Generating realistic urban environments by scattering or placing buildings on maps is a challenging problem. Unlike the existing procedural methods, we employ a data-driven approach to this problem. We combine two recent advances in machine learning techniques, Generative Latent optimization (GLO) together with adversarial training, to learn a model that can easily generate and place buildings on a given map. Such a model enables its users, particularly artists, to easily generate areas with specific styles, e.g. residential or commercial, just by providing examples. In contrast, traditional procedural methods require lengthy manual tuning of hyper-parameters. Using a more flexible method like ours allows artists to iterate over their designs of urban layouts much faster. Finally, our experiments on real-world data show that our method outperforms state-of-the-art methods in visual quality and can better match the underlying distribution of the building placements.

Recently, there have been several high-profile achievements of agents learning to play games against humans and beat them. In this article, we study the problem of training intelligent agents in service of game development. Unlike the agents built to “beat the game,” our agents aim to produce human-like behavior to help with game evaluation and balancing. We discuss two fundamental metrics based on which we measure the human-likeness of agents, namely skill and style, which are multifaceted concepts with practical implications outlined in this article. We report four case studies in which the style and skill requirements inform the choice of algorithms and metrics used to train agents; ranging from A* search to state-of-the-art deep reinforcement learning (RL). Furthermore, we, show that the learning potential of state-of-the-art deep RL models does not seamlessly transfer from the benchmark environments to target ones without heavily tuning their hyperparameters, leading to linear scaling of the engineering efforts, and computational cost with the number of target domains.

Games have been a major focus of AI since the field formed seventy years ago. Recently, video games have replaced chess and go as the current "Mt. Everest Problem." This paper looks beyond the video games themselves to the application of AI techniques within the ecosystems that produce them. Electronic Arts (EA) must deal with AI at scale across many game studios as it develops many AAA games each year, and not a single, AI-based, flagship application. EA has adopted a horizontal scaling strategy in response to this challenge and built a platform for delivering AI artifacts anywhere within EA's software universe. By combining a data warehouse for player history, an Agent Store for capturing processes acquired through machine learning, and a recommendation engine as an action layer, EA has been delivering a wide range of AI solutions throughout the company during the last two years. These solutions, such as dynamic difficulty adjustment, in-game content and activity recommendations, matchmaking, and game balancing, have had major impact on engagement, revenue, and development resources within EA.

The existence of considerable amount of redundancy in the Internet traffic at the packet level has stimulated the deployment of packet-level redundancy elimination techniques within the network by enabling network nodes to memorize data packets. Redundancy elimination results in traffic reduction which in turn improves the efficiency of network links. In this paper, the concept of network compression is introduced that aspires to exploit the statistical correlation beyond removing large duplicate strings from the flow to better suppress redundancy. In the first part of the paper, we introduce “memory-assisted compression,” which utilizes the memorized content within the network to learn the statistics of the information source generating the packets which can then be used toward reducing the length of codewords describing the packets emitted by the source. Using simulations on data gathered from real network traces, we show that memory-assisted compression can result in significant traffic reduction. In the second part of the paper, we study the scaling of the average network-wide benefits of memory-assisted compression. We discuss routing and memory placement problems in network for the reduction of overall traffic. We derive a closed-form expression for the scaling of the gain in Erdös-Rényi random network graphs, where obtain a threshold value for the number of memories deployed in a random graph beyond which network-wide benefits start to shine. Finally, the network-wide benefits are studied on Internet-like scale-free networks. We show that non-vanishing network compression gain is obtained even when only a tiny fraction of the total number of nodes in the network are memory-enabled.

Traces derived from real-world traffic show that significant redundancy exists at the packet level in mobile network traffic. This has inspired new solutions to suppress the redundancy present in the packet data to manage the explosive traffic. In this paper, we propose a novel approach to performing redundancy elimination by employing universal compression using memory-enabled overhearing helpers without backhaul connectivity, referred to as wireless network compression. The helpers overhear the data packets previously sent by the wireless gateway to various mobile clients within their coverage and use them as side information to reduce the overall communication cost. We study wireless network compression via overhearing helpers from an information-theoretic point of view and conclude that this approach potentially offers a threefold benefit: 1) offloading the wireless gateway and hence increasing the maximum number of mobile nodes the gateway can reliably serve; 2) reducing the average packet delay; and 3) improving the overall throughput in the network.

Rapid growth of emerging medical applications such as e-health and tele-medicine requires fast, low cost, and often lossless access to massive amount of medical images and data over bandlimited channels. In this paper, we first show that significant amount of correlation and redundancy exist across different medical images. Such a correlation can be utilized to achieve better compression, and consequently less storage and less communication overhead on the network. We propose a novel memory-assisted compression technique, as a learning-based universal coding, which can be used to complement any existing algorithm to further eliminate redundancies across images. The approach is motivated by the fact that, often in medical applications, massive amount of correlated images from the same family are available as training data for learning the dependencies and deriving appropriate reference models. Such models can then be used for compression of any new image from the same family. In particular, Principal Component Analysis (PCA) is applied on a set of images from training data to form the required reference models. The proposed memory-assisted compression allows each image to be processed independently of other images, and hence allows individual image access and transmission. Experimental results on X-ray images show that the proposed algorithm achieves 20% improvement over and above traditional lossless image compression methods reported in the literature.

Molecular communication allows bio nodes to communicate and cooperate in an aqueous environment. We recently proposed an m-ary modulation scheme in which the information is encoded into the concentration of molecules emitted by the bio nodes. The performance of such scheme, among other factors, is limited by the maximum concentration of molecules that can be induced by the transmitter at the receiver. This paper investigates relaying to improve the reliability of such molecular communication. We consider the case that nodes consist of a population of biological agents and study the scenario in which the relay node decodes the incoming information symbol and forwards it to the destination using the same or a different type of molecules as the transmitter. We show how the use of relaying in molecular communication can increase the effective range of molecular concentration induced at the receiver and also can help with achieving diversity at the receiver. We use a generalized form of Maximum Ratio Combining (MRC) and show as to how the probability of error is improved using the optimal relaying. We also compare this scenario with the case that the relay node uses the same type of the molecule.

Recently, we proposed wireless network compression via memory-enabled overhearing helpers as an endeavor to reduce the traffic load on the wireless gateway via elimination of the redundant data in the network. In this setup, each memory-enabled helper overhears the data packets previously sent by the wireless gateway to various mobile clients within its coverage and uses them toward forming a model about the content of the packets from the traffic. The resulting model is then used as side information by the wireless network compression module in a two-part code to reduce the overall cost of delivering a packet to a client over links with asymmetric cost (where the helper-client link is far less costly than the gateway-client link). One main challenge in this scenario is the fact that memory-enabled overhearing helpers do not receive all of the sequences sent to the mobile clients (as there is no feedback in place in the overhearing link), resulting in mismatched side information between the encoder (i.e., gateway) and the helper. In this paper, we present an information theoretic formulation for the mismatched side information problem. We study this problem in the context of universal lossless compression and derive bounds on the average minimax redundancy of encoding each packet. Our results also lead to construction of coding schemes for the mismatched side information using two-part codes.

Recent studies have shown the existence of considerable amount of packet-level redundancy in the network flows. Since application-layer solutions cannot capture the packet-level redundancy, development of new content-aware approaches capable of redundancy elimination at the packet and sub-packet levels is necessary. These requirements motivate the redundancy elimination of packets from an information-theoretic point of view. For efficient compression of packets, a new framework called memory-assisted universal compression has been proposed. This framework is based on learning the statistics of the source generating the packets at some intermediate nodes and then leveraging these statistics to effectively compress a new packet. This paper investigates both theoretically and experimentally the memory-assisted compression of network packets. Clearly, a simple source cannot model the data traffic. Hence, we consider traffic from a complex source that is consisted of a mixture of simple information sources for our analytic study. We develop a practical code for memory-assisted compression and combine it with a proposed hierarchical clustering to better utilize the memory. Finally, we validate our results via simulation on real traffic traces. Memory-assisted compression combined with hierarchical clustering method results in compression of packets close to the fundamental limit. As a result, we report a factor of two improvement over traditional end-to-end compression.

This paper is eligible for the student paper award. Molecular communication between biological entities is a new paradigm in communications. Recently, we studied molecular communication between two nodes formed from synthetic bacteria. Due to high randomness in behavior of bacteria, we used a population of them in each node. The reliability of such communication systems depends on both the maximum concentration of molecules that a transmitter node is able to produce at the receiver node as well as the number of bacteria in each nodes. This maximum concentration of molecules falls with distance which makes the communication to the far nodes nearly impossible. In order to alleviate this problem, in this paper, we propose to use a molecular relaying node. The relay node can resend the message either by the different or the same type of molecules as the original signal from the transmitter. We study two scenarios of relaying. In the first scenario, the relay node simply senses the received concentration and forwards it to the receiver. We show that this sense and forward scenario, depending on the type of molecules used for relaying, results in either increasing the range of concentration of molecules at the receiver or increasing the effective number of bacteria in the receiver node. For both cases of sense and forward relaying, we obtain the resulting improvement in channel capacity. We conclude that multi-type molecular relaying outperforms the single-type relaying. In the second scenario, we study the decode and forward relaying for the M-ary signaling scheme. We show that this relaying strategy increases the reliability of M-ary communication significantly.

The design of biologically-inspired wireless communication systems using bacteria as the basic element of the system is initially motivated by a phenomenon called Quorum Sensing. Due to high randomness in the individual behavior of a bacterium, reliable communication between two bacteria is almost impossible. Therefore, we have recently proposed that a population of bacteria in a cluster is considered as a bio node in the network capable of molecular transmission and reception. This proposition enables us to form a reliable bio node out of many unreliable bacteria. In this paper, we study the communication between two nodes in such a network where information is encoded in the concentration of molecules by the transmitter. The molecules produced by the bacteria in the transmitter node propagate through the diffusion channel. Then, the concentration of molecules is sensed by the bacteria population in the receiver node which would decode the information and output light or fluorescent as a result. The uncertainty in the communication is caused by all three components of communication, i.e., transmission, propagation and reception. We study the theoretical limits of the information transfer rate in the presence of such uncertainties. Finally, we consider M-ary signaling schemes and study their achievable rates and corresponding error probabilities.

Recently, the existence of considerable amount of redundancy in the Internet traffic has stimulated the deployment of several redundancy elimination techniques within the network. These techniques are often based on either packet-level Redundancy Elimination (RE) or Content-Centric Networking (CCN). However, these techniques cannot exploit sub-packet redundancies. Further, other alternative techniques such as the end-to-end universal compression solutions would not perform well either over the Internet traffic, as such techniques require infinite length traffic to effectively remove redundancy. This paper proposes a memory-assisted universal compression technique that holds a significant promise for reducing the amount of traffic in the networks. The proposed work is based on the observation that if a source is to be compressed and sent over a network, the associated universal code entails a substantial overhead in transmission due to finite length traffic. However, intermediate nodes can learn the source statistics and this can be used to reduce the cost of describing the source statistics, reducing the transmission overhead for such traffics. We present two algorithms (statistical and dictionary-based) for the memory-assisted universal lossless compression of information sources. These schemes are universal in the sense that they do not require any prior knowledge of the traffic's statistical distribution. We demonstrate the effectiveness of both algorithms and characterize the memorization gain using the real Internet traces. Furthermore, we apply these compression schemes to Internet-like power-law graphs and solve the routing problem for compressed flows. We characterize the network-wide gain of the memorization from the information theoretic point of view. In particular, through our analysis on power-law graphs, we show that non-vanishing network-wide gain of memorization is obtained even when the number of memory units is a tiny fraction of the total number of nodes in the network. Finally, we validate our predictions of the memorization gain by simulation on real traffic traces.

Many applications require data processing to be performed on individual pieces of data which are of finite sizes, e.g., files in cloud storage units and packets in data networks. However, traditional universal compression solutions would not perform well over the finite-length sequences. Recently, we proposed a framework called memory-assisted universal compression that holds a significant promise for reducing the amount of redundant data from the finite-length sequences. The proposed compression scheme is based on the observation that it is possible to learn source statistics (by memorizing previous sequences from the source) at some intermediate entities and then leverage the memorized context to reduce redundancy of the universal compression of finite-length sequences. We first present the fundamental gain of the proposed memory-assisted universal source coding over conventional universal compression (without memorization) for a single parametric source. Then, we extend and investigate the benefits of the memory-assisted universal source coding when the data sequences are generated by a compound source which is a mixture of parametric sources. We further develop a clustering technique within the memory-assisted compression framework to better utilize the memory by classifying the observed data sequences from a mixture of parametric sources. Finally, we demonstrate through computer simulations that the proposed joint memorization and clustering technique can achieve up to 6-fold improvement over the traditional universal compression technique when a mixture of non-binary Markov sources is considered.

The design of biological networks using bacteria as the basic elements of the network is initially motivated by a phenomenon called quorum sensing. Through quorum sensing, each bacterium performs sensing the medium and communicating it to others via molecular communication. As a result, bacteria can orchestrate and act collectively and perform tasks impossible otherwise. In this paper, we consider a population of bacteria as a single node in a network. In our version of biological communication networks, such a node would communicate with one another via molecular signals. As a first step toward such networks, this paper focuses on the study of the transfer of information to the population (i.e., the node) by stimulating it with a concentration of special type of a molecules signal. These molecules trigger a chain of processes inside each bacteria that results in a final output in the form of light or fluorescence. Each stage in the process adds noise to the signal carried to the next stage. Our objective is to measure (compute) the maximum amount of information that we can transfer to the node. This can be viewed as the collective sensing capacity of the node. The molecular concentration, which carries the information, is the input to the node, which should be estimated by observing the produced light as the output of the node (i.e., the entire population of bacteria forming the node. The molecules are trapped in the bacteria receptors forming complexes inside the bacteria which affect the genes responsible for producing the light. We focus on the noise caused by the random process of trapping molecules at the receptors as well as the variation of outputs of different bacteria in the node. The optimal input distribution to maximize the mutual information between the output of the node, e.g., light, and the applied molecule concentration is derived. Further, the capacity variation with the number of bacteria in the node and the number of receptors per bacteria is obtained. Finally, we investigated the collective sensing capability of the node when a specific form of molecular signaling concentration (which resembles M-ary modulation) is used. The achievable sensing capacity and the corresponding error probabilities were obtained for such practical signaling techniques.

Molecular communication is an expanding body of research. Recent advances in biology have encouraged using genetically engineered bacteria as the main component in the molecular communication. This has stimulated a new line of research that attempts to study molecular communication among bacteria from an information-theoretic point of view. Due to high randomness in the individual behavior of the bacterium, reliable communication between two bacteria is almost impossible. Therefore, we recently proposed that a population of bacteria in a cluster is considered as a node capable of molecular transmission and reception. This proposition enables us to form a reliable node out of many unreliable bacteria. The bacteria inside a node sense the environment and respond accordingly. In this paper, we study the communication between two nodes, one acting as the transmitter and the other as the receiver. We consider the case in which the information is encoded in the concentration of molecules by the transmitter. The molecules produced by the bacteria in the transmitter node propagate in the environment via the diffusion process. Then, their concentration sensed by the bacteria in the receiver node would decode the information. The randomness in the communication is caused by both the error in the molecular production at the transmitter and the reception of molecules at the receiver. We study the theoretical limits of the information transfer rate in such a setup versus the number of bacteria per node. Finally, we consider M-ary modulation schemes and study the achievable rates and their error probabilities.

In diffusion-based molecular communications, messages can be conveyed via the variation in the concentration of molecules in the medium. In this paper, we intend to analyze the achievable capacity in transmission of information from one node to another in a diffusion channel. We observe that because of the molecular diffusion in the medium, the channel possesses memory. We then model the memory of the channel by a two-step Markov chain and obtain the equations describing the capacity of the diffusion channel. By performing a numerical analysis, we obtain the maximum achievable rate for different levels of the transmitter power, i.e., the molecule production rate.

A diffusion-based molecular communication system has two major components: the diffusion in the medium, and the ligand-reception. Information bits, encoded in the time variations of the concentration of molecules, are conveyed to the receiver front through the molecular diffusion in the medium. The receiver, in turn, measures the concentration of the molecules in its vicinity in order to retrieve the information. This is done via ligand-reception process. In this paper, we develop models to study the constraints imposed by the concentration sensing at the receiver side and derive the maximum rate by which a ligand-receiver can receive information. Therefore, the overall capacity of the diffusion channel with the ligand receptors can be obtained by combining the results presented in this paper with our previous work on the achievable information rate of molecular communication over the diffusion channel.

Several studies have identified a significant amount of redundancy in the network traffic. For example, it is demonstrated that there is a great amount of redundancy within the content of a server over time. This redundancy can be leveraged to reduce the network flow by the deployment of memory units in the network. The question that arises is whether or not the deployment of memory can result in a fundamental improvement in the performance of the network. In this paper, we answer this question affirmatively by first establishing the fundamental gains of memory-assisted source compression and then applying the technique to a network. Specifically, we investigate the gain of memory-assisted compression in random network graphs consisted of a single source and several randomly selected memory units. We find a threshold value for the number of memories deployed in a random graph and show that if the number of memories exceeds the threshold we observe network-wide reduction in the traffic.

We consider the problem of distributing a file in a network of storage nodes whose storage budget is limited but at least equals the size file. We first generate T encoded symbols (from the file) which are then distributed among the nodes. We investigate the optimal allocation of T encoded packets to the storage nodes such that the probability of reconstructing the file by using any r out of n nodes is maximized. Since the optimal allocation of encoded packets is difficult to find in general, we find another objective function which well approximates the original problem and yet is easier to optimize. We find the optimal symmetric allocation for all coding redundancy constraints using the equivalent approximate problem. We also investigate the optimal allocation in random graphs. Finally, we provide simulations to verify the theoretical results.

In this paper, we address the problem of distributing a large amount of bulk data to a sparse vehicular network from roadside infostations, using efficient vehicle-to-vehicle collaboration. Due to the highly dynamic nature of the underlying vehicular network topology, we depart from architectures requiring centralized coordination, reliable MAC scheduling, or global network state knowledge, and instead adopt a distributed paradigm with simple protocols. In other words, we investigate the problem of reliable dissemination from multiple sources when each node in the network shares a limited amount of its resources for cooperating with others. By using rateless coding at the Road Side Unit (RSU) and using vehicles as data carriers, we describe an efficient way to achieve reliable dissemination to all nodes (even disconnected clusters in the network). In the nutshell, we explore vehicles as mobile storage devices. We then develop a method to keep the density of the rateless codes packets as a function of distance from the RSU at the desired level set for the target decoding distance. We investigate various tradeoffs involving buffer size, maximum capacity, and the mobility parameter of the vehicles.