Developments in sophisticated mobile physiological sensors have presented many novel opportunities for monitoring coaching of individuals. In this work, we investigate the ability to utilize physiological data to recognize the state ofa user while exercising. We discuss recognition of user state using data suchas heart rate, respiration rate, and activity level. We also discuss the development of a motivational agent which utilizes the physiological data to help encourage a user during an exercise routine.

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.

Organizational agility is a key challenge in today's business world. The Knowledge-Intensive Service Support approach tackles agility by combining process modeling and business rules. In the paper at hand, we present five approaches of process mining that could further increase the agility of processes by improving an existing process model.

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.

We consider the problem of assessing the sustainability of alternative designs (e.g., for an urban environment) that are assembled from multiple components (e.g., water supply, transportation system, shopping centers, commercial spaces, parks). We model the sustainability of a design in terms of a set of sustainability attributes. Given the (qualitative) preferences and tradeoffs of decision makers over the sustainability attributes, we formulate the problem of identifying sustainable designs as the problem of finding the most preferred designs with respect to those preferences. We show how techniques for representing and reasoning with qualitative preferences can be used to identify the most preferred designs based on the decision maker’s stated preferences and tradeoffs.

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

Sustainability requires emphasizing the importance of environmental causes and effects among design knowledge from heterogeneous stakeholders to make a sustainable decision. Recently, such causes and effects have been well developed in ontological representation, which has been challenged to generate and integrate multiple domain knowledge due to its domain specific characteristics. Moreover, it is too challengeable to represent heterogeneous, domain-specific design knowledge in a standardized way. Causal knowledge can meet the necessity of knowledge integration in domains. Therefore, this paper aims to develop a causal knowledge integration system with the authors’ previous mathematical causal knowledge representation.

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.