B-SMART: A Reference Architecture for Artificially Intelligent Autonomic Smart Buildings

Towards autonomic orchestration of machine learning pipelines in future networks

Machine learning (ML) techniques are being increasingly used in mobile networks for network planning, operation, management, optimisation and much more. These techniques are realised using a set of logical nodes known as ML pipeline. A single network operator might have thousands of such ML pipelines distributed across its network. These pipelines need to be managed and orchestrated across network domains. Thus it is essential to have autonomic multi-domain orchestration of ML pipelines in mobile networks. International Telecommunications Union (ITU) has provided an architectural framework for…

A Electric Network Reconfiguration Strategy with Case-Based Reasoning for the Smart Grid

The complexity, heterogeneity and scale of electrical networks have grown far beyond the limits of exclusively human-based management at the Smart Grid (SG). Likewise, researchers cogitate the use of artificial intelligence and heuristics techniques to create cognitive and autonomic management tools that aim better assist and enhance SG management processes like in the grid reconfiguration. The development of self-healing management approaches towards a cognitive and autonomic distribution power network reconfiguration is a scenario in which the scalability and on-the-fly computation are issue…

On Evaluating Power Loss with HATSGA Algorithm for Power Network Reconfiguration in the Smart Grid

This paper presents the power network reconfiguration algorithm HATSGA with a "R" modeling approach and evaluates its behavior in computing new reconfiguration topologies for the power network in the Smart Grid context. The modeling of the power distribution network with the language "R" is used to represent the network and support the computation of distinct algorithm configurations towards the evaluation of new reconfiguration topologies. The HATSGA algorithm adopts a hybrid Tabu Search and Genetic Algorithm strategy and can be configured in different ways to compute network reconfiguration …

最小化智能建筑能源优化中强化学习风险的迁移学习方法

A Transfer Learning Approach to Minimize Reinforcement Learning Risks in Energy Optimization for Smart Buildings

本文提出ReLBOT技术，通过迁移学习与深度强化学习结合，将既有优化建筑的知识迁移至新智能建筑，有效减少RL智能体预热期带来的不适风险，实现预热时长缩短6.2倍、预测方差降低132倍的显著提升。

Asymptotically Unambitious Artificial General Intelligence

General intelligence, the ability to solve arbitrary solvable problems, is supposed by many to be artificially constructible. Narrow intelligence, the ability to solve a given particularly difficult problem, has seen impressive recent development. Notable examples include self-driving cars, Go engines, image classifiers, and translators. Artificial General Intelligence (AGI) presents dangers that narrow intelligence does not: if something smarter than us across every domain were indifferent to our concerns, it would be an existential threat to humanity, just as we threaten many species despite…

奖励是不够的：我们能否将人工智能从强化学习范式中解放出来？

Reward is not enough: can we liberate AI from the reinforcement learning paradigm?

本文反驳奖励最大化足以解释智能的观点，指出强化学习范式不完整，并强调其对开发安全稳健AI的深远影响。