In many real-world machine learning problems, feature values are not readily available. To make predictions, some of the missing features have to be acquired, which can incur a cost in money, computational time, or human time, depending on the problem domain. This leads us to the problem of choosing which features to use at the prediction time. The chosen features should increase the prediction accuracy for a low cost, but determining which features will do that is challenging. The choice should take into account the previously acquired feature values as well as the feature costs. This paper proposes a novel approach to address this problem. The proposed approach chooses the most useful features adaptively based on how relevant they are for the prediction task as well as what the corresponding feature costs are. Our approach uses a generic neural network architecture, which is suitable for a wide range of problems. We evaluate our approach on three cost-sensitive datasets, including Yahoo! Learning to Rank Competition dataset as well as two health datasets. We show that our approach achieves high accuracy with a lower cost than the current state-of-the-art approaches.

Mean field games (MFG) and mean field control (MFC) are critical classes of multi-agent models for efficient analysis of massive populations of interacting agents. Their areas of application span topics in economics, finance, game theory, industrial engineering, crowd motion, and more. In this paper, we provide a flexible machine learning framework for the numerical solution of potential MFG and MFC models. State-of-the-art numerical methods for solving such problems utilize spatial discretization that leads to a curse-of-dimensionality. We approximately solve high-dimensional problems by combining Lagrangian and Eulerian viewpoints and leveraging recent advances from machine learning. More precisely, we work with a Lagrangian formulation of the problem and enforce the underlying Hamilton-Jacobi-Bellman (HJB) equation that is derived from the Eulerian formulation. Finally, a tailored neural network parameterization of the MFG/MFC solution helps us avoid any spatial discretization. Our numerical results include the approximate solution of 100-dimensional instances of optimal transport and crowd motion problems on a standard work station. These results open the door to much-anticipated applications of MFG and MFC models that were beyond reach with existing numerical methods.

EDU Robotics Institute, Carnegie Mellon University, Pittsburgh, P A 15213, USA Abstract High fidelity behavior prediction of intelligent agents is critical in many applications. However, the prediction model trained on the training set may not generalize to the testing set due to domain shift and time variance. The challenge motivates the adoption of online adaptation algorithms to update prediction models in real-time to improve the prediction performance. Inspired by Extended Kalman Filter (EKF), this paper introduces a series of online adaptation methods, which are applicable to neural network-based models. A base adaptation algorithm Modified EKF with forgetting factor (MEKF λ) is introduced first, followed by exponential moving average filtering techniques. Then this paper introduces a dynamic multi-epoch update strategy to effectively utilize samples received in real time. With all these extensions, we propose a robust online adaptation algorithm: MEKF with Exponential Moving Average and Dynamic Multi-Epoch strategy (MEKF EMA-DME). The proposed algorithm outperforms existing methods as demonstrated in experiments. Keywords: Online adaptation, extended Kalman filter, exponential moving average, optimization 1. Introduction Supervised learning has been widely used to obtain models to predict the behaviors of intelligent agents Rudenko et al. (2019). Behavior prediction is a sub-topic of time series prediction Weigend (2018), which includes but is not limited to vehicle trajectory prediction during autonomous driving Lef evre et al. (2014) and human-motion prediction during human-robot collaboration Cheng et al. (2019).

A classic graph coloring problem is to assign colors to vertices of any graph so that distinct colors are assigned to adjacent vertices. Optimal graph coloring colors a graph with a minimum number of colors, which is its chromatic number . Finding out the chromatic number is a combinatorial optimization problem proven to be computationally intractable, which implies that no algorithm that computes large instances of the problem in a reasonable time is known. F or this reason, approximate methods and metaheuristics form a set of techniques that do not guarantee optimality, but obtain good solutions in a reasonable time. This paper reports a comparative study of the Hill-Climbing, Simulated Annealing, T abu Search, and Iterated Local Search metaheuristics for the classic graph coloring problem considering its time efficiency for processing the DSJC125 and DSJC250 instances of the DIMACS benchmark.

Was investigated in this paper the use of a search strategy in the problem of finding the best path among multiple origins and multiple destinations. In this kind of problem, it must be decided within a lot of combinations which is the best origin and the best destination, and also the best path between these two regions. One remarkable difficulty to answer this sort of problem is to perform the search in a reduced time. This monography is a extension of previous research in which the problem described here was studied only in a bus network in the city of Fortaleza. This extension consisted of an exploration of the search strategy in graphs that represent public ways in cities like Fortaleza, Mumbai and Tokyo. Using this strategy with a heuristic algorithm, Haversine distance, was noticed that is possible to reduce substantially the time of the search, but introducing an error because of the loss of the admissible characteristic of the heuristic function applied.

Advances in artificial intelligence have renewed interest in conversational agents. Additionally to software developers, today all kinds of employees show interest in new technologies and their possible applications for customers. German insurance companies generally are interested in improving their customer service and digitizing their business processes. In this work we investigate the potential use of conversational agents in insurance companies theoretically by determining which classes of agents exist which are of interest to insurance companies, finding relevant use cases and requirements. We add two practical parts: First we develop a showcase prototype for an exemplary insurance scenario in claim management. Additionally in a second step, we create a prototype focusing on customer service in a chatbot hackathon, fostering innovation in interdisciplinary teams. In this work, we describe the results of both prototypes in detail. We evaluate both chatbots defining criteria for both settings in detail and compare the results and draw conclusions for the maturity of chatbot technology for practical use, describing the opportunities and challenges companies, especially small and medium enterprises, face.

A novel approach of applying deep reinforcement learning to an RF pulse design is introduced. This method, which is referred to as $DeepRF_{SLR}$, is designed to minimize the peak amplitude or, equivalently, minimize the pulse duration of a multiband refocusing pulse generated by the Shinar Le-Roux (SLR) algorithm. In the method, the root pattern of SLR polynomial, which determines the RF pulse shape, is optimized by iterative applications of deep reinforcement learning and greedy tree search. When tested for the designs of the multiband factors of three and seven RFs, $DeepRF_{SLR}$ demonstrated improved performance compared to conventional methods, generating shorter duration RF pulses in shorter computational time. In the experiments, the RF pulse from $DeepRF_{SLR}$ produced a slice profile similar to the minimum-phase SLR RF pulse and the profiles matched to that of the computer simulation. Our approach suggests a new way of designing an RF by applying a machine learning algorithm, demonstrating a machine-designed MRI sequence.

Discriminating between causality and correlation is a major problem in machine learning, and theoretical tools for determining causality are still being developed. However, people commonly make causality judgments and are often correct, even in unfamiliar domains. What are humans doing to make these judgments? This paper examines differences in human experts' and non-experts' ability to attribute causality by comparing their performances to those of machine-learning algorithms. We collected human judgments by using Amazon Mechanical Turk (MTurk) and then divided the human subjects into two groups: experts and non-experts. We also prepared expert and non-expert machine algorithms based on different training of convolutional neural network (CNN) models. The results showed that human experts' judgments were similar to those made by an "expert" CNN model trained on a large number of examples from the target domain. The human non-experts' judgments resembled the prediction outputs of the CNN model that was trained on only the small number of examples used during the MTurk instruction. We also analyzed the differences between the expert and non-expert machine algorithms based on their neural representations to evaluate the performances, providing insight into the human experts' and non-experts' cognitive abilities.

We present an innovative methodology for studying and teaching the impacts of AI through a role - play game. The game serves two primary purposes: 1) training AI developers and AI policy professionals to reflect on and prepare for future social and ethical challenges related to AI and 2) exploring possible futures involving AI technology developm ent, deployment, social impacts, and governance. While the game currently focuses on the inter - relations between short -, mid - and long - term impacts of AI, it has potential to be adapted for a broad range of scenarios, exploring in greater depths issues of AI policy research and affording training within organizations. The game presented here has undergone two years of development and has been tested through over 30 events involving between 3 and 70 participants. The game is under active development, but pre liminary findings suggest that role - play is a promising methodology for both exploring AI futures and training individuals and organizations in thinking about, and reflecting on, the impacts of AI and strategic mistakes that can be avoided today.

Counterfactual Thinking is a human cognitive ability studied in a wide variety of domains. It captures the process of reasoning about a past event that did not occur, namely what would have happened had this event occurred, or, otherwise, to reason about an event that did occur but what would ensue had it not. Given the wide cognitive empowerment of counterfactual reasoning in the human individual, the question arises of how the presence of individuals with this capability may improve cooperation in populations of self-regarding individuals. Here we propose a mathematical model, grounded on Evolutionary Game Theory, to examine the population dynamics emerging from the interplay between counterfactual thinking and social learning (i.e., individuals that learn from the actions and success of others) whenever the individuals in the population face a collective dilemma. Our results suggest that counterfactual reasoning fosters coordination in collective action problems occurring in large populations, and has a limited impact on cooperation dilemmas in which coordination is not required. Moreover, we show that a small prevalence of individuals resorting to counterfactual thinking is enough to nudge an entire population towards highly cooperative standards.