Even though tech and customer expectations shift over time, causing changes in customer service, the core qualities of good service remain the same. The customer must find answers quickly and be satisfied with their experience/result. Four of these customer service trends are interconnected; mobile connectivity, self-service portals, improved human interactions, and refined virtual agents. The final trend is more of a "best practice" for companies to remind them of the importance of the customer experience which is crucial to any firm's long-term success. Mobile connectedness drives the other trends, because it sets customer expectations. They have a computer in their hand all day (and much of the night), and they expect instant responses and gratification.

From guide dogs for the visually impaired to search-and-rescue animals, canines can be trained to help with a wide range of critical tasks. So, it might come as no surprise that researchers are now designing machines to learn more like dogs. Computer scientists have modeled machines to learn like dogs, with the short-term goal of improving human interactions with robots and the long-term hope of more efficiently training service animals. These machines rely on human feedback. Real animal trainees, like dogs, also provide helpful, subtle cues about their understanding to human trainers, and now that aspect of a training relationship is being transferred to machine learning.

TechRadar and other tech sites are reporting that Google is thinking up a kill switch for dangerous AI. As TechRadar put it, humans can keep the upper hand, for now. David Nield reported: "Google is working on an AI'kill switch' that allows human operators to turn off super intelligent systems no matter how big their egos get. It's called "safe interruptibility" and it's being developed as part of the DeepMind system." An open letter went out last year that caused as much of a stir as the worrisome statements preceding it over building superintelligent machines.

Our ability to communicate with computers is constrained by the technical level of the hardware and software. In the early days, we used cryptic UNIX and DOS commands. Then came the graphical user interface (GUI) with mice, windows, and the still obligatory keyboard. In the case of Viv, the next generation AI agent beyond Siri, the agent actually writes its own code, in about 10 milliseconds, to enable it to answer a question or perform a task. This video provide a preview of how we'll interact with computers in the future.

The Council for Scientific and Industrial Research (CSIR) has partnered with five South African universities to launch the Centre for Artificial Intelligence Research (CAIR) on 24 May 2016. Based at the CSIR and chaired at the University of Cape Town, the centre aims to build research capacity in the field of artificial intelligence, and to ensure work reaches commercialisation. CAIR will also ensure artificial intelligence research is spread beyond the traditional institutions of the field. The five partner universities, or collaboration nodes, are the University of Cape Town (UCT), the University of Pretoria (UP), Stellenbosch University (SU), the University of KwaZulu-Natal (UKZN) and North-West University (NWU). The centre is funded by the CSIR, the Department of Science and Technology (DST), and the participating universities, and the wide research scope of the programme is evident by the variety of university departments involved.

Multi-Agent Path Finding (MAPF) is well studied in both AI and robotics. Given a discretized environment and agents with assigned start and goal locations, MAPF solvers from AI find collision-free paths for hundreds of agents with user-provided sub-optimality guarantees. However, they ignore that actual robots are subject to kinematic constraints (such as finite maximum velocity limits) and suffer from imperfect plan-execution capabilities. We therefore introduce MAPF-POST, a novel approach that makes use of a simple temporal network to postprocess the output of a MAPF solver in polynomial time to create a plan-execution schedule that can be executed on robots. This schedule works on non-holonomic robots, takes their maximum translational and rotational velocities into account, provides a guaranteed safety distance between them, and exploits slack to absorb imperfect plan executions and avoid time-intensive replanning in many cases. We evaluate MAPF-POST in simulation and on differential-drive robots, showcasing the practicality of our approach.

This paper presents a novel method for controlling teams of unmanned aerial vehicles using Stochastic Optimal Control (SOC) theory. The approach consists of a centralized high-level planner that computes optimal state trajectories as velocity sequences, and a platform-specific low-level controller which ensures that these velocity sequences are met. The planning task is expressed as a centralized path-integral control problem, for which optimal control computation corresponds to a probabilistic inference problem that can be solved by efficient sampling methods. Through simulation we show that our SOC approach (a) has significant benefits compared to deterministic control and other SOC methods in multimodal problems with noise-dependent optimal solutions, (b) is capable of controlling a large number of platforms in real-time, and (c) yields collective emergent behaviour in the form of flight formations. Finally, we show that our approach works for real platforms, by controlling a team of three quadrotors in outdoor conditions.

It is well understood that,through cooperation, multiple agents can achieve tasks that are unachievable by a single agent.However, there are no formal characterizations of situations where cooperation is required to achieve a goal, thus warranting the application of multiple agents. In this paper, we provide such a formal characterization for multi-agent planning problems with sequential action execution. We first show that determining whether there is required cooperation (RC) is in general intractable even in this limited setting. As a result, we start our analysis with a subset of more restrictive problems where agents are homogeneous.For such problems, we identify two conditions that can cause RC. We establish that when none of these conditions hold, the problem is single-agent solvable;otherwise, we provide upper bounds on the minimum number of agents required. For the remaining problems with heterogeneous agents, we further divide them into two subsets.For one of the subsets,we propose the concept of {\em transformer agent} to reduce the number of agents to be considered which is used to improve planning performance.We implemented a planner using our theoretical results and compared it with one of the best IPC CoDMAP planners in the centralized track.Results show that our planner provides significantly improved performance on IPC CoDMAP domains.

Distributed heuristic search is a well established technique for multi-agent planning. It has been shown that distributed heuristics may crucially improve the search guidance, but are costly in terms of communication and computation time. One solution is to compute a heuristic additively, in the sense that each agent can compute its part of the heuristic independently and obtain a complete heuristic estimate by summing up the individual parts. In this paper, we show that the recently published potential heuristic is a good candidate for such heuristic, moreover admissible. We also demonstrate how the multi-agent distributed A* search can be modified in order to benefit from such additive heuristic. The modified search equipped with a distributed potential heuristic outperforms the state of the art.

Collaborative privacy-preserving planning (CPPP) is a multi-agent planning task in which agents need to achieve a common set of goals without revealing certain private information. In many CPPP algorithms the individual agents reason about a projection of the multiagent problem onto a single-agent classical planning problem. For example, an agent can plan as if it controls the public actions of other agents, ignoring their unknown private preconditions and effects, and use the cost of this plan as a heuristic for the cost of the full, multi-agent plan. Using such a projection, however, ignores some dependencies between agents’ public actions. In particular, it does not contain dependencies between actions of other agents caused by their private facts. We propose a projection in which these private dependencies are maintained. The benefit of our dependency-preserving projection is demonstrated by using it to produce high level plans in a new privacy preserving planner that is able to solve more benchmark problems than any other state-of-the-art privacy preserving planner. This more informed projection does not explicitly share private information. In addition, we show that even if an adversary agent knows that an agent has some private objects of a given type (e.g., trucks), it cannot infer how many such private objects the agent controls. This introduces a novel strong form of privacy that is motivated by real-world requirements.