Context-Aware Generation and Adaptive Execution of Daily Living Care Pathways

Sánchez-Garzón, Inmaculada; Milla-Millán, Gonzalo; Fernández-Olivares, Juan

doi:10.1007/978-3-642-35395-6_49

Cited by 5 publications

(7 citation statements)

References 8 publications

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“…Given context information, the most common approach taken by studies, such as [1], [9], [10], [11], [12], is to invoke a planning algorithm by an explicit user request, and to plan without considering the possibilities to reduce power consumption. For example, Kaldeli et al [1] encode the planning problem as a Constraint Satisfaction Problem (CSP) in which the problem description contains context information and a declarative goal issued either by a user or by a rulebased engine.…”

Section: Related Workmentioning

confidence: 99%

Combining Activity Recognition and AI Planning for Energy-Saving Offices

Georgievski

Nguyen

Aiello

2013

2013 IEEE 10th International Conference on Ubiquitous Intelligence and Computing and 2013 IEEE 10th International Conference On

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Energy-saving offices require autonomous and optimised control of integrated devices and appliances with the objective of saving energy while the occupant comfort and productivity are preserved. We propose an approach that analyses and controls an office space and accounts for the objectives of energy-saving offices. The approach considers ontology-based occupant activity recognition using simple sensors to process the context information, and employs Artificial Intelligence planning to control appliances. The approach is evaluated in a semisimulated setting. The activity recognition strategy is tested in an actual living lab and shows recognising accuracy of about 80%. The planning technique is able to cope efficiently under a simulated and increasing number of offices and recognised activities. The overall solution shows intriguing potential for energy saving in the order of 70%, given mostly sunny days and a provisional set of devices for experimentation.

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Section: Related Workmentioning

confidence: 99%

Combining Activity Recognition and AI Planning for Energy-Saving Offices

Georgievski

Nguyen

Aiello

2013

2013 IEEE 10th International Conference on Ubiquitous Intelligence and Computing and 2013 IEEE 10th International Conference On

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“…Preferences. A user may select and customise recipes [Kotsovinos and Vukovic 2005], indicate personalised choices on various devices [Ding et al 2006;Fraile et al 2013], and provide preferences on services [Liang et al 2010;Song and Lee 2013;Chen et al 2014], daily activities [Mastrogiovanni et al 2010;Vaquero et al 2015;Köckemann et al 2014], domains Yau and Buduru 2014], treatments [S ánchez-Garzón et al 2012], timetables ], companion systems [Honold et al 2014], and products ]. In Ranganathan and Campbell [2004], user preferences are in the form of utility u for each predicate in different contexts, where u ∈ [−10, 10].…”

Section: Review Of Primary Studiesmentioning

confidence: 99%

“…Human Actions. Human actions are primary entities in the domain of assisted living for implementing applications for care giving [Courtemanche et al 2008;Hidalgo et al 2011;S ánchez-Garzón et al 2012], cooking [Kotsovinos and Vukovic 2005;Sando and Hishiyama 2011;Ortiz et al 2013], shopping , training [Bacon et al 2013], and system assembling [Honold et al 2014]. It is practically difficult to extract a generalisation of human actions because of the diversity of adopted models and the lack of representation details (for examples of human actions encoded in PDDL, we refer to Appendix B.2).…”

Section: Review Of Primary Studiesmentioning

confidence: 99%

“…The majority of classified studies use PDDL as a modelling syntax for their domains. Several studies make use of HTN-based languages, such as the SHOP2 language in Song and Lee [2013] and Ha et al [2005], and Hierarchical Planning Definition Language in S ánchez- Garzón et al [2012] and Georgievski et al [2013]; and two studies employ the Action Description Language [Kotsovinos and Vukovic 2005;Vukovic et al 2007]. Others use nonplanning modelling languages, such as SMIL [Ding et al 2006], Scone [Santofimia et al 2010], XML [Kaldeli et al 2012;Harrington and Cahill 2011], OWL-S , and PRISM [Sukkerd et al 2015].…”

Section: :25mentioning

confidence: 99%

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Automated Planning for Ubiquitous Computing

Georgievski

Aiello

2016

ACM Comput. Surv.

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The goal of ubiquitous computing is to create ambience in which one’s experiences and quality of life are improved by monitoring and assisting people using ubiquitous technologies and computation in coherence. The continuous advancements of involved technologies, such as wireless communications, mobile devices, and sensors, imply fast evolution of ubiquitous computing environments too. The complexity of these environments is reaching a point where traditional solutions simply no longer work. The environments are in need of computational techniques that can deal with the evolution and uncertainty of ubiquitous computing environments dynamically and automatically. Artificial Intelligence (AI) can contribute towards satisfying this future scenario in many ways, while numerous approaches inspired by work in the AI planning community have already been designed for ubiquitous computing. We devote this study to investigate the current progress of AI planning for ubiquitous computing by analysing those approaches. We rigorously search for and select relevant literature out of which we extract qualitative information. Using the extracted qualities, we derive a generic framework that consists of aspects important to planning for ubiquitous computing. The framework’s main purpose is to facilitate the understanding of those aspects, and classify the literature according to them. We then analyse the literature in a consolidated way, and identify future challenges of planning for ubiquitous computing.

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“…For example, reasoning in (Viterbo and Endler, 2012) is distributed in that different computational nodes cooperate to infer a global context state; the reasoning process is however limited to ontology-based inference, and is not resilient to inaccurate or conflicting information. Finally, all existing knowledge-based reasoning approaches assume a single direction information flow: from sensor input, through to data analysis and decision-making, and then to reaction and control, irrespective of the extent to which each of these phases are decentralised (Snchez-Garzn et al, 2012;Valero et al, 2013). …”

Section: Research Challengesmentioning

confidence: 99%

Collaborative Explanation and Response in Assisted Living Environments Enhanced with Humanoid Robots

Bikakis

Caire

Clark

et al. 2016

Proceedings of the 8th International Conference on Agents and Artificial Intelligence

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Abstract:An ageing population with increased social care needs has provided recent impetus for research into assisted living technologies, as the need for different approaches to providing supportive environments for senior citizens becomes paramount. Ambient intelligence (AmI) systems are already contributing to this endeavour. A key feature of future AmI systems will be the ability to identify causes and explanations for changes to the environment, in order to react appropriately. We identify some of the challenges that arise in this respect, and argue that an iterative and distributed approach to explanation generation is required, interleaved with directed data gathering. We further argue that this can be realised by developing and combining state-of-the art techniques in automated distributed reasoning, activity recognition, robotics, and knowledge-based control.

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Context-Aware Generation and Adaptive Execution of Daily Living Care Pathways

Cited by 5 publications

References 8 publications

Combining Activity Recognition and AI Planning for Energy-Saving Offices

Combining Activity Recognition and AI Planning for Energy-Saving Offices

Automated Planning for Ubiquitous Computing

Collaborative Explanation and Response in Assisted Living Environments Enhanced with Humanoid Robots

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