research-article Share on How post time and type affect user engagement on public profiles in the Arab World Authors: Haneen AlRawashdeh British University in Dubai British University in DubaiView Profile , Faten Shwedeh British University in Dubai British University in DubaiView Profile , Sherief Abdallah British University in Dubai British University in DubaiView Profile Authors Info & Claims ASONAM '17: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017July 2017 Pages 554–561https://doi.org/10.1145/3110025.3110108Published:31 July 2017Publication History 0citation82DownloadsMetricsTotal Citations0Total Downloads82Last 12 Months16Last 6 weeks1 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access

Q-learning (QL) is a popular reinforcement learning algorithm that is guaranteed to converge to optimal policies in Markov decision processes. However, QL exhibits an artifact: in expectation, the effective rate of updating the value of an action depends on the probability of choosing that action. In other words, there is a tight coupling between the learning dynamics and underlying execution policy. This coupling can cause performance degradation in noisy non-stationary environments. Here, we introduce Repeated Update Q-learning (RUQL), a learning algorithm that resolves the undesirable artifact of Q-learning while maintaining simplicity. We analyze the similarities and differences between RUQL, QL, and the closest state-of-the-art algorithms theoretically. Our analysis shows that RUQL maintains the convergence guarantee of QL in stationary environments, while relaxing the coupling between the execution policy and the learning dynamics. Experimental results confirm the theoretical insights and show how RUQL outperforms both QL and the closest state-of-the-art algorithms in noisy non-stationary environments.

Handwritten Arabic character recognition systems face several challenges, including the unlimited variation in human handwriting and the unavailability of large public databases of handwritten characters and words. The use of synthetic data for training and testing handwritten character recognition systems is one of the possible solutions to provide several variations for these characters and to overcome the lack of large databases. While this can be using arbitrary distortions, such as image noise and randomized affine transformations, such distortions are not realistic. In this work, we model real distortions in handwriting using real handwritten Arabic character examples and then use these distortion models to synthesize handwritten examples that are more realistic. We show that the use of our proposed approach leads to significant improvements across different machine-learning classification algorithms.

Networks are seen everywhere in our modern life, including the Internet, the Grid, P2P file sharing, and sensor networks. Consequently, researchers in Artificial Intelligence (and Multi-Agent Systems in particular) have been actively seeking methods for optimizing the performance of these networks. A promising yet challenging optimization direction is multi-agent learning: allowing agents to adapt their behavior through interaction with one another. However, understanding the dynamics of an adaptive agent network is complicated due to the large number of system parameters, the concurrency by which the system parameters change, and the delay in the effect/consequence of parameter changes. All these factors make it hard to understand why an adaptive network of agents performed well at some time and poorly at another. In this paper we present a software tool that enables researchers in the multi-agent systems field to visualize and analyze the evolution of adaptive networks. The proposed software customizes and implements techniques from data mining and social network analysis research and augment these techniques in order to analyze local agent behaviors. We use our tool to analyze two domains. In both domains we are able to report and explain interesting observations using our tool.

Several multiagent reinforcement learning (MARL) algorithms have been proposed to optimize agents' decisions. Due to the complexity of the problem, the majority of the previously developed MARL algorithms assumed agents either had some knowledge of the underlying game (such as Nash equilibria) and/or observed other agents actions and the rewards they received. We introduce a new MARL algorithm called the Weighted Policy Learner (WPL), which allows agents to reach a Nash Equilibrium (NE) in benchmark 2-player-2-action games with minimum knowledge. Using WPL, the only feedback an agent needs is its own local reward (the agent does not observe other agents actions or rewards). Furthermore, WPL does not assume that agents know the underlying game or the corresponding Nash Equilibrium a priori. We experimentally show that our algorithm converges in benchmark two-player-two-action games. We also show that our algorithm converges in the challenging Shapley's game where previous MARL algorithms failed to converge without knowing the underlying game or the NE. Furthermore, we show that WPL outperforms the state-of-the-art algorithms in a more realistic setting of 100 agents interacting and learning concurrently. An important aspect of understanding the behavior of a MARL algorithm is analyzing the dynamics of the algorithm: how the policies of multiple learning agents evolve over time as agents interact with one another. Such an analysis not only verifies whether agents using a given MARL algorithm will eventually converge, but also reveals the behavior of the MARL algorithm prior to convergence. We analyze our algorithm in two-player-two-action games and show that symbolically proving WPL's convergence is difficult, because of the non-linear nature of WPL's dynamics, unlike previous MARL algorithms that had either linear or piece-wise-linear dynamics. Instead, we numerically solve WPL's dynamics differential equations and compare the solution to the dynamics of previous MARL algorithms.

This paper discusses a solution to the problems posed by sensor resource allocation in an adaptive, distributed radar array. We have formulated a variant of the classic resource allocation problem, called the setting-based resource allocation problem, which reflects the challenges posed in domains in which sensors have multiple settings, each of which could be useful to multiple tasks. Further, we have implemented a solution to this problem that takes advantage of the locality of resources and tasks that is common to such domains. This solution involves translating tasks and possible resource configurations into bids that can be solved by a modified combinatorial auction, thus allowing us to make use of recent developments in the solution of such auctions. We have also developed an information-theoretic procedure for accomplishing this translation, which models the effect various sensor settings would have on the network's output.