A key question in cooperative game theory is that of coalitional stability, usually captured by the notion of the \emph{core}--the set of outcomes such that no subgroup of players has an incentive to deviate. However, some coalitional games have empty cores, and any outcome in such a game is unstable. In this paper, we investigate the possibility of stabilizing a coalitional game by using external payments. We consider a scenario where an external party, which is interested in having the players work together, offers a supplemental payment to the grand coalition (or, more generally, a particular coalition structure). This payment is conditional on players not deviating from their coalition(s). The sum of this payment plus the actual gains of the coalition(s) may then be divided among the agents so as to promote stability. We define the \emph{cost of stability (CoS)} as the minimal external payment that stabilizes the game. We provide general bounds on the cost of stability in several classes of games, and explore its algorithmic properties. To develop a better intuition for the concepts we introduce, we provide a detailed algorithmic study of the cost of stability in weighted voting games, a simple but expressive class of games which can model decision-making in political bodies, and cooperation in multiagent settings. Finally, we extend our model and results to games with coalition structures.

We present a procedure for effective estimation of entropy and mutual information from small-sample data, and apply it to the problem of inferring high-dimensional gene association networks. Specifically, we develop a James-Stein-type shrinkage estimator, resulting in a procedure that is highly efficient statistically as well as computationally. Despite its simplicity, we show that it outperforms eight other entropy estimation procedures across a diverse range of sampling scenarios and data-generating models, even in cases of severe undersampling. We illustrate the approach by analyzing E. coli gene expression data and computing an entropy-based gene-association network from gene expression data. A computer program is available that implements the proposed shrinkage estimator.

Music genre classification is an essential tool for music information retrieval systems and it has been finding critical applications in various media platforms. Two important problems of the automatic music genre classification are feature extraction and classifier design. This paper investigates inter-genre similarity modelling (IGS) to improve the performance of automatic music genre classification. Inter-genre similarity information is extracted over the mis-classified feature population. Once the inter-genre similarity is modelled, elimination of the inter-genre similarity reduces the inter-genre confusion and improves the identification rates. Inter-genre similarity modelling is further improved with iterative IGS modelling(IIGS) and score modelling for IGS elimination(SMIGS). Experimental results with promising classification improvements are provided.

Graph theory provides a primary tool for analyzing and designing computer communication networks. In the past few decades, Graph theory has been used to study various types of networks, including the Internet, wide Area Networks, Local Area Networks, and networking protocols such as border Gateway Protocol, Open shortest Path Protocol, and Networking Networks. In this paper, we present some key graph theory concepts used to represent different types of networks. Then we describe how networks are modeled to investigate problems related to network protocols. Finally, we present some of the tools used to generate graph for representing practical networks.

This paper describes a novel approach based on a combination of techniques in AI, parallel computing, and network science to address an important problem in social sciences and public health: planning and responding in the event of epidemics. Spread of infectious disease is an important societal problem -- human behavior, social networks, and the civil infrastructures all play a crucial role in initiating and controlling such epidemic processes.  We specifically consider the economic and social effects of realistic interventions  proposed and adopted by public health officials and behavioral changes  of  private citizens in the event of a ``flu-like'' epidemic. Our results provide new insights for developing robust public policies that can prove useful for epidemic planning.

The use of statistical AI techniques in authorship recognition (or stylometry) has contributed to literary and historical breakthroughs. These successes have led to the use of these techniques in criminal investigations and prosecutions.  However, few have studied adversarial attacks and their devastating effect on the robustness of existing classification methods. This paper presents a framework for adversarial attacks including obfuscation attacks, where a subject attempts to hide their identity imitation attacks, where a subject attempts to frame another subject by imitating their writing style.  The major contribution of this research is that it demonstrates that both attacks work very well.  The obfuscation attack reduces the effectiveness of the techniques to the level of random guessing and the imitation attack succeeds with 68-91% probability depending on the stylometric technique used.  These results are made more significant by the fact that the experimental subjects were unfamiliar with stylometric techniques, without specialized knowledge in linguistics, and spent little time on the attacks. This paper also provides another significant contribution to the field in using human subjects to empirically validate the claim of high accuracy for current techniques (without attacks) by reproducing results for three representative stylometric methods.

This paper illustrates how we create a software agent by employing FALCON, a self-organizing neural network that performs reinforcement learning, to play a well-known first person shooter computer game known as Unreal Tournament 2004. Through interacting with the game environment and its opponents, our agent learns in real-time without any human intervention. Our agent bot participated in the 2K Bot Prize competition, similar to the \emph{Turing test} for intelligent agents, wherein human judges were tasked to identify whether their opponents in the game were human players or virtual agents. To perform well in the competition, an agent must act like human and be able to adapt to some changes made to the game. Although our agent did not emerge top in terms of human-like, the overall performance of our agent was encouraging as it acquired the highest game score while staying convincing to be human-like in some judges' opinions.

We discuss experiences from evaluating the learning performance of a user-adaptive personal assistant agent.  We discuss the challenge of designing adequate evaluation and the tension of collecting adequate data without a fully functional, deployed system.  Reflections on negative and positive experiences point to the challenges of evaluating user-adaptive AI systems.  Lessons learned concern early consideration of evaluation and deployment, characteristics of AI technology and domains that make controlled evaluations appropriate or not, holistic experimental design, implications of "in the wild" evaluation, and the effect of AI-enabled functionality and its impact upon existing tools and work practices.

We present an implementation of a plan adaptation system, BioPlanner, built for biological pathway prediction across species. BioPlanner formulates a pathway discovery problem as a Hierarchical Task Network (HTN) planning problem and solves it by adapting a plan solution of another well-studied pathway. BioPlanner provides the following functionalities: It automatically builds HTN planning models for a biological pathway domain from the semantic web biological knowledge bases (KBs). It retrieves plan cases from the biological KBs. It generates hypothetical pathways using plan adaptation strategies with the aid of biological domain knowledge. It evaluates the hypothetical plan candidates, ranks them, and recommends the most likely hypotheses to users. It employs an information gathering multi-agent system to capture knowledge from heterogeneous sources to help the hypothetical plan generation process. We utilize BioPlanner to predict Signaling Transduction pathways for Mus musculus, Gallus gallus, and Drosophila melanogaster from Homo sapiens.

Many city governments are under pressure to optimize the utilization of their resources to respond to fire, rescue and medical emergencies. In this paper we describe a simulation-based optimization software called SOFER that learns from a history of emergency requests to optimize the placement of resources and response policies. We describe a two-level random-restart hill climbing approach that yields policies which perform better than the current practice, satisfy the usability constraints, and are sensitive to optimization metrics and population changes. Some of the policies learned by the system give insight into response practices that would otherwise be counterintuitive.