--Model-driven engineering problems often require complex model transformations (MTs), i.e., MTs that are chained in extensive sequences. Pertinent examples of such problems include model synchronization, automated model repair, and design space exploration. Manually developing complex MTs is an error-prone and often infeasible process. Reinforcement learning (RL) is an apt way to alleviate these issues. In RL, an autonomous agent explores the state space through trial and error to identify beneficial sequences of actions, such as MTs. In these situations, human guidance can be of high utility. In this paper, we present an approach and technical framework for developing complex MT sequences through RL, guided by potentially uncertain human advice. Our framework allows user-defined MTs to be mapped onto RL primitives, and executes them as RL programs to find optimal MT sequences. Our evaluation shows that human guidance, even if uncertain, substantially improves RL performance, and results in more efficient development of complex MTs. Through a trade-off between the certainty and timeliness of human advice, our method takes a step towards RL-driven human-in-the-loop engineering methods. Modeling activities are often more complex than an atomic model transformation (MT) and rely on sequences of MTs . Pertinent examples can be found in model synchronization [1], model refactoring [2], and rule-based design-space exploration [3]. Typically, there might be more than one MT sequence that can successfully transform the source model into the target state, and choosing the most appropriate (cost-effective, efficient, safe) one manually is not tractable. This raises the need for automated methods for developing complex MTs, in which MTs are chained in sequences.

Human guidance is often desired in reinforcement learning to improve the performance of the learning agent. However, human insights are often mere opinions and educated guesses rather than well-formulated arguments. While opinions are subject to uncertainty, e.g., due to partial informedness or ignorance about a problem, they also emerge earlier than hard evidence could be produced. Thus, guiding reinforcement learning agents through opinions offers the potential for more performant learning processes, but comes with the challenge of modeling and managing opinions in a formal way. In this article, we present a method to guide reinforcement learning agents through opinions. To this end, we provide an end-to-end method to model and manage advisors' opinions. To assess the utility of the approach, we evaluate it with synthetic and human advisors, at different levels of uncertainty, and under multiple advise strategies. Our results indicate that opinions, even if uncertain, improve the performance of reinforcement learning agents, resulting in higher rewards, more efficient exploration, and a better reinforced policy. Although we demonstrate our approach in a simplified topological running example, our approach is applicable to complex problems with higher dimensions as well.

Despite the promising results achieved, state-of-the-art interactive reinforcement learning schemes rely on passively receiving supervision signals from advisor experts, in the form of either continuous monitoring or pre-defined rules, which inevitably result in a cumbersome and expensive learning process. In this paper, we introduce a novel initiative advisor-in-the-loop actor-critic framework, termed as Ask-AC, that replaces the unilateral advisor-guidance mechanism with a bidirectional learner-initiative one, and thereby enables a customized and efficacious message exchange between learner and advisor. At the heart of Ask-AC are two complementary components, namely action requester and adaptive state selector, that can be readily incorporated into various discrete actor-critic architectures. The former component allows the agent to initiatively seek advisor intervention in the presence of uncertain states, while the latter identifies the unstable states potentially missed by the former especially when environment changes, and then learns to promote the ask action on such states. Experimental results on both stationary and non-stationary environments and across different actor-critic backbones demonstrate that the proposed framework significantly improves the learning efficiency of the agent, and achieves the performances on par with those obtained by continuous advisor monitoring.

The general consensus appears to be leaning towards the idea that artificial intelligence can replace the role of human financial advisors and therefore, those in the industry must adapt or risk getting left behind. But before jumping to that conclusion, it's worth exploring some important questions: what's next, what is needed and who needs it? And, perhaps crucially, whether AI will ever remove the need for human advisors in the financial industry. AI transforming financial sector Business leaders have revealed that the use of technology including AI plays a significant role in filling gaps within financial services offerings. Jim Pendergast, Senior Vice President and General Manager at AltLINE by The Southern Bank, has said that AI can improve the consistency of financial advice.

A new study published in Economic Inquiry is the first to assess the willingness of consumers to adopt advisory services in the banking sector that are based on artificial intelligence (AI). Investigators examined whether the likelihood that consumers adopt AI in banking services depends on tastes for human interaction across different cultures. The study focused on robo-advisory services, which are automated investment platforms that provide investment advice without the intervention of a human advisor. When investigators analyzed an ING Bank dataset encompassing 11,000 respondents from 11 countries, they found that different attitudes across cultures shape differences in local consumers' likelihood of adopting robo-advisory services. The analysis revealed that robo-advisory services may be adopted where they compensate for feelings of a lack of trust and reliability towards human advisors among consumers.

Artificial intelligence thrives with data. The more data you have, the better your algorithms will be. However, just having a lot of data is not sufficient anymore. "We don't have better algorithms, we just have more data. More data beats clever algorithm, but better data beats more data."

Ilker is Co-founder of Botanalytics, conversational analytics company based in San Francisco.

Artificial intelligence thrives with data. The more data you have, the better your algorithms will be. However, just having a lot of data is not sufficient anymore. Nowadays, most organisations collect vast troves of data, but especially the financial sector is well-suited for also collecting high-quality data. Simply because of regulations and because a lot of data in the financial sector is structured data.

Artificial intelligence thrives with data. The more data you have, the better your algorithms will be. However, just having a lot of data is not sufficient anymore. "We don't have better algorithms, we just have more data. More data beats clever algorithm, but better data beats more data."

You may have to pay to speak to a real person when you agree to hybrid human-robo management. Technically, you are always in charge of your finances, but you may not be willing to hand over your portfolio's reigns to a robot. A robo-advisor may not be a great fit if you want a more hands-on approach to online guidance. Even an algorithm is still the most sophisticated computer algorithm. It can't sit with you, it can't explain anything to you, and it can't listen to your future dreams.