Contextualised Ambient Intelligence Through Case-Based Reasoning

Kofod-Petersen, Anders; Aamodt, Agnar

doi:10.1007/11805816_17

Cited by 34 publications

(35 citation statements)

References 17 publications

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“…So, speculative reasoning is a compromise approach that prevents agents from idling or expending agents' computation power doing unnecessary computation while waiting for the answers. Petersen [28] has proposed to solve the reasoning problem in ambient intelligence through case-based reasoning, and has shown how different concepts for reasoning and modeling can be combined. However, as pointed out in [29] his approach may suffer from maintaining the potentially very large case base, which is a risk when running in an on-line manner.…”

Section: Discussionmentioning

confidence: 99%

Distributed Defeasible Speculative Reasoning in Ambient Environment

Lam

Governatori

Satoh

et al. 2012

Lecture Notes in Computer Science

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Abstract. Speculative Computation is an effective means for solving problems with incomplete information in an open and distributed environment, such as peer-to-peer environment. It allows such a system to compute tentative (and possibly final) solutions using default knowledge about the current environment, or the agent's perception, even if the communications between peers are delayed or broken. However, previous work in speculative reasoning assumed that agents are hierarchically structured, which may not be the case in reality. We propose a more general multi-agents system with no centralized control. Agents in the framework have equivalent functionalities and can collaborate with each other to achieve their common goals. We characterize the framework using the argumentation semantics of defeasible logic, which provides support of speculative reasoning in the presence of conflicting information. We provide an operational model for the framework and present a prototype implementation of the model.

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Section: Discussionmentioning

confidence: 99%

Distributed Defeasible Speculative Reasoning in Ambient Environment

Lam

Governatori

Satoh

et al. 2012

Lecture Notes in Computer Science

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“…There are also other learning algorithms that can do clustering but do not label them (Unsupervised Learning). Consequently, the major disadvantage is that learning can take a lot of human effort and time (several months [22]). …”

Section: Study Of the Path Between The Observables And The Predicatesmentioning

confidence: 99%

Model of a Context-Aware Middleware for Mobile Workers

Moustafa¹,

Rey

Lavirotte

et al. 2017

JCC

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With the development of Internet of things and Web of things, computing becomes more pervasive, invisible and present everywhere. In fact, in our environment, we are surrounded by multiple devices that deliver (web) services which meet the needs of the users. However, the mobility of these devices as the users has important repercussions that challenge software design of these applications because the variability of the environment cannot be anticipated at the design time. Thus, it will be interesting to dynamically discover the environment and adapt the application during its execution to the new contextual conditions. We therefore, propose a model of a context-aware middleware that can address this issue through a monitoring service which is capable of reasoning and observation channels capable of calculating the context during the runtime. The monitoring service evaluates the pre-defined X-Query predicates in the context manager and uses Prolog to deduce the services needed to respond back. An independent observation channel for each different predicate is then dynamically generated by the monitoring service depending on the current state of the environment. Each channel sends its result directly to the context manager which consequently calculates the context based on all the predicates' results while preserving the reactivity of the selfadaptive system.

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“…In this case, the goal is to gather the relevant information. This goal is sent to the sensitivity part to be solved [21].…”

Section: Examplementioning

confidence: 99%

Modelling with Problem Frames: Explanations and Context in Ambient Intelligent Systems

Kofod-Petersen

Cassens

2011

Lecture Notes in Computer Science

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Abstract. When designing and implementing real world ambient intelligent systems we are in need of applicable information systems engineering methods. The tools we find in the intelligent systems area focus on the knowledge engineering parts, whereas traditional software engineering techniques are usually not designed with the peculiarities of intelligent systems design in mind. This holds in particular for explanation-aware intelligent systems. This work looks at problem frames for explanations and investigates how problem frames can be used to elicit, analyse, and specify these specific requirements. The point of departure is an existing ambient intelligent information system for the hospital ward domain. The work presented here analyses how such a system can be redesigned with a focus on explanation-awareness.

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Contextualised Ambient Intelligence Through Case-Based Reasoning

Cited by 34 publications

References 17 publications

Distributed Defeasible Speculative Reasoning in Ambient Environment

Distributed Defeasible Speculative Reasoning in Ambient Environment

Model of a Context-Aware Middleware for Mobile Workers

Modelling with Problem Frames: Explanations and Context in Ambient Intelligent Systems

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