Recent successes combine reinforcement learning algorithms and deep neural networks, despite reinforcement learning not being widely applied to robotics and real world scenarios. This can be attributed to the fact that current state-of-the-art, end-to-end reinforcement learning approaches still require thousands or millions of data samples to converge to a satisfactory policy and are subject to catastrophic failures during training. Conversely, in real world scenarios and after just a few data samples, humans are able to either provide demonstrations of the task, intervene to prevent catastrophic actions, or simply evaluate if the policy is performing correctly. This research investigates how to integrate these human interaction modalities to the reinforcement learning loop, increasing sample efficiency and enabling real-time reinforcement learning in robotics and real world scenarios. This novel theoretical foundation is called Cycle-of-Learning, a reference to how different human interaction modalities, namely, task demonstration, intervention, and evaluation, are cycled and combined to reinforcement learning algorithms. Results presented in this work show that the reward signal that is learned based upon human interaction accelerates the rate of learning of reinforcement learning algorithms and that learning from a combination of human demonstrations and interventions is faster and more sample efficient when compared to traditional supervised learning algorithms. Finally, Cycle-of-Learning develops an effective transition between policies learned using human demonstrations and interventions to reinforcement learning. The theoretical foundation developed by this research opens new research paths to human-agent teaming scenarios where autonomous agents are able to learn from human teammates and adapt to mission performance metrics in real-time and in real world scenarios.

Reinforcement learning and probabilistic reasoning algorithms aim at learning from interaction experiences and reasoning with probabilistic contextual knowledge respectively. In this research, we develop algorithms for robot task completions, while looking into the complementary strengths of reinforcement learning and probabilistic reasoning techniques. The robots learn from trial-and-error experiences to augment their declarative knowledge base, and the augmented knowledge can be used for speeding up the learning process in potentially different tasks. We have implemented and evaluated the developed algorithms using mobile robots conducting dialog and navigation tasks. From the results, we see that our robot's performance can be improved by both reasoning with human knowledge and learning from task-completion experience. More interestingly, the robot was able to learn from navigation tasks to improve its dialog strategies.

This article discusses recent trends and concepts in developing new kinds of artificial intelligence (AI) systems which relate to complex facets and different types of human intelligence, especially social, emotional, attentional and ethical intelligence, which to date have been under-discussed. We describe various aspects of multiple human intelligence and learning styles, which may impact on a variety of AI problem domains. Using the concept of multiple intelligence rather than a single type of intelligence, we categorize and provide working definitions of various AI depending on their cognitive skills or capacities. Future AI systems will be able not only to communicate with human actors and each other, but also to efficiently exchange knowledge with abilities of cooperation, collaboration and even co-creating something new and valuable and have meta-learning capacities. Multi-agent systems such as these can be used to solve problems that would be difficult to solve by any individual intelligent agent.

Active learning (AL) attempts to maximize the performance gain of the model by marking the fewest samples. Deep learning (DL) is greedy for data and requires a large amount of data supply to optimize massive parameters, so that the model learns how to extract high-quality features. In recent years, due to the rapid development of internet technology, we are in an era of information torrents and we have massive amounts of data. In this way, DL has aroused strong interest of researchers and has been rapidly developed. Compared with DL, researchers have relatively low interest in AL. This is mainly because before the rise of DL, traditional machine learning requires relatively few labeled samples. Therefore, early AL is difficult to reflect the value it deserves. Although DL has made breakthroughs in various fields, most of this success is due to the publicity of the large number of existing annotation datasets. However, the acquisition of a large number of high-quality annotated datasets consumes a lot of manpower, which is not allowed in some fields that require high expertise, especially in the fields of speech recognition, information extraction, medical images, etc. Therefore, AL has gradually received due attention. A natural idea is whether AL can be used to reduce the cost of sample annotations, while retaining the powerful learning capabilities of DL. Therefore, deep active learning (DAL) has emerged. Although the related research has been quite abundant, it lacks a comprehensive survey of DAL. This article is to fill this gap, we provide a formal classification method for the existing work, and a comprehensive and systematic overview. In addition, we also analyzed and summarized the development of DAL from the perspective of application. Finally, we discussed the confusion and problems in DAL, and gave some possible development directions for DAL.

Response process data collected from human-computer interactive items contain rich information about respondents' behavioral patterns and cognitive processes. Their irregular formats as well as their large sizes make standard statistical tools difficult to apply. This paper develops a computationally efficient method for exploratory analysis of such process data. The new approach segments a lengthy individual process into a sequence of short subprocesses to achieve complexity reduction, easy clustering and meaningful interpretation. Each subprocess is considered a subtask. The segmentation is based on sequential action predictability using a parsimonious predictive model combined with the Shannon entropy. Simulation studies are conducted to assess performance of the new methods. We use the process data from PIAAC 2012 to demonstrate how exploratory analysis of process data can be done with the new approach.

Advances in transportation systems have resulted in the generation of a large amount of traffic data from various sources [1-3]. In everyday life, GPS sensors installed in smartphones carried by millions of people can collect crowd flow data. Furthermore, taximeters and bus card readers can collect crowd demand data, and vehicle loop detectors can collect traffic flow or speed data. In the mean time, Deep Neural Networks (DNNs) have achieved promising performance improvements in various application areas. They can classify images into thousands of classes [4,5] as well as recognize human speech [6,7], with only small errors.

Hidden Markov chains are widely applied statistical models of stochastic processes, from fundamental physics and chemistry to finance, health, and artificial intelligence. The hidden Markov processes they generate are notoriously complicated, however, even if the chain is finite state: no finite expression for their Shannon entropy rate exists, as the set of their predictive features is generically infinite. As such, to date one cannot make general statements about how random they are nor how structured. Here, we address the first part of this challenge by showing how to efficiently and accurately calculate their entropy rates. We also show how this method gives the minimal set of infinite predictive features. A sequel addresses the challenge's second part on structure.

The p-tensor Ising model is a one-parameter discrete exponential family for modeling dependent binary data, where the sufficient statistic is a multi-linear form of degree p 2. This is a natural generalization of the matrix Ising model, that provides a convenient mathematical framework for capturing, not just pairwise, but higher-order dependencies in complex relational data. In this paper, we consider the problem of estimating the natural parameter of the p-tensor Ising model given a single sample from the distribution on N nodes. Our estimate is based on the maximum pseudo-likelihood (MPL) method, which provides a computationally efficient algorithm for estimating the parameter that avoids computing the intractable partition function. We derive general conditions under which the MPL estimate is N-consistent, that is, it converges to the true parameter at rate 1/ N. Our conditions are robust enough to handle a variety of commonly used tensor Ising models, including spin glass models with random interactions and models where the rate of estimation undergoes a phase transition. In particular, this includes results on N-consistency of the MPL estimate in the well-known p-spin Sherrington-Kirkpatrick (SK) model, spin systems on general p-uniform hypergraphs, and Ising models on the hypergraph stochastic block model (HSBM). In fact, for the HSBM we pin down the exact location of the phase transition threshold, which is determined by the positivity of a certain mean-field variational problem, such that above this threshold the MPL estimate is N-consistent, while below the threshold no estimator is consistent. Finally, we derive the precise fluctuations of the MPL estimate in the special case of the p-tensor Curie-Weiss model, which is the Ising model on the complete p-uniform hypergraph. An interesting consequence of our results is that the MPL estimate in the Curie-Weiss model saturates the Cramer-Rao lower bound at all points above the estimation threshold, that is, the MPL estimate incurs no loss in asymptotic statistical efficiency in the estimability regime, even though it is obtained by minimizing only an approximation of the true likelihood function for computational tractability.

Markov decision processes (MDPs) are a fundamental mathematical abstraction for sequential decision making under uncertainty and they serve as a basic modeling tool in reinforcement learning (RL) and stochastic control [5, 24, 30]. Two prominent classes of MDPs are average-reward MDPs (AMDPs) and discounted MDPs (DMDPs). Each have been studied extensively; AMDPs are applicable to optimal control, learning automata, and various real-world reinforcement learning settings [17, 3, 22] and DMDPs have a number of nice theoretical properties including reward convergence and operator monotonicity [6]. In this paper we consider the prevalent computational learning problem of finding an approximately optimal policy of an MDP given only restricted access to the model. In particular, we consider the problem of computing an ɛ-optimal policy, i.e. a policy with an additive ɛ error in expected cumulative reward over infinite horizon, under the standard assumption of a generative model [14, 13], which allows one to sample from state-transitions given the current state-action pair. This problem is well-studied and there are multiple known upper and lower bounds on its sample complexity [4, 32, 28, 31]. In this work, we provide a unified framework based on primal-dual stochastic mirror descent (SMD) for learning an ɛ-optimal policies for both AMDPs and DMDPs with a generative model.

One of the most difficult problems for an adaptable agent is gauging how to behave in a nonstationary environment. When conditions are stable, an organism generally pursues a strategy known to provide the best outcome. However, when environmental conditions change, an organism abandons the current action plan and searches for a new best option. The most challenging aspect of this search—calculating the exact time point at which to change strategies—requires the brain to integrate past and present observations and evaluate whether they remain consistent with current environmental conditions. On page 1076 of this issue, Domenech et al. ([ 1 ][1]) report on the modeling of rare direct electrical recordings from the prefrontal cortices (PFCs) of a small group of human epilepsy patients as they flexibly negotiated a nonstationary environment. To understand the brain's mode of navigation, consider for example a sailor at sea (see the figure). The winds and the currents determine the waves that drive the sailor to continuously adjust the rudder so as to stay on course. By observing the wave patterns, he can anticipate the navigational effects of his actions and adapt accordingly. But when the currents or the weather changes, the sailor must adapt his course to reach the next port of call. At that time, the sailor observes essentially the same stimulus (the waves) but has to remap his action plan (rudder adjustments) to the new wind conditions and currents. This difficult-decision problem—how to detect and then adapt to a nonstationary environment—is captured perfectly in the exploration-exploitation dilemma: When should I stop exploiting my current action plan and start exploring different ways to reach my goals? An optimal solution tracks the discounted sum of normalized future rewards. However, this approach applies strictly to stationary environments and thus does not capture the dynamic changes that organisms encounter in their daily lives ([ 2 ][2]). Yet the human brain and those of other species seem to smoothly solve the exploration-exploitation dilemma in nonstationary environments. Decision neuroscience has investigated the flexible adaptation to changing environmental contingencies with diverse experimental paradigms and assorted computational models. The simplest paradigm is probabilistic reversal learning, in which the agent has to search for reward among two options with complementary reward probabilities. This adaptation problem can be solved by hidden Markov models ([ 3 ][3]), which are well-approximated by reinforcement learning (RL) models that also update nonchosen actions ([ 4 ][4]). Extension of this paradigm to include independently changing reward probabilities reveals two distinct neural responses: Expected-value signals, which reflect “exploitative” choices, spur activation of the ventromedial prefrontal cortex (vmPFC); and “explorative” choices (that is, the choosing of a currently lesser valued option) activate the frontopolar cortex ([ 5 ][5]). ![Figure][6] A sailor solves a dilemma at sea As the ship nears bad weather, the sailor's ventromedial prefrontal cortex (vmPFC) evaluates the ongoing (orange) action plan (exploitation) and the prospective (brown, red) plans (exploration). Once the red (calm waters) plan is exploited, the sailor's dorsomedial PFC (dmPFC) uses trial-and-error learning to map the proper rudder adjustments. GRAPHIC: A. KITTERMAN/ SCIENCE Another task with both rapid and slow changes in the reward probabilities of various options was used to develop a hierarchical Bayesian model that estimates the volatility of the environment and adjusts the learning rate accordingly ([ 6 ][7]). This model has found its generalization in the hierarchical Gaussian filter (HGF) framework ([ 7 ][8]), which is widely used in modeling social and nonsocial human decision-making in nonstationary environments. Although these computational modeling frameworks differ, all are trying to solve similar problems: How to infer the latent structure of the world from discrete observations and how to detect transitions between different states of the world. Domenech et al. address the same problems with yet another experimental paradigm, this one carried out with a small group of human epilepsy patients. Electrodes deeply implanted in the patients' PFCs delivered direct electrical recordings from the vmPFC and dorsomedial PFC (dmPFC) while the patients performed a multioption decision task. The participants had to associate three different stimuli with three distinct actions, thus constituting an action plan. The mapping changed every 33 to 57 trials, and participants had to relearn the association of the same stimuli with a different combination of actions, much like our sailor at sea who faces changes in weather and currents that alter wave patterns. The computational model ([ 8 ][9]) generates a reliability value for the ongoing action plan and other concurrently monitored plans. When the ongoing action plan is deemed reliable, the model is in “exploitation” mode and learns the stimulus-action mapping through RL mechanisms. When the ongoing action plan is deemed unreliable, the model switches to “exploration” mode. New provisional action plans are created and evaluated, until one emerges as a reliable predictor for successful stimulus-action mapping (see the figure). Using a state-of-the-art model-based analysis that associates the model-derived variables with the brain activity in various frequency bands of the neural recordings, the authors found a delicate interplay between the vmPFC and dmPFC that supports a predictive coding interpretation for resolution of the exploration-exploitation dilemma. vmPFC monitors and represents the reliability of the ongoing action plan. vmPFC relays the ongoing action plan to the dmPFC as either a “stay” or “switch” trial. A stay trial triggers additional learning through RL mechanisms in the dmPFC. In contrast, the dmPFC responds to a switch trial by suppressing activity related to maintaining the ongoing action plan. These findings resonate with and extend earlier results obtained with functional neuroimaging ([ 5 ][5], [ 9 ][10]). These computational approaches to the problem of behavioral flexibility in a nonstationary environment share one commonality: They are all building a model of the environment and the transition therein, either explicitly (as in the HGF framework) or implicitly (by evaluating the ongoing action plan, as in the Domenech et al. study). Although all of these models strive for generality, each was developed for a specific experimental context. It remains to be seen which of these provides the best account of flexible decision-making in humans and other species, preferably using a unified experimental paradigm. A model-free RL account ([ 10 ][11]) likely will not suffice, as several studies have demonstrated the superiority of more-complex models over this “vanilla” RL model. 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