A key requirement for the current generation of artificial decision-makers is that they should adapt well to changes in unexpected situations. This paper addresses the situation in which an AI for aerial dog fighting, with tunable parameters that govern its behavior, must optimize behavior with respect to an objective function that is evaluated and learned through simulations. Bayesian optimization with a Gaussian Process surrogate is used as the method for investigating the objective function. One key benefit is that during optimization, the Gaussian Process learns a global estimate of the true objective function, with predicted outcomes and a statistical measure of confidence in areas that haven't been investigated yet. Having a model of the objective function is important for being able to understand possible outcomes in the decision space; for example this is crucial for training and providing feedback to human pilots. However, standard Bayesian optimization does not perform consistently or provide an accurate Gaussian Process surrogate function for highly volatile objective functions. We treat these problems by introducing a novel sampling technique called Hybrid Repeat/Multi-point Sampling. This technique gives the AI ability to learn optimum behaviors in a highly uncertain environment. More importantly, it not only improves the reliability of the optimization, but also creates a better model of the entire objective surface. With this improved model the agent is equipped to more accurately/efficiently predict performance in unexplored scenarios.

I had an interesting chat with Michael Fauscette, the chief research officer for G2 Crowd, a site that focuses on reviewing business software and services. His research identified a major trend, the application of artificial intelligence (AI) for HR. I have a degree in this area, and my first career path, which didn't survive college, was supposed to be in HR so the topic interested me. As we chatted it became clear that Michael and I were in agreement about the future of AI. We both believe that soon we'll be up to our armpits in AI agents in virtually every area of business operations, and IT shops should be at the very least anticipate the possibility that robots may start replacing employees.

In multiagent systems, social choice functions can help aggregate the distinct preferences that agents have over alternatives, enabling them to settle on a single choice. Despite the basic manipulability of all reasonable voting systems, it would still be desirable to find ways to reach plausible outcomes, which are stable states, i.e., a situation where no agent would wish to change its vote. One possibility is an iterative process in which, after everyone initially votes, participants may change their votes, one voter at a time. This technique, explored in previous work, converges to a Nash equilibrium when Plurality voting is used, along with a tie-breaking rule that chooses a winner according to a linear order of preferences over candidates. In this paper, we both consider limitations of the iterative voting method, as well as expanding upon it. We demonstrate the significance of tie-breaking rules, showing that no iterative scoring rule converges for all tie-breaking. However, using a restricted tie-breaking rule (such as the linear order rule used in previous work) does not by itself ensure convergence. We prove that in addition to plurality, the veto voting rule converges as well using a linear order tie-breaking rule. However, we show that these two voting rules are the only scoring rules that converge, regardless of tie-breaking mechanism.

Delegates at an event hosted by Accenture last month in the Conrad Hotel in lower Manhattan were welcomed and registered by a smiling Amelia. But Amelia does not work only in events. She is a hologram -- a cognitive agent who can take on a wide variety of service desk roles, emulating human intelligence and capable of natural interaction with people. The age of automation and artificial intelligence (AI) has been predicted for decades -- and it may finally be arriving, thanks to the explosion in data, the fuel that powers the AI machines. Consulting firms are not only implementing automation and AI for clients but also using it to transform their own back-office functions and operations.

Hoping to teach AI agents the common sense they need to solve arbitrary tasks without specific training, OpenAI on Monday will introduce Universe, a collection of virtualized video games, browser interfaces, and applications that serve as a training ground for code-based decision making. Universe is open-source middleware that supports Gym, the organization's toolkit for developing and evaluating reinforcement learning (RL) algorithms. RL is used to train software perform specific actions, such as playing a videogame or making a 3D model walk, under a framework that prioritizes actions through a reward scheme. Universe aims to accelerate the education of AI agents by broadening the number of available training resources. Previously, according to OpenAI, the largest RL resource consisted of 55 Atari games, the Atari Learning Environment.

ABSTRACT: Despite much scientific evidence, a large fraction of the American public doubts that greenhouse gases are causing global warming. We present a simulation model as a computational test-bed for climate prediction markets. Traders adapt their beliefs about future temperatures based on the profits of other traders in their social network. We simulate two alternative climate futures, in which global temperatures are primarily driven either by carbon dioxide or by solar irradiance. These represent, respectively, the scientific consensus and a hypothesis advanced by prominent skeptics.

Social commitment protocols regulate interactions of agents in multiagent systems. Several methods have been developed to analyze properties of commitment protocols. However, analysis of an agent's behavior in a commitment protocol, which should take into account the agent's goals and beliefs, has received less attention. In this paper we present ProMoca framework to address this issue. Firstly, we develop an expressive formal language to model agents with respect to their commitments. Our language provides dedicated elements to define commitment protocols, and model agents in terms of their goals, behaviors, and beliefs. Furthermore, our language provides probabilistic and non-deterministic elements to model uncertainty in agents' beliefs. Secondly, we identify two essential properties of an agent with respect to a commitment protocol, namely compliance and goal satisfaction. We formalize these properties using a probabilistic variant of linear temporal logic. Thirdly, we adapt a probabilistic model checking algorithm to automatically analyze compliance and goal satisfaction properties. Finally, we present empirical results about efficiency and scalability of ProMoca.

The aim of this paper is to investigate the interplay between knowledge shared by a group of agents and its coalition ability. We investigate this relation in the standard context of imperfect information concurrent game. We assume that whenever a set of agents form a coalition to achieve a goal, they share their knowledge before acting. Based on this assumption, we propose a new semantics for alternating-time temporal logic with imperfect information and perfect recall. It turns out that this semantics is sufficient to preserve all the desirable properties of coalition ability in traditional coalitional logics. Meanwhile, we investigate how knowledge sharing within a group of agents contributes to its coalitional ability through the interplay of epistemic and coalition modalities. This work provides a partial answer to the question: which kind of group knowledge is required for a group to achieve their goals in the context of imperfect information.

Artificial Intelligence (AI) can help us in many ways: it can perform hard, dangerous or boring work for us, can help us to save lives and cope with disasters, can entertain us and make our daily life more comfortable. Advances in AI are occurring at high speed. The potential risks and problems of AI technology are filling newspapers (e.g. However, rather than being a threat to our existence or plotting to take over the rule of the world, AI is already changing our daily lives, almost entirely in ways that improve human health, safety, and productivity. In the coming years we can expect AI systems to be used increasingly in domains such as transportation, service robots, healthcare, education, low-resource communities, public safety and security, employment and workplace, and entertainment (100 Year AI report).

Autonomous AI agents have begun to take over entire tasks start to finish. And this is just the beginning. We expect to see a plethora of such agents in the next half decade, and these will take on all sorts of tasks that humans currently perform, if unhappily. In many ways the machines will be better at these tasks than we are. Self-driving cars have superhuman sensors, reaction time and true multitasking, and they will always apply their full attention to the task at hand, driving!