By Liming Chen, Chris D. Nugent, Jit Biswas, Jesse Hoey
This publication includes a few chapters addressing various points of job acceptance, approximately in 3 major different types of subject matters. the 1st subject can be fascinated about task modeling, illustration and reasoning utilizing mathematical types, wisdom illustration formalisms and AI options. the second one subject will be aware of task popularity equipment and algorithms. except conventional tools in line with information mining and laptop studying, we're fairly drawn to novel techniques, resembling the ontology-based method, that facilitate info integration, sharing and automatic/automated processing. within the 3rd subject we intend to hide novel architectures and frameworks for task reputation, that are scalable and acceptable to giant scale allotted dynamic environments. moreover, this subject also will comprise the underpinning technological infrastructure, i.e. instruments and APIs, that helps function/capability sharing and reuse, and quick improvement and deployment of technological options. The fourth type of subject may be devoted to consultant functions of job reputation in clever environments, which handle the lifestyles cycle of job attractiveness and their use for novel features of the end-user structures with accomplished implementation, prototyping and evaluate. this may comprise a variety of program situations, akin to shrewdpermanent houses, clever convention venues and cars.
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Additional resources for Activity Recognition in Pervasive Intelligent Environments
1, pp. 55–81. , (2005), The gator tech smart house: a programmable pervasive space, IEEE Computer, Vol. 38, No. 3, pp. 64–74. Pattern Analysis and Machine Intelligence, Vol. 28, No. 10, pp. 1553–1567. , (2005), A Decision-Theoretic Approach to Task Assistance for Persons with Dementia, In Proc. of the International Joint Conference on Artificial Intelligence (IJCAI’05), pp. 1293–1299. , (2007), Common Sense Based Joint Training of Human Activity Recognizers, In Proc. International Joint Conference on Artificial Intelligence.
To recognise fine-grained personalised ADLs, an assistive agent needs to compare the perceived sensorised contextual information with the specified property values of the instances in the Instance pane. Fig. 5 shows the main properties of the UserA_Preferred_Tea ADL. In comparison with the perceived context of cup, hot water, location and teabag, an assistive agent can recognise that the user is making a Chinesetea with skimmed milk and sugar along with a China Activity Recognition: Approaches, Practices and Trends 21 Fig.
IEEE Trans. Knowl. , 20(8):1082–1090, 2008. Chapter 2 A Possibilistic Approach for Activity Recognition in Smart Homes for Cognitive Assistance to Alzheimer’s Patients Patrice C. ca Abstract Providing cognitive assistance to Alzheimer’s patients in smart homes is a field of research that receives a lot of attention lately. The recognition of the patient’s behavior when he carries out some activities in a smart home is primordial in order to give adequate assistance at the opportune moment. To address this challenging issue, we present a formal activity recognition framework based on possibility theory and description logics.