List of organizers of the Logical Formalizations of Commonsense Reasoning AAAI symposium.

Applications range from the detection of potential all times t 1,...T (m 1)τ. We refer to such a sequence problems (such as an elderly person who has fallen down as a model of the system. Note that these models are in their home) to general monitoring of disease progression nonparametric. Theoretically, under some smoothness assumptions (e.g. in Parkinson's disease), or simply tracking the amount (Takens, 1981), if m is big enough, and τ is not of exercise and physical activity that a person gets. Ideally, a multiple of the period of the system, such a model captures such activities should be monitored as precisely as possible, all the relevant dynamics. However, real data is noisy, but using cheap or easily available devices, and in a way that so nonparametric models of the same activity can have high does not interfere with daily life.

Our research is directed towards agile supply chains enabling enterprises to quickly respond to individual customer demand. From this perspective, agility encompasses three dimensions of adaptivity: space, time, and economy. Supply chain agility can be achieved by exploiting the most fundamental resource of any enterprise: knowledge. Studying supply chains, we regard all their tiers, participants, and potential relationships, as the search space for fulfilling individual customer demand. We study supply chains from a knowledge-based coordination perspective and regard logistics as the guiding conceptualization. The contribution of this research is a logistics knowledge infrastructure. We report about applying parts of this infrastructure to coordination problems in three selected case studies.

Detecting and characterizing emerging topics of discussion and consumer trends through analysis of Internet data is of great interest to businesses. This paper considers the problem of monitoring the Web to spot emerging memes – distinctive phrases which act as “tracers” for topics – as a means of early detection of new topics and trends. We present a novel methodology for predicting which memes will propagate widely, appearing in hundreds or thousands of blog posts, and which will not, thereby enabling discovery of significant topics. We begin by identifying measurables which should be predictive of meme success. Interestingly, these metrics are not those traditionally used for such prediction but instead are subtle measures of meme dynamics. These metrics form the basis for learning a classifier which predicts, for a given meme, whether or not it will propagate widely. The utility of the prediction methodology is demonstrated through analysis of a sample of 200 memes which emerged online during the second half of 2008.

The cost-effectiveness and accuracy of a multidisciplinary design optimization (MDO) process is highly dependent on designers’ ability to flexibly formulate the optimization problem for specific challenges. Designers need to rapidly modify how object parameters are assigned to groupings of objects in the product model. Our research has developed a Reference-Based Optimization Method (RBOM) to enable this type of flexible problem formulation. However, the responsibility still falls on the designer to manage the problem formulation and MDO process, which can lead to inefficient and costly design decisions. By means of artificial intelligence, in particular knowledge-based systems, these potential barriers to MDO adoption in the Architecture, Engineering, and Construction (AEC) industry could be mitigated, resulting in more efficient design processes and, ultimately, energy-efficient built environments.

A method is introduced to incorporate sustainability considerations in the early design stages, while simultaneously accounting for supply chain factors, such as cost and lead time. Overall, this work is our first step in understanding the trade-offs between sustainability metrics and more traditional supply chain performance metrics (i.e., cost and lead time). Based on our understanding of these trade-offs, we intend to help build computational artificial intelligence tools that can exploit these trade-offs for improved customization in produc

One of the challenges in accurately applying metrics for life cycle assessment lies in accounting for both irreducible and inherent uncertainties in how a design will perform under real world conditions. This paper presents a preliminary study that compares two strategies, one simulation-based and one set-based, for propagating uncertainty in a system. These strategies for uncertainty propagation are then aggregated. This work is conducted in the context of an amorphous photovoltaic (PV) panel, using data gathered from the National Solar Radiation Database, as well as realistic data collected from an experimental hardware setup specifically for this study. Results show that the influence of various sources of uncertainty can vary widely, and in particular that solar radiation intensity is a more significant source of uncertainty than the efficiency of a PV panel. This work also shows both set-based and simulation-based approaches have limitations and must be applied thoughtfully to prevent unrealistic results. Finally, it was found that aggregation of the two uncertainty propagation methods provided faster results than either method alone.

Sustainable building design is a complex social and technical process in which a broad range of stakeholders must construct and clearly communicate high quality design spaces. This paper summarizes recent assessments of current practice that illustrate how far industry today is from achieving this quality and clarity. Efforts to develop a platform of tools to address these limitations are discussed. PIP helps people communicate, share, and understand collaborative design processes; MACDADI helps project teams identify and manage rationale and consensus on decisions; Design Scenarios helps them generate requirements-driven alternative spaces, BIM, model-based analysis, and PIDO which helps to systematically assess these alternatives for their energy, daylight, structural, and cost impacts; and iRooms and the web, which help to communicate all of this information to engage designers, stakeholders, and decision makers in fast, multidisciplinary design and analysis processes. This new platform considerably improves the quality and clarity of AEC design spaces. However additional work would enable significant additional improvement. The paper concludes with a proposal for how AI might further improve the performance of this platform.

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.