments. The first experiments kept more restrained rules, thus generating new designs that closer resembled the existing ones. The progressive relaxation of rules and constraints allowed for a larger number of variations to emerge. Analysis of energy results provide insight into the main patterns resulting from the GA search processes.

Buildings designed around occupant A sustainable home is more than a green building: it is also intelligence will provide flexible, adaptive task a living experience that encourages occupants to use fewer environments, refined control zones and technologies that resources more effectively. Research has shown that small maximize occupants' access to adaptive opportunities changes in behaviour in how we use our homes, such as (Cole & Brown, 2009). Architects, engineers and system turning off lights, reducing heat and uncovering or designers are faced with the challenge of reframing design covering windows, or shortening showers, can result in strategies as a co-evolution of human and building substantial energy and water savings. But changing the intelligence that will encourage as well as underpin way we use resources is proving challenging.

We apply artificial intelligence techniques to perform data analysis and activity recognition in smart homes. Sensors embedded in smart home provide primary data for reasoning about observations. The final goal is to provide appropriate assistance for residents to complete their Daily living Activities. Here, we introduce a qualitative approach that considers spatiotemporal specifications of activities in the Activity Recognition Agent to do knowledge representation and reasoning about the observations. We consider different existing uncertainties within sensors observations and Observed Agent’s activities. In the introduced approach, the more details about environment context would cause the less activity recognition process complexity and more precise functionality. To represent the knowledge, we apply the fuzzy logic to represent the world state by the fuzzified received values from sensors. The knowledge would be represented in the fuzzy context frame. To reduce the amount of collected data, meaningful changes in sensors generated values are considered to do Activity Recognition. Applying possibility distributions for event occurrence orders and sequences within different scenarios of activities realization, we are able to generate hypotheses about future possible occur-able events. The possible occur-able events and fuzzy digit parameters of their possible happening moments are represented in matrix format. The hypotheses about possible future observable contexts are generated considering spatial, temporal and other environmental parameters and then they would be ranked. Our final goal is to better explain the observations. If no possible explanation about observation be found, it would be recognized as abnormal behavior. In the case that no expected event be observed, we can reason that maybe event has occurred but not triggered and so next available events in previously learned scenarios would be expected. The system patience for number of possible missed events depends to trade-off between the degrees of resident's forgetfulness and probability of events trigger by applied sensors.

The growth of health literacy and patient empowerment movements has resulted in a more active and prominent role for patients as autonomous actors in decisions relating to their health. The Internet has become an important source of information for patients seeking to understand their health conditions and to evaluate possible treatments. However, in making autonomous healthcare decisions, the Internet can be viewed by patients as a decision support system. The Internet is poorly adapted to this task and may lead patients to make hasty, ill-informed, and even dangerous health choices. It is important, therefore, to guide patients to approach the Internet with appropriate skepticism and to temper their perceptions of autonomy.

This paper describes PATSy, an online repository of virtual patient cases for training and research for >students and practitioners in the clinical sciences. A typical student session with PATSy is illustrated. An extension to PATSy that adds vicarious learning resources (VL-PATSy) is also described. The concept of vicarious learning is outlined and results from a study of learning outcomes from VL-PATSy are presented. PATSy and VL-PATSy will be demonstrated at the symposium.

Scientific advances over the past 150 years, particularly in the medical field, have allowed the extension of life expectancy in western countries and this trend seems to increase in future years. Conservative estimates suggest that by 2030 in EU countries the proportion of people over 60 years regard the entire population will be around 50%; this means that we will see a gradual increase in the number of those subjects with chronic diseases (ie diseases not involving healing), that will therefore increase the cost and effort over health care facilities [1]. As consequence of the exponential growth of hardware and software infrastructure it is possible to rethink the whole approach to the treatment of complex chronic disease, by limiting the hospitalization only to a severe worsening of patient's condition. This was the original idea behind the CHRONIOUS project: constructing a generic platform to monitor, in an unobtrusive way, a chronic disease patient with two goals[2]: - Improve the patients quality of life, by reducing as much as possible the hospitalizations.