Emergency Navigation in Confined Spaces Using Dynamic Grouping

Bi, Huibo; Akinwande, Olumide J.; Gelenbe, Erol

doi:10.1109/ngmast.2015.12

Cited by 3 publications

(3 citation statements)

References 22 publications

(21 reference statements)

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“…TITLE-ABS-KEY( ("reinforcement learning" OR "contextual bandit") AND ("personalization" OR "personalized" OR "personal" OR "personalisation" OR "personalised" OR "customization" OR "customized" OR "customised" OR "customised" OR "individualized" OR "individualised" OR "tailored")) Table 7 Table containing all included publications. The first column refers to the data items in Table 2 # Value Publications 1 n [1,4,10,11,13,16,[18][19][20][24][25][26][27][28][29]31,32,35,36,38,[40][41][42][43][44][45]48,49,52,56,63,66,68,70,74,82,85,86,88,91,93,94,99,101,104,[106]…”

Section: Den Hengst Et Al / Reinforcement Learning For Personalizatimentioning

confidence: 99%

Reinforcement learning for personalization: A systematic literature review

Hengst

Grua

Hassouni

et al. 2020

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The major application areas of reinforcement learning (RL) have traditionally been game playing and continuous control. In recent years, however, RL has been increasingly applied in systems that interact with humans. RL can personalize digital systems to make them more relevant to individual users. Challenges in personalization settings may be different from challenges found in traditional application areas of RL. An overview of work that uses RL for personalization, however, is lacking. In this work, we introduce a framework of personalization settings and use it in a systematic literature review. Besides setting, we review solutions and evaluation strategies. Results show that RL has been increasingly applied to personalization problems and realistic evaluations have become more prevalent. RL has become sufficiently robust to apply in contexts that involve humans and the field as a whole is growing. However, it seems not to be maturing: the ratios of studies that include a comparison or a realistic evaluation are not showing upward trends and the vast majority of algorithms are used only once. This review can be used to find related work across domains, provides insights into the state of the field and identifies opportunities for future work.

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Section: Den Hengst Et Al / Reinforcement Learning For Personalizatimentioning

confidence: 99%

Reinforcement learning for personalization: A systematic literature review

Hengst

Grua

Hassouni

et al. 2020

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“…In Gelenbe [33,34,36], similar point process models are used for communications with spintronics, while Wang and Gelenbe [81] use the RNN for smart routing in networks, as well as for building Software Defined Networks (Francois and Gelenbe [23]) that optimize quality of service (QoS). In Akinwande, Bi, and Gelenbe [6], Bi, Akinwande, and Gelenbe [13], Bi and Gelenbe [14], the RNN issued for smart routing of evacuees in emergencies, while Abdelrahman and Gelenbe [4] studied the movement of individuals or animals in a random environment.…”

Section: Introductionmentioning

confidence: 99%

Random Neural Network Learning Heuristics

Javed

Larijani

Ahmadinia

et al. 2017

Prob. Eng. Inf. Sci.

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The random neural network (RNN) is a probabilitsic queueing theory-based model for artificial neural networks, and it requires the use of optimization algorithms for training. Commonly used gradient descent learning algorithms may reside in local minima, evolutionary algorithms can be also used to avoid local minima. Other techniques such as artificial bee colony (ABC), particle swarm optimization (PSO), and differential evolution algorithms also perform well in finding the global minimum but they converge slowly. The sequential quadratic programming (SQP) optimization algorithm can find the optimum neural network weights, but can also get stuck in local minima. We propose to overcome the shortcomings of these various approaches by using hybridized ABC/PSO and SQP. The resulting algorithm is shown to compare favorably with other known techniques for training the RNN. The results show that hybrid ABC learning with SQP outperforms other training algorithms in terms of mean-squared error and normalized root-mean-squared error.

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“…These higher-order specifications can then be compiled into the formalisms used in this thesis such as LTL and FSTs. [59,97,202,349] Transport [24,32,58,107,155,257,270,329,390] A…”

Section: Subsymbolic Rl and Symbolic Knowledgementioning

confidence: 99%

Learning to Behave

den Hengst

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Reinforcement learning (RL) has recently attracted significant attention with applications such as improving microchip designs, predicting the behaviour of protein structures and beating humanity’s best in the games of go, chess and Starcraft-II. These impressive and inspring successes show how RL can improve our lives, however, they have so far been seen mostly in settings that involve humans to a very limited extend. This thesis looks into the usage of RL in human contexts. First, we provide a systematic literature review of the usage of RL for personalisation, i.e. the adaptation of systems to individuals. Next, we show how RL can be used to personalise a conversational recommender system and find that it outperforms existing approaches, including a gold-standard and task-specific solutions in a simulation-based study. Since simulators may not be available for all conversational systems that could benefit from personalisation, we next look into the collection of user satisfaction ratings for dialogue data. We consolidate best practices in a UI for user satisfaction annotation and show that high-quality ratings can be obtained. Next, we look into the usage of RL for strategic workforce planning. Here, we find that RL is robust to the uncertainties that are an inherent part of this problem and that RL enables the specification of goals intuitive to domain experts. Having looked into these use-cases, we then turn toward the inclusion of safety constraints in RL. We propose how safety constraints from a medical guideline can be taken into account in an observational study on the optimisation of ventilator settings for ICU patients. Next, we look into safety constraints that contain a temporal component, we find that these may make the learning problem infeasible and propose a solution based on reward shaping to address this issue. Finally, we propose how RL can benefit from instructions that break a full task into smaller pieces based on the option framework and propose an approach for learning reusable behaviours from instructions to greatly reduce data requirements.

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Emergency Navigation in Confined Spaces Using Dynamic Grouping

Cited by 3 publications

References 22 publications

Reinforcement learning for personalization: A systematic literature review

Reinforcement learning for personalization: A systematic literature review

Random Neural Network Learning Heuristics

Learning to Behave

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