This article will introduce you to a range of introductory concepts used in artificial intelligence for games (or'Game AI' for short) so that you can understand what tools are available for approaching your AI problems, how they work together, and how you might start to implement them in the language or engine of your choice. We're going to assume you have a basic knowledge of video games, and some grasp on mathematical concepts like geometry, trigonometry, etc. Most code examples will be in pseudo-code, so no specific programming language knowledge should be required. Game AI is mostly focused on which actions an entity should take, based on the current conditions. This is what the traditional AI literature refers to as controlling'intelligent agents' where the agent is usually a character in the game – but could also be a vehicle, a robot, or occasionally something more abstract such as a whole group of entities, or even a country or civilization. In each case it is a thing that ...

With advances in artificial intelligence impacting every industry from healthcare to retail, it's no wonder people are scared. After all, these pesky machines can already perform a great many tasks better than us humans and it's only going to get worse. I'm not just talking about replacing mindless busywork like sorting mail and processing tax returns – I'm talking about AI systems taking on complex jobs like forecasting financial markets, diagnosing medical patients, even making optimized hiring decisions, and doing it all better than highly trained humans. Consider the field of radiology. To become a practicing radiologist in the US, an aspiring doctor must devote 4 years to undergraduate education, another 4 years to medical school and a final 4 years to a radiology residency program.

On the surface, preventing injuries to professional-caliber athletes would seem to have little in common with preventing operational failures for a machine (i.e., autonomous vehicle, locomotive, airplane, CT Scan). However, both athletes and machines deal with inter-twined complex systems (where the interactions of one complex system can have a ripple effect on others) that can have significant impact on their operational effectiveness. My son Max, the Director of Sport Science at Resilient Code and Chief Science Officer at Exsurgo Technologies, has turned into quite an analytics nerd (check him out on twitter at @strong_science, but he saves his good stuff for Instagram where you can follow him at "strong_by_science"). Max and Resilient Code co-founder Dr. Dustin Nabhan have been educating me on the use of Agent-based Models (ABM) as a technique to predict and prevent injuries in athletes, especially high-caliber professional athletes. Unlike the weekend warrior like myself, preventing career-ending injuries can translate into tens of millions of dollars of additional income for professional athletes[1]. Here is the conversation with Max that got me thinking more about the similarities between ABM and Digital Twins (Max's input is in drab, boring grey and mine is in cool, hip blue): Okay, probably a boring conversation for most families, but now you know what we talk about when we go out to eat! Yea, you don't wanna sit next to us… During these conversations with Max and Dustin, I was struck by the similarities in using ABM to prevent injuries in the same way that we use Digital Twins (or what we call Asset Avatars) to prevent machine breakdowns, failures, under-performance and unplanned downtime.

Everybody is taking a stab at designing artificial intelligence processors, or electronic chips that could become the brains of computers that act as if they were humans. The latest to tackle the task of designing AI chips is Vinod Dham, a former Intel executive known as the "father of the Pentium." He has teamed up with some younger chip designers to build RAP chips, or real AI processors. At AlphaICs, the team is creating a coprocessor chip that can do agent-based artificial intelligence. These RAP chips could one day be deployed in computing devices and autonomous cars to make decisions at lightning speeds, or in data centers on a massive scale.

In many service robot applications, such as intra-logistics, surveillance or stock monitoring, it is desirable for a collection of tasks to be allocated to a team of robots. In this paper, we address applications such as these where tasks are independent (there are no inter-task dependencies) and each task only requires a single robot to complete it. Most existing approaches for solving this class of problems divide the problem into separate task allocation (TA) and planning processes. TA determines which robot should complete which tasks, and planning determines how each task, or conjunction of tasks, should be completed. This separation is made to reduce the computational complexity of the problem. It allows each robot to plan separately for its own task set, avoiding the need for a joint planning model which is typically exponential in the number of team members. This separation also allows specialised algorithms to be used for the TA and planning parts, increasing the efficiency with which the task-directed behaviour of the team can be generated. When doing this, TA usually assumes a greatly simplified model of planning in order to be able to efficiently compute allocations. However, this separation also means that the TA process cannot be informed by the plans of the individual robots, which prevents it from exploiting opportunities, or avoiding hindrances, that are only evident once planning has been performed.

Machine learning models have been widely used in security applications such as intrusion detection, spam filtering, and virus or malware detection. However, it is well-known that adversaries are always trying to adapt their attacks to evade detection. For example, an email spammer may guess what features spam detection models use and modify or remove those features to avoid detection. There has been some work on making machine learning models more robust to such attacks. However, one simple but promising approach called {\em randomization} is underexplored. This paper proposes a novel randomization-based approach to improve robustness of machine learning models against evasion attacks. The proposed approach incorporates randomization into both model training time and model application time (meaning when the model is used to detect attacks). We also apply this approach to random forest, an existing ML method which already has some degree of randomness. Experiments on intrusion detection and spam filtering data show that our approach further improves robustness of random-forest method. We also discuss how this approach can be applied to other ML models.

Abstract-- This paper addresses the complete area coverage problem of a known environment by multiple-robots. Complete area coverage is the problem of moving an end-effector over all available space while avoiding existing obstacles. In such tasks, using multiple robots can increase the efficiency of the area coverage in terms of minimizing the operational time and increase the robustness in the face of robot attrition. Unfortunately, the problem of finding an optimal solution for such an area coverage problem with multiple robots is known to be NPcomplete. The first solution presented is a direct extension of an efficient single robot area coverage algorithm, based on an exact cellular decomposition. The second algorithm is a greedy approach that divides the area into equal regions and applies an efficient single-robot coverage algorithm to each region. Results indicate that our approaches provide good coverage distribution between robots and minimize the workload per robot, meanwhile ensuring complete coverage of the area. Index Terms-- Multiple and distributed robots, path planning, coverage.

Our new series labelled "DApp of the Week" regularly features the most innovative applications built on top of iExec, to showcase what can be achieved with the tools and librairies we have developed, and how you can already launch decentralized applications running on a decentralized cloud. The DApp of the Week is Cerebrum, and has been created by Salman Rahim. In the last years, established IT giants like Google, IBM, and Nvidia -- fueled by the abundance of data, algorithmic advances, and the usage of high-performance hard ware for parallel processing -- have begun bridging the gap between science and busi ness applications. The global market for AI-based services, software, and hardware is expected to grow at an astonishing annual rate of 15 to 25% and reach $130 billion by 2025. Most of the investment in AI consists of internal R&D spending by large, cash-rich digital-native companies like Amazon, Baidu, and Google, which raises an imminent danger in today's society: This decentralized artificial intelligence (DAI) offers a unique proposition that no other AI can offer: the democratization of AI.

Learning to cooperate with friends and compete with foes is a key component of multi-agent reinforcement learning. Typically to do so, one requires access to either a model of or interaction with the other agent(s). Here we show how to learn effective strategies for cooperation and competition in an asymmetric information game with no such model or interaction. Our approach is to encourage an agent to reveal or hide their intentions using an information-theoretic regularizer. We consider both the mutual information between goal and action given state, as well as the mutual information between goal and state. We show how to stochastically optimize these regularizers in a way that is easy to integrate with policy gradient reinforcement learning. Finally, we demonstrate that cooperative (competitive) policies learned with our approach lead to more (less) reward for a second agent in two simple asymmetric information games.

We consider the problem of mining signal temporal logical requirements from a dataset of regular (good) and anomalous (bad) trajectories of a dynamical system. We assume the training set to be labeled by human experts and that we have access only to a limited amount of data, typically noisy. We provide a systematic approach to synthesize both the syntactical structure and the parameters of the temporal logic formula using a two-steps procedure: first, we leverage a novel evolutionary algorithm for learning the structure of the formula; second, we perform the parameter synthesis operating on the statistical emulation of the average robustness for a candidate formula w.r.t. its parameters. We compare our results with our previous work [{BufoBSBLB14] and with a recently proposed decision-tree [bombara_decision_2016] based method. We present experimental results on two case studies: an anomalous trajectory detection problem of a naval surveillance system and the characterization of an Ineffective Respiratory effort, showing the usefulness of our work.