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.

We formalize actions and change in a housekeeping domain with multiple cleaning robots, and commonsense knowledge about this domain, in the action language C+. Geometric reasoning is lifted to high-level representation by embedding motion planning in the domain description using external predicates. With such a formalization of the domain, a plan can be computed using the causal reasoner CCalc for each robot to tidy some part of the house. We introduce a planning and monitoring algorithm for safe execution of these plans, so that it can recover from plan failures due to collision with movable objects whose presence and location are not known in advance or due to heavy objects that cannot be lifted alone. We illustrate the applicability of this algorithm with a simulation of a housekeeping domain.

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.

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.

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.

Within the context of human/multi-robot teams, the "help me help you" paradigm offers different opportunities. A team of robots can help a human operator accomplish a goal, and a human operator can help a team of robots accomplish the same, or a different, goal. Two scenarios are examined here. First, a team of robots helps a human operator search a remote facility by recognizing objects of interest. Second, the human operator helps the robots improve their position (localization) information by providing quality control feedback.

Most of the developing countries are re-inventing the wheel in their efforts to launch egovernment initiatives — especially in the areas of healthcare, education, economic development, supply chains for food distribution, and emergency services. A Computer Aided Strategic Planner, part of the UN eNabler Toolset, has been developed to quickly and effectively produce detailed strategic plans for a wide range of egovernment services based on best practices and standards. The generated plan is highly customized for the type of service as well as the country/region by using the latest thinking in AI, ontologies, and patterns. The Planner, available through the UN-GAID initiative, can be and has been used very effectively to educate as well as assist the government officials of developing countries to accelerate progress in crucial areas.

We are developing an ontology of microsocial concepts for use in an instructional system for teaching cross-cultural communication. We report here on that part of the ontology relating to interpersonal relationships. We first explicate the key concepts of commitment, shared plans, and good will. Then in terms of these we present a formal account of the host-guest relationship.

Vast volumes of information from public Web portals are readily accessible from virtually any computer in the world. This can be seen as an enormous repository of information which brings significant business value for companies working in e-commerce activities. However, the main problems encountered when using this information are: (I) the information is published in various, non-machine-processable formats, (II) a lack of services that match and store information from various sources in a homogenous structure, and (III) the accessible datasets are rarely provided with e-commerce concepts in mind. These problems make them difficult to use by e-commerce applications. The main goal of this paper is to propose a methodology and analysis of components required for combining and integrating information into machine-processable dataset from different Web data sources, based on suitable e-commerce ontology. In order to demonstrate proposed methodology, the process of wrapping and matching the data from two public datasets will be discussed as an example.

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.