The autonomous car technology promises to replace human drivers with safer driving systems. But although autonomous cars can become safer than human drivers this is a long process that is going to be refined over time. Before these vehicles are deployed on urban roads a minimum safety level must be assured. Since the autonomous car technology is still under development there is no standard methodology to evaluate such systems. It is important to completely understand the technology that is being developed to design efficient means to evaluate it. In this paper we assume safety-critical systems reliability as a safety measure. We model an autonomous road vehicle as an intelligent agent and we approach its evaluation from an artificial intelligence perspective. Our focus is the evaluation of perception and decision making systems and also to propose a systematic method to evaluate their integration in the vehicle. We identify critical aspects of the data dependency from the artificial intelligence state of the art models and we also propose procedures to reproduce them.

This short paper provides a description of an architecture to acquisition and use of knowledge by intelligent agents over a restricted domain of the Internet Infrastructure. The proposed architecture is added to an intelligent agent deployment model over a very useful server for Internet Autonomous System administrators. Such servers, which are heavily dependent on arbitrary and eventual updates of human beings, become unreliable. This is a position paper that proposes three research questions that are still in progress.

Webstresser.org, a popular DDoS-for-Hire website service on Wednesday was taken down by authorities from the US, UK, Netherlands, and various other countries in a major international investigation and arrests have been made. The website is blamed for more than four million cyber attacks globally in the past three years and had over 134,000 registered users at the time of the takedown. The operation, dubbed "Operation Power OFF," targeted Webstresser.org, It involved law enforcement agencies from the Netherlands, United Kingdom, Serbia, Croatia, Spain, Italy, Germany, Australia, Hongkong, Canada, and United States of America, coordinating with Europol. The domain name was seized by the US Department of Defence.

Robots that navigate among pedestrians use collision avoidance algorithms to enable safe and efficient operation. Recent works present deep reinforcement learning as a framework to model the complex interactions and cooperation. However, they are implemented using key assumptions about other agents' behavior that deviate from reality as the number of agents in the environment increases. This work extends our previous approach to develop an algorithm that learns collision avoidance among a variety of types of dynamic agents without assuming they follow any particular behavior rules. This work also introduces a strategy using LSTM that enables the algorithm to use observations of an arbitrary number of other agents, instead of previous methods that have a fixed observation size. The proposed algorithm outperforms our previous approach in simulation as the number of agents increases, and the algorithm is demonstrated on a fully autonomous robotic vehicle traveling at human walking speed, without the use of a 3D Lidar.

We study information elicitation without verification (IEWV) and ask the following question: Can we achieve truthfulness in dominant strategy in IEWV? This paper considers two elicitation settings. The first setting is when the mechanism designer has access to a random variable that is a noisy or proxy version of the ground truth, with known biases. The second setting is the standard peer prediction setting where agents' reports are the only source of information that the mechanism designer has. We introduce surrogate scoring rules (SSR) for the first setting, which use the noisy ground truth to evaluate quality of elicited information, and show that SSR achieve truthful elicitation in dominant strategy. Built upon SSR, we develop a multi-task mechanism, dominant truth serum (DTS), to achieve truthful elicitation in dominant strategy when the mechanism designer only has access to agents' reports (the second setting). The method relies on an estimation procedure to accurately estimate the average bias in the reports of other agents. With the accurate estimation, a random peer agent's report serves as a noisy ground truth and SSR can then be applied to achieve truthfulness in dominant strategy. A salient feature of SSR and DTS is that they both quantify the quality or value of information despite lack of ground truth, just as proper scoring rules do for the with verification setting. Our work complements both the strictly proper scoring rule literature by solving the case where the mechanism designer only has access to a noisy or proxy version of the ground truth, and the peer prediction literature by achieving truthful elicitation in dominant strategy.

Linda Yeardly presents on some of the challenges of omnichannel customer service that agents have to face everyday.

We introduce and empirically evaluate two techniques aimed at enhancing the performance of multi-robot prioritized path planning. The first technique is the deterministic procedure for re-scheduling (as opposed to well-known approach based on random restarts), the second one is the heuristic procedure that modifies the search-space of the individual planner involved in the prioritized path finding.

Recently Anthony Evans, principal consultant with Computer Design & Integration, was recruited to come in halfway through what should have been a relatively straightforward project. The company wanted to deploy artificial intelligence at its customer service help desk to provide agents with a sort of "whisper agent" that would help the agents with questions about which they were unsure. Either the virtual agent would have the answer or it would escalate the question to a second tier of assistance. But something was off with the implementation pilot -- the whisper agent turned out to be only of marginal help to the desk agents. Eventually the team discovered where they went wrong, according to Evans.

Negotiation is a very common interaction between automated agents. Many common negotiation protocols work with cardinal utilities, even though ordinal preferences, which only rank the outcomes, are easier to elicit from humans. In this work we concentrate on negotiation with ordinal preferences over a finite set of outcomes. We study an intuitive protocol for bilateral negotiation, where the two parties make offers alternately. We analyze the negotiation protocol under different settings. First, we assume that each party has full information about the other party's preference order. We provide elegant strategies that specify a sub-game perfect equilibrium for the agents. We further show how the studied negotiation protocol almost completely implements a known bargaining rule. Finally, we analyze the no information setting. We study several solution concepts that are distribution-free, and analyze both the case where neither party knows the preference order of the other party, and the case where only one party is uninformed.