This research considers Bayesian decision-analytic approaches toward the traversal of an uncertain graph. Namely, a traveler progresses over a graph in which rewards are gained upon a node's first visit and costs are incurred for every edge traversal. The traveler knows the graph's adjacency matrix and his starting position but does not know the rewards and costs. The traveler is a Bayesian who encodes his beliefs about these values using a Gaussian process prior and who seeks to maximize his expected utility over these beliefs. Adopting a decision-analytic perspective, we develop sequential decision-making solution strategies for this coupled information-collection and network-routing problem. We show that the problem is NP-Hard and derive properties of the optimal walk. These properties provide heuristics for the traveler's problem that balance exploration and exploitation. We provide a practical case study focused on the use of unmanned aerial systems for public safety and empirically study policy performance in myriad Erdos-Renyi settings.

Increasing the success rate of a process, i.e. the percentage of cases that end in a positive outcome, is a recurrent process improvement goal. At runtime, there are often certain actions (a.k.a. treatments) that workers may execute to lift the probability that a case ends in a positive outcome. For example, in a loan origination process, a possible treatment is to issue multiple loan offers to increase the probability that the customer takes a loan. Each treatment has a cost. Thus, when defining policies for prescribing treatments to cases, managers need to consider the net gain of the treatments. Also, the effect of a treatment varies over time: treating a case earlier may be more effective than later in a case. This paper presents a prescriptive monitoring method that automates this decision-making task. The method combines causal inference and reinforcement learning to learn treatment policies that maximize the net gain. The method leverages a conformal prediction technique to speed up the convergence of the reinforcement learning mechanism by separating cases that are likely to end up in a positive or negative outcome, from uncertain cases. An evaluation on two real-life datasets shows that the proposed method outperforms a state-of-the-art baseline.

Prescriptive process monitoring is a family of techniques to optimize the performance of a business process by triggering interventions at runtime. Existing prescriptive process monitoring techniques assume that the number of interventions that may be triggered is unbounded. In practice, though, specific interventions consume resources with finite capacity. For example, in a loan origination process, an intervention may consist of preparing an alternative loan offer to increase the applicant's chances of taking a loan. This intervention requires a certain amount of time from a credit officer, and thus, it is not possible to trigger this intervention in all cases. This paper proposes a prescriptive process monitoring technique that triggers interventions to optimize a cost function under fixed resource constraints. The proposed technique relies on predictive modeling to identify cases that are likely to lead to a negative outcome, in combination with causal inference to estimate the effect of an intervention on the outcome of the case. These outputs are then used to allocate resources to interventions to maximize a cost function. A preliminary empirical evaluation suggests that the proposed approach produces a higher net gain than a purely predictive (non-causal) baseline.

As the world grows increasingly connected, growing concern regarding the influence of artificial intelligence (AI) has been bubbling to the surface, affecting perceptions by industries big and small along with the general populace. Spurred on by sensationalized media predictions of AI taking over human decision-making and silver-screen tales of robot revolutions, there is a fear of allowing AI or its cousin, the Internet of Things (IoT), into our lives. Here is AI's man behind the curtain. One of the biggest sticking points is the popular – yet mistaken – notion that AI will cost people their jobs. In truth, the situation is just the opposite.

The World Economic Forum predicts that while some jobs will be eliminated due to Artificial Intelligence, many will change or be added, with a net gain. While on my morning commute, I noticed a bumper sticker in front of me read "Where are we going? And why am I in a handbasket?" With intelligent tech on my mind, my optimistic self instinctively responded: "No, we are not heading to our doom in a handbasket! We will soon live in a world where intelligent technologies will create jobs, clean the oceans, save lives and we'll all live happily ever after."

The capabilities of current AI systems go far beyond anything available only a few years ago, and levels of disruption are likely to skyrocket. How will AI transform jobs? And how will society respond? There have been a lot of studies on the subject recently. Let's unpack some of the findings.

Artificial intelligence (AI) could create more jobs than it displaces in Scotland over the next 20 years, according to a report. Research by professional services firm PwC suggested AI could create 558,000 Scottish posts by 2037. Over the same period 544,000 jobs could be lost as a result of automation - resulting in a net increase of 14,000. PwC said the new jobs could come from innovations such as drones, robotics and driverless vehicles. It argued that AI would create employment as productivity and real incomes rise, and new and better products are developed. PwC's latest Economic Outlook indicated that health, education and professional, scientific and technical services would benefit most, with manufacturing, transport and storage and public administration set to be the biggest losers.