2QBF is a special case of general quantified Boolean formulae (QBF). It is limited to just two quantification levels, i.e., to a form forall-exists. Despite this limitation it applies to a wide range of applications, e.g., to artificial intelligence, graph theory, synthesis, etc.. Recent research showed that CEGAR-based methods give a performance boost to QBF solving (e.g, compared to QDPLL). Conjunctive normal form (CNF) is a commonly accepted representation for both SAT and QBF problems; however, it does not reflect the circuit structure that might be present in the problem. Existing attempts of extracting this structure from CNF and using it in 2QBF context do not show advantages over CNF based 2QBF solvers. In this work we introduce a new workflow for 2QBF, containing a new semantic circuit extraction algorithm and a CEGAR-based 2QBF solver that uses circuit structure and is improved by a so-called "cofactor sharing'' heuristics. We evaluate the proposed methodology on a range of benchmarks and show the practicality of the new approach.

With the efforts of moving to sustainable and reliable energy supply, electricity markets are undergoing far-reaching changes. Due to the high-cost of failure in the real-world, it is important to test new market structures in simulation. This is the focus of the Power Trading Agent Competition (Power TAC), which proposes autonomous electricity broker agents as a means for stabilizing the electricity grid. This paper focuses on the question: how should an autonomous electricity broker agent act in competitive electricity markets to maximize its profit. We formalize the complete electricity trading problem as a continuous, high-dimensional Markov Decision Process (MDP), which is computationally intractable to solve. Our formalization provides a guideline for approximating the MDP's solution, and for extending existing solutions. We show that a previously champion broker can be viewed as approximating the solution using a lookahead policy. We present TacTex15, which improves upon this previous approximation and achieves state-of-the-art performance in competitions and controlled experiments. Using thousands of experiments against 2015 finalist brokers, we analyze TacTex15's performance and the reasons for its success. We find that lookahead policies can be effective, but their performance can be sensitive to errors in the transition function prediction, specifically demand-prediction.

The current approaches to model dynamism in DCOPs solve a sequence of static problems, reacting to the changes in the environment as the agents observe them. Such approaches, thus, ignore possible predictions on the environment evolution. To overcome such limitations, we introduce the Proactive Dynamic DCOP (PD-DCOP) model, a novel formalism to model dynamic DCOPs in the presence of exogenous uncertainty. In contrast to reactive approaches, PD-DCOPs are able to explicitly model the possible changes to the problem, and take such information into account proactively, when solving the dynamically changing problem.

Most modern network-based intrusion detection systems (IDSs) passively monitor network traffic to identify possible attacks through known vectors. Though useful, this approach has widely known high false positive rates, often causing administrators to suffer from a "cry wolf effect," where they ignore all warnings because so many have been false. In this paper, we focus on a method to reduce this effect using an idea borrowed from computer vision and neuroscience called active perception. Our approach is informed by theoretical ideas from decision theory and recent research results in neuroscience. The active perception agent allocates computational and sensing resources to (approximately) optimize its Value of Information. To do this, it draws on models to direct sensors towards phenomena of greatest interest to inform decisions about cyber defense actions. By identifying critical network assets, the organization's mission measures self-interest (and value of information). This model enables the system to follow leads from inexpensive, inaccurate alerts with targeted use of expensive, accurate sensors. This allows the deployment of sensors to build structured interpretations of situations. From these, an organization can meet mission-centered decision-making requirements with calibrated responses proportional to the likelihood of true detection and degree of threat.

A key front for ethical questions in artificial intelligence, and computer science more generally, is teaching students how to engage with the questions they will face in their professional careers based on the tools and technologies we teach them.  In past work (and current teaching) we have advocated for the use of science fiction as an appropriate tool which enables AI researchers to engage students and the public on the current state and potential impacts of AI. We present teaching suggestions for E.M. Forster's 1909 story, "The Machine Stops," to teach topics in computer ethics.  In particular, we use the story to examine ethical issues related to being constantly available for remote contact, physically isolated, and dependent on a machine --- all without mentioning computer games or other media to which students have strong emotional associations. We give a high-level view of common ethical theories and indicate how they inform the questions raised by the story and afford a structure for thinking about how to address them.

The focus of this work is to investigate how children's perception of the robot changes with age and gender, and to enable the robot to adapt to these differences for improving human-robot interaction (HRI). We propose a neural network-based learning architecture to estimate children's age and gender based on the body motion performing a set of actions. To evaluate our system, we collected a fully annotated depth dataset of 28 children (aged between 7 and 16 years old) and applied it to a learning-based method for age and gender estimation by modeling children's 3D skeleton motion data. We discuss our results that show an average accuracy of 95.2% and 90.3% for age and gender respectively in the context of a real-world scenario.

This paper presents a new system which provides services to elderly and persons suffering from motor or cognitive impair-ments in a smart home (SH). SH are alternative solutions in order to keep elderly and impaired persons as long as possible at their homes to allow them to live with more comfort. SH are dynamically evolving environments, thus the provided services by this system are context aware and customizable for every user. These services can be accessed by users through an application installed on a mobile device. The sys-tem uses a multi agent system (MAS) to have a dynamic and adaptive response to environmental change. Experiments are carried out in order to validate the chosen solutions.

Online social communities are becoming increasingly popular platforms for people to share information, seek emotional support, and maintain accountability for losing weight. Studying the discourse in these communities can offer insights on how users benefit from using these applications. This paper presents an analysis of language and discourse patterns in forum posts by users who lose weight and keep it off versus users with fluctuating weight dynamics. In contrast to prior studies, we have access to the weekly self-reported check-in weights of users along with their forum posts. This paper also presents a study on how goal-oriented forums are different from general online forums in terms of language markers. Our results reveal dierences about how the types of posts made by users vary along with their weight-loss patterns. These insights are closely related to the power dynamics of social interactions and can enable better design ofweight-loss applications thereby contributing to a healthy society.

In many situations, agents are required to use a set of strategies (behaviors) and switch among them during the course of an interaction. This work focuses on the problem of recognizing the strategy used by an agent within a small number of interactions. We propose using a Bayesian framework to address this problem. Bayesian policy reuse (BPR) has been empirically shown to be efficient at correctly detecting the best policy to use from a library in sequential decision tasks. In this paper we extend BPR to adversarial settings, in particular, to opponents that switch from one stationary strategy to another. Our proposed extension enables learning new models in an online fashion when the learning agent detects that the current policies are not performing optimally. Experiments presented in repeated games show that our approach is capable of efficiently detecting opponent strategies and reacting quickly to behavior switches, thereby yielding better performance than state-of-the-art approaches in terms of average rewards.

Scientific articles are not born equal. Some generate an entire discipline while others make relatively fewer contributions. When reviewing scientific literatures, it would be useful to identify those important articles and understand how they influence others. In this paper, we introduce J-Index, a quantitative metric modeling topic-level academic influence. J-Index is calculated based on the novelty of each article as well as its contributions to the articles where it is cited. We devise a generative model named Reference Topic Model (RefTM) which jointly utilizes the textual content and citation information in scientific literatures. We show how to learn RefTM to discover both the novelty of each paper and the strength of each citation. Experiments on a collection of more than 420,000 research papers demonstrate that RefTM outperforms the state-of-the-art approaches in terms of topic coherence as well as prediction performance, and validate J-Index's effectiveness of capturing topic-level academic influence in scientific literatures.