Online symptom checkers have been deployed by sites such as WebMD and Mayo Clinic to identify possible causes and treatments for diseases based on a patient’s symptoms. Symptom checking first assesses a patient by asking a series of questions about their symptoms, then attempts to predict potential diseases. The two design goals of a symptom checker are to achieve high accuracy and intuitive interactions. In this paper we present our context-aware hierarchical reinforcement learning scheme, which significantly improves accuracy of symptom checking over traditional systems while also making a limited number of inquiries.

Continuous Human Activity Recognition (HAR) is an important application of smart mobile/wearable systems for providing dynamic assistance to users. However, HAR in real-time requires continuous sampling of data using built-in sensors (e.g., accelerometer), which significantly increases the energy cost and shortens the operating span. Reducing sampling rate can save energy but causes low recognition accuracy. Therefore, choosing adaptive sampling frequency that balances accuracy and energy efficiency becomes a critical problem in HAR. In this paper, we formalize the problem as minimizing both classification error and energy cost by choosing dynamically appropriate sampling rates. We propose Datum-Wise Frequency Selection (DWFS) to solve the problem via a continuous state Markov Decision Process (MDP). A policy function is learned from the MDP, which selects the best frequency for sampling an incoming data entity by exploiting a datum related state of the system. We propose a method for alternative learning the parameters of an activity classification model and the MDP that improves both the accuracy and the energy efficiency. We evaluate DWFS with three real-world HAR datasets, and the results show that DWFS statistically outperforms the state-of-the-arts regarding a combined measurement of accuracy and energy efficiency.

Predicting travel times of vehicles in urban settings is a useful and tangible quantity of interest in the context of intelligent transportation systems. We address the problem of travel time prediction in arterial roads using data sampled from probe vehicles. There is only a limited literature on methods using data input from probe vehicles. The spatio-temporal dependencies captured by existing data driven approaches are either too detailed or very simplistic. We strike a balance of the existing data driven approaches to account for varying degrees of influence a given road may experience from its neighbors, while controlling the number of parameters to be learnt. Specifically, we use a NoisyOR conditional probability distribution (CPD) in conjunction with a dynamic Bayesian network (DBN) to model state transitions of various roads. We propose an efficient algorithm to learn model parameters. We also propose an algorithm for predicting travel times on trips of arbitrary durations. Using synthetic and real world data traces we demonstrate the superior performance of the proposed method under different traffic conditions.

The technique called splitting sets has been proven useful in simplifying the investigation of Answer Set Programming (ASP). In this paper, we investigate the splitting set theorem for LP MLN that is a new extension of ASP created by combining the ideas of ASP and Markov Logic Networks (MLN). Firstly, we extend the notion of splitting sets to LP MLN programs and present the splitting set theorem for LP MLN . Then, the use of the theorem for simplifying several LP MLN inference tasks is illustrated. After that, we give two parallel approaches for solving LP MLN programs via using the theorem. The preliminary experimental results show that these approaches are alternative ways to promote an LP MLN solver.

In this paper, we consider the problem of fair statistical inference involving outcome variables. Examples include classification and regression problems, and estimating treatment effects in randomized trials or observational data. The issue of fairness arises in such problems where some covariates or treatments are "sensitive," in the sense of having potential of creating discrimination. In this paper, we argue that the presence of discrimination can be formalized in a sensible way as the presence of an effect of a sensitive covariate on the outcome along certain causal pathways, a view which generalizes (Pearl 2009). A fair outcome model can then be learned by solving a constrained optimization problem. We discuss a number of complications that arise in classical statistical inference due to this view and provide workarounds based on recent work in causal and semi-parametric inference.

In Markov Decision Processes (MDPs), the reward obtained in a state is Markovian, i.e., depends on the last state and action. This dependency makes it difficult to reward more interesting long-term behaviors, such as always closing a door after it has been opened, or providing coffee only following a request. Extending MDPs to handle non-Markovian reward functions was the subject of two previous lines of work. Both use LTL variants to specify the reward function and then compile the new model back into a Markovian model. Building on recent progress in temporal logics over finite traces, we adopt LDLf for specifying non-Markovian rewards and provide an elegant automata construction for building a Markovian model, which extends that of previous work and offers strong minimality and compositionality guarantees.

This paper proposes a low-cost, easily realizable strategy to equip a reinforcement learning (RL) agent the capability of behaving ethically. Our model allows the designers of RL agents to solely focus on the task to achieve, without having to worry about the implementation of multiple trivial ethical patterns to follow. Based on the assumption that the majority of human behavior, regardless which goals they are achieving, is ethical, our design integrates human policy with the RL policy to achieve the target objective with less chance of violating the ethical code that human beings normally obey.

Automatic facial expression analysis in inter-personal communication is challenging. Not only because conversation partners' facial expressions mutually influence each other, but also because no correct interpretation of facial expressions is possible without taking social context into account. In this paper, we propose a probabilistic framework to model interactional synchronization between conversation partners based on their facial expressions. Interactional synchronization manifests temporal dynamics of conversation partners' mutual influence. In particular, the model allows us to discover a set of common and unique facial synchronization templates directly from natural interpersonal interaction without recourse to any predefined labeling schemes. The facial synchronization templates represent periodical facial event coordinations shared by multiple conversation pairs in a specific social context. We test our model on two different dyadic conversations of negotiation and job-interview. Based on the discovered facial event coordination, we are able to predict their conversation outcomes with higher accuracy than HMMs and GMMs.

Crowdsourcing technique provides an efficient platform to employ human skills in sentiment analysis, which is a difficult task for automatic language models due to the large variations in context, writing style, view point and so on. However, the standard crowdsourcing aggregation models are incompetent when the number of crowd labels per worker is not sufficient to train parameters, or when it is not feasible to collect labels for each sample in a large dataset. In this paper, we propose a novel hybrid model to exploit both crowd and text data for sentiment analysis, consisting of a generative crowdsourcing aggregation model and a deep sentimental autoencoder. Combination of these two sub-models is obtained based on a probabilistic framework rather than a heuristic way. We introduce a unified objective function to incorporate the objectives of both sub-models, and derive an efficient optimization algorithm to jointly solve the corresponding problem. Experimental results indicate that our model achieves superior results in comparison with the state-of-the-art models, especially when the crowd labels are scarce.

Volunteers who help with online crowdsourcing such as citizen science tasks typically make only a few contributions before exiting. We propose a computational approach for increasing users' engagement in such settings that is based on optimizing policies for displaying motivational messages to users. The approach, which we refer to as Trajectory Corrected Intervention (TCI), reasons about the tradeoff between the long-term influence of engagement messages on participants' contributions and the potential risk of disrupting their current work. We combine model-based reinforcement learning with off-line policy evaluation to generate intervention policies, without relying on a fixed representation of the domain. TCI works iteratively to learn the best representation from a set of random intervention trials and to generate candidate intervention policies. It is able to refine selected policies off-line by exploiting the fact that users can only be interrupted once per session.We implemented TCI in the wild with Galaxy Zoo, one of the largest citizen science platforms on the web. We found that TCI was able to outperform the state-of-the-art intervention policy for this domain, and significantly increased the contributions of thousands of users. This work demonstrates the benefit of combining traditional AI planning with off-line policy methods to generate intelligent intervention strategies.