One of the basic motivations for solving DCOPs is maintaining agents' privacy. Thus, researchers have evaluated the privacy loss of DCOP algorithms and defined corresponding notions of privacy preservation for secured DCOP algorithms. However, no secured protocol was proposed for Max-Sum, which is among the most studied DCOP algorithms. As part of the ongoing effort of designing secure DCOP algorithms, we propose P-Max-Sum, the first private algorithm that is based on Max-Sum. The proposed algorithm has multiple agents preforming the role of each node in the factor graph, on which the Max-Sum algorithm operates. P-Max-Sum preserves three types of privacy: topology privacy, constraint privacy, and assignment/decision privacy. By allowing a single call to a trusted coordinator, P-Max-Sum also preserves agent privacy. The two main cryptographic means that enable this privacy preservation are secret sharing and homomorphic encryption. In addition, we design privacy-preserving implementations of four variants of Max-Sum. We conclude by analyzing the price of privacy in terns of runtime overhead, both theoretically and by extensive experimentation.

Sponsored Search Auctions (SSAs) are one of the most successful applications of microeconomic mechanisms, with a revenue of about $72 billion in the US alone in 2016. However, the problem of designing the best economic mechanism for sponsored search auctions is far from being solved, and, given the amount at stake, it is no surprise that it has received growing attention over the past few years. The most common auction mechanism for SSAs is the Generalized Second Price (GSP). However, the GSP is known not to be truthful: the agents participating in the auction might have an incentive to report false values, generating economic inefficiency and suboptimal revenues in turn. Superior, efficient truthful mechanisms, such as the Vickrey-Clarke-Groves (VCG) auction, are well known in the literature. However, while the VCG auction is currently adopted for the strictly related scenario of contextual advertising, e.g., by Google and Facebook, companies are reluctant to extend it to SSAs, fearing prohibitive switching costs. Other than truthfulness, two issues are of paramount importance in designing effective SSAs. First, the choice of the user model; not only does an accurate user model better target ads to users, it also is a critical factor in reducing the inefficiency of the mechanism. Often an antagonist to this, the second issue is the running time of the mechanism, given the performance pressure these mechanisms undertake in real-world applications. In our work, we argue in favor of adopting the VCG mechanism based on the cascade model with ad/position externalities (APDC-VCG). Our study includes both the derivation of inefficiency bounds and the design and the experimental evaluation of exact and approximate algorithms.

In this paper, we demonstrate the application of Fuzzy Markup Language (FML) to construct an FML-based Dynamic Assessment Agent (FDAA), and we present an FML-based Human-Machine Cooperative System (FHMCS) for the game of Go. The proposed FDAA comprises an intelligent decision-making and learning mechanism, an intelligent game bot, a proximal development agent, and an intelligent agent. The intelligent game bot is based on the open-source code of Facebook Darkforest, and it features a representational state transfer application programming interface mechanism. The proximal development agent contains a dynamic assessment mechanism, a GoSocket mechanism, and an FML engine with a fuzzy knowledge base and rule base. The intelligent agent contains a GoSocket engine and a summarization agent that is based on the estimated win rate, real-time simulation number, and matching degree of predicted moves. Additionally, the FML for player performance evaluation and linguistic descriptions for game results commentary are presented. We experimentally verify and validate the performance of the FDAA and variants of the FHMCS by testing five games in 2016 and 60 games of Google Master Go, a new version of the AlphaGo program, in January 2017. The experimental results demonstrate that the proposed FDAA can work effectively for Go applications.

Many real-world tasks involve multiple agents with partial observability and limited communication. Learning is challenging in these settings due to local viewpoints of agents, which perceive the world as non-stationary due to concurrently-exploring teammates. Approaches that learn specialized policies for individual tasks face problems when applied to the real world: not only do agents have to learn and store distinct policies for each task, but in practice identities of tasks are often non-observable, making these approaches inapplicable. This paper formalizes and addresses the problem of multi-task multi-agent reinforcement learning under partial observability. We introduce a decentralized single-task learning approach that is robust to concurrent interactions of teammates, and present an approach for distilling single-task policies into a unified policy that performs well across multiple related tasks, without explicit provision of task identity.

Data-driven management has risen sharply from a decade ago, when Thomas Davenport wrote Competing on Analytics.Data is now the critical tool for managing many corporate functions, including marketing, pricing, supply chain, operations, and more. This movement is being further fueled by the promise of AI and machine learning, and by the ease of collecting and storing data about every facet of our daily lives. But has the pendulum swung too far? Are managers relying excessively on data to guide their decisions, abdicating their own knowledge and experience? One possible solution may be found in Agent-Based Simulation(ABS), a novel approach to solving complex business problems through computer simulations.

In popular media, "Artificial Intelligence" is by turns godlike, monstrous, uncannily human and a hoax; it inspires both awe and deep suspicion - it's unnatural. Researchers who actually develop AI technologies - like those at PROWLER.io - prefer narrower, more useful terms like Machine Learning (ML) and decision theory. They're wary of the catch-all phrase "Artificial Intelligence", in part because human intelligence is itself largely artificial, an encoded system of man-made concepts, rules of thumb, recipes, customs, laws, even whole cultures. Humans have always used thinking tools, rules and systems to keep chaos at bay. Turn off the traffic lights in central London and you'll soon see how far "natural" intelligence gets us in a complex system.

High-speed, low-latency obstacle avoidance that is insensitive to sensor noise is essential for enabling multiple decentralized robots to function reliably in cluttered and dynamic environments. While other distributed multi-agent collision avoidance systems exist, these systems require online geometric optimization where tedious parameter tuning and perfect sensing are necessary. We present a novel end-to-end framework to generate reactive collision avoidance policy for efficient distributed multi-agent navigation. Our method formulates an agent's navigation strategy as a deep neural network mapping from the observed noisy sensor measurements to the agent's steering commands in terms of movement velocity. We train the network on a large number of frames of collision avoidance data collected by repeatedly running a multi-agent simulator with different parameter settings. We validate the learned deep neural network policy in a set of simulated and real scenarios with noisy measurements and demonstrate that our method is able to generate a robust navigation strategy that is insensitive to imperfect sensing and works reliably in all situations. We also show that our method can be well generalized to scenarios that do not appear in our training data, including scenes with static obstacles and agents with different sizes. Videos are available at https://sites.google.com/view/deepmaca.

Understanding the evolution of human society, as a complex adaptive system, is a task that has been looked upon from various angles. In this paper, we simulate an agent-based model with a high enough population tractably. To do this, we characterize an entity called \textit{society}, which helps us reduce the complexity of each step from $\mathcal{O}(n^2)$ to $\mathcal{O}(n)$. We propose a very realistic setting, where we design a joint alternate maximization step algorithm to maximize a certain \textit{fitness} function, which we believe simulates the way societies develop. Our key contributions include (i) proposing a novel protocol for simulating the evolution of a society with cheap, non-optimal joint alternate maximization steps (ii) providing a framework for carrying out experiments that adhere to this joint-optimization simulation framework (iii) carrying out experiments to show that it makes sense empirically (iv) providing an alternate justification for the use of \textit{society} in the simulations.

In recent years, AI-based applications have increasingly been used in real-world domains. For example, game theory-based decision aids have been successfully deployed in various security settings to protect ports, airports, and wildlife. This article describes our unique problem-to-project educational approach that used games rooted in real-world issues to teach AI concepts to diverse audiences. Specifically, our educational program began by presenting real-world security issues, and progressively introduced complex AI concepts using lectures, interactive exercises, and ultimately hands-on games to promote learning. We describe our experience in applying this approach to several audiences, including students of an urban public high school, university undergraduates, and security domain experts who protect wildlife. We evaluated our approach based on results from the games and participant surveys.

The AAAI 2016 Fall Symposium Series was held Thursday through Saturday, November 17–19, at the Westin Arlington Gateway in Arlington, Virginia adjacent to Washington, DC. The titles of the six symposia were Accelerating Science: A Grand Challenge for AI; Artificial Intelligence for Human-Robot Interaction, Cognitive Assistance in Government and Public Sector Applications, Cross-Disciplinary Challenges for Autonomous Systems, Privacy and Language Technologies, Shared Autonomy in Research and Practice. The highlights of each (except Acceleration Science) symposium are presented in this report.