The frequency and intensity of natural disasters has significantly increased over the past decades and this trend is predicted to continue. Facing these possible and unexpected disasters, understanding and simulating of human emergency mobility following disasters will becomethe critical issue for planning effective humanitarian relief, disaster management, and long-term societal reconstruction. However, due to the uniquenessof various disasters and the unavailability of reliable and large scale human mobility data, such kind of research is very difficult to be performed. Hence, in this paper,we collect big and heterogeneous data (e.g. 1.6 million users' GPS records in three years, 17520 times of Japan earthquake data in four years, news reporting data, transportation network data and etc.) to capture and analyze human emergency mobility following different disasters. By mining these big data, we aim to understand what basic laws govern human mobility following disasters, and develop a general model of human emergency mobility for generating and simulating large amount of human emergency movements. The experimental results and validations demonstrate the efficiency of our simulation model, and suggest that human mobility following disasters may be significantly morepredictable and can be easier simulated than previously thought.

Energy disaggregation, the task of taking a whole home electricity signal and decomposing it into its component appliances, has been proved to be essential in energy conservation research. One powerful cue for breaking down the entire household's energy consumption is user's daily energy usage behavior, which has so far received little attention: existing works on energy disaggregation mostly ignored the relationship between the energy usages of various appliances across different time slots. To model such relationship, we combine topic models with Hawkes processes, and propose a novel probabilistic model based on marked Hawkes process that enables the modeling of marked event data. The proposed model seeks to capture the influence from the occurrence and the marks of one usage event to the occurrence and the marks of subsequent usage events in the future. We also develop an inference algorithm based on variational inference for model parameter estimation. Experimental results on both synthetic data and three real world data sets demonstrate the effectiveness of our model, which outperforms state-of-the-art approaches in decomposing the entire consumed energy to each appliance. Analyzing the influence captured by the proposed model provides further insights into numerous interesting energy usage behavior patterns.

Renewable power sources such as wind and solar are inflexible in their energy production, which requires demand to rapidly follow supply in order to maintain energy balance. Promising controllable demands are air-conditioners and heat pumps which use electric energy to maintain a temperature at a setpoint. Such Thermostatically Controlled Loads (TCLs) have been shown to be able to follow a power curve using reactive control. In this paper we investigate the use of planning under uncertainty to pro-actively control an aggregation of TCLs to overcome temporary grid imbalance. We present a formal definition of the planning problem under consideration, which we model using the Multi-Agent Markov Decision Process (MMDP) framework. Since we are dealing with hundreds of agents, solving the resulting MMDPs directly is intractable. Instead, we propose to decompose the problem by decoupling the interactions through arbitrage. Decomposition of the problem means relaxing the joint power consumption constraint, which means that joining the plans together can cause overconsumption. Arbitrage acts as a conflict resolution mechanism during policy execution, using the future expected value of policies to determine which TCLs should receive the available energy. We experimentally compare several methods to plan with arbitrage, and conclude that a best response-like mechanism is a scalable approach that returns near-optimal solutions.

Notions of identity and of the self have long been studied in social psychology and sociology as key guiding elements of social interaction and coordination. In the AI of the future, these notions will also play a role in producing natural, socially appropriate artificially intelligent agents that encompass subtle and complex human social and affective skills. We propose here a Bayesian generalization of the sociological affect control theory of self as a theoretical foundation for socio-affectively skilled artificial agents. This theory posits that each human maintains an internal model of his or her deep sense of "self" that captures their emotional, psychological, and socio-cultural sense of being in the world. The "self" is then externalised as an identity within any given interpersonal and institutional situation, and this situational identity is the person's local (in space and time) representation of the self. Situational identities govern the actions of humans according to affect control theory. Humans will seek situations that allow them to enact identities consistent with their sense of self. This consistency is cumulative over time: if some parts of a person's self are not actualized regularly, the person will have a growing feeling of inauthenticity that they will seek to resolve. In our present generalisation, the self is represented as a probability distribution, allowing it to be multi-modal (a person can maintain multiple different identities), uncertain (a person can be unsure about who they really are), and learnable (agents can learn the identities and selves of other agents). We show how the Bayesian affect control theory of self can underpin artificial agents that are socially intelligent.

Intelligent item recommendation is a key issue in AI research which enables recommender systems to be more “human-minded” when generating recommendations. However, one of the major features of human — forgetting, has barely been discussed as regards recommender systems. In this paper, we considered people’s forgetting of interest when performing personalized recommendations, and brought forward a personalized framework to integrate interest-forgetting property with Markov model. Multiple implementations of the framework were investigated and compared. The experimental evaluation showed that our methods could significantly improve the accuracy of item recommendation, which verified the importance of considering interest-forgetting in recommendations.

Approximate Bayesian computation (ABC) is a powerful and elegant framework for performing inference in simulation-based models. However, due to the difficulty in scaling likelihood estimates, ABC remains useful for relatively low-dimensional problems. We introduce Hamiltonian ABC (HABC), a set of likelihood-free algorithms that apply recent advances in scaling Bayesian learning using Hamiltonian Monte Carlo (HMC) and stochastic gradients. We find that a small number forward simulations can effectively approximate the ABC gradient, allowing Hamiltonian dynamics to efficiently traverse parameter spaces. We also describe a new simple yet general approach of incorporating random seeds into the state of the Markov chain, further reducing the random walk behavior of HABC. We demonstrate HABC on several typical ABC problems, and show that HABC samples comparably to regular Bayesian inference using true gradients on a high-dimensional problem from machine learning.

Many machine learning tasks can be formulated in terms of predicting structured outputs. In frameworks such as the structured support vector machine (SVM-Struct) and the structured per-ceptron, discriminative functions are learned by iteratively applying efficient maximum a posteri-ori (MAP) decoding. However, maximum likelihood estimation (MLE) of probabilistic models over these same structured spaces requires computing partition functions, which is generally intractable. This paper presents a method for learning discrete exponential family models using the Bethe approximation to the MLE. Remarkably, this problem also reduces to iterative (MAP) decoding. This connection emerges by combining the Bethe approximation with a Frank-Wolfe (FW) algorithm on a convex dual objective which circumvents the intractable partition function. The result is a new single loop algorithm MLE-Struct, which is substantially more efficient than previous double-loop methods for approximate maximum likelihood estimation. Our algorithm outperforms existing methods in experiments involving image segmentation, matching problems from vision, and a new dataset of university roommate assignments.

Maintaining an accurate set of beliefs in a partially observable scenario, particularly with respect to other agents operating in the same space, is a vital aspect of multiagent planning. We analyze how the beliefs of an agent can be updated for fast adaptivity to changes in the behavior of an unknown teammate. The main contribution of this paper is the empirical evaluation of an agent cooperating with a teammate whose goals change periodically. We test our approach in a collaborative multiagent domain where identification of goals is necessary for successful completion. The belief revision technique we propose outperforms the traditional approach in a majority of test cases. Additionally, our results suggest the ability to approximate a higher level model by utilizing a belief distribution over a set of lower level behaviors, particularly when the belief update strategy identifies changes in the behavior in a responsive manner.

This paper presents a novel approach to modeling the dynamics of human movements with a grid-based representation.For each grid cell, we formulate the local dynamics using a variant of the left-to-right HMM, and thus explicitly model the exiting direction from the current cell. The dependency of this process on the entry direction is captured by employing the Input-Output HMM (IOHMM). On a higher level, we introduce the place where the whole trajectory originated into the IOHMM framework forming a hierarchical input structure. Therefore, we manage to capture both local spatial-temporal correlations and the long-term dependency on faraway initiating events, thus enabling the developed model to incorporate more information and to generate more informative predictions of future trajectories.The experimental results in an office corridor environment verify the capabilities of our method.

Inspired by recent successes of deep learning in computer vision and speech recognition, we propose a novel framework to encode time series data as different types of images, namely, Gramian Angular Fields (GAF) and Markov Transition Fields (MTF). This enables the use of techniques from computer vision for classification. Using a polar coordinate system, GAF images are represented as a Gramian matrix where each element is the trigonometric sum (i.e., superposition of directions) between different time intervals. MTF images represent the first order Markov transition probability along one dimension and temporal dependency along the other. We used Tiled Convolutional Neural Networks (tiled CNNs) on 12 standard datasets to learn high-level features from individual GAF, MTF, and GAF-MTF images that resulted from combining GAF and MTF representations into a single image. The classification results of our approach are competitive with five stateof-the-art approaches. An analysis of the features and weights learned via tiled CNNs explains why the approach works.