Human operators supervising multiple semi-autonomous drones must decide which alert to handle, when to revise search parameters, and how to respond to failures. We present an advising agent that recommends nonbinding actions from the current mission state. Because collecting many human demonstrations is costly, the agent perturbs a small set of real operator trajectories in simulation, checks that the resulting synthetic trajectories remain human-like, and trains a reward-estimation model to predict long-term mission outcomes. We instantiate the approach in a simulated search-and-rescue task. Experiments involved 37 participants; in the evaluation batch, advice increased the number of targets found by 20% and the area scanned by 41.4%, without a significant change in NASA Task Load Index scores. The paper thus contributes a human-conditioned simulation method that preserves operator-specific trajectory structure, a real-time adviser that ranks feasible actions by predicted long-term utility, and human-subject evidence that the resulting advice improves mission performance.

Multi-drone systems have become transformative technologies across various industries, offering innovative applications. However, despite significant advancements, their autonomous capabilities remain inherently limited. As a result, human operators are often essential for supervising and controlling these systems, creating what is referred to as a human-multi-drone team. In realistic settings, human operators must make real-time decisions while addressing a variety of signals, such as drone statuses and sensor readings, and adapting to dynamic conditions and uncertainty. This complexity may lead to suboptimal operations, potentially compromising the overall effectiveness of the team. In critical contexts like Search And Rescue (SAR) missions, such inefficiencies can have costly consequences. This work introduces an advising agent designed to enhance collaboration in human-multi-drone teams, with a specific focus on SAR scenarios. The advising agent is designed to assist the human operator by suggesting contextual actions worth taking. To that end, the agent employs a novel computation technique that relies on a small set of human demonstrations to generate varying realistic human-like trajectories. These trajectories are then generalized using machine learning for fast and accurate predictions of the long-term effects of different advice. Through human evaluations, we demonstrate that our approach delivers high-quality assistance, resulting in significantly improved performance compared to baseline conditions.

In recent years, agents have become capable of communicating seamlessly via natural language and navigating in environments that involve cooperation and competition, a fact that can introduce social dilemmas. Due to the interleaving of cooperation and competition, understanding agents' decision-making in such environments is challenging, and humans can benefit from obtaining explanations. However, such environments and scenarios have rarely been explored in the context of explainable AI. While some explanation methods for cooperative environments can be applied in mixed-motive setups, they do not address inter-agent competition, cheap-talk, or implicit communication by actions. In this work, we design explanation methods to address these issues. Then, we proceed to establish generality and demonstrate the applicability of the methods to three games with vastly different properties. Lastly, we demonstrate the effectiveness and usefulness of the methods for humans in two mixed-motive games. The first is a challenging 7-player game called no-press Diplomacy. The second is a 3-player game inspired by the prisoner's dilemma, featuring communication in natural language.

The use of virtual agents (bots) has become essential for providing online assistance to customers. However, even though a lot of effort has been dedicated to the research, development, and deployment of such virtual agents, customers are frequently frustrated with the interaction with the virtual agent and require a human instead. We suggest that a holistic approach, combining virtual agents and human operators working together, is the path to providing satisfactory service. However, implementing such a holistic customer service system will not, and cannot, be achieved using any single AI technology or branch. Rather, such a system will inevitably require the integration of multiple and diverse AI technologies, including natural language processing, multi-agent systems, machine learning, reinforcement learning, and behavioral cloning; in addition to integration with other disciplines such as psychology, business, sociology, economics, operation research, informatics, computer-human interaction, and more. As such, we believe this customer service application offers a rich domain for experimentation and application of multidisciplinary AI. In this paper, we introduce the holistic customer service application and discuss the key AI technologies and disciplines required for a successful AI solution for this setting. For each of these AI technologies, we outline the key scientific questions and research avenues stemming from this setting. We demonstrate that integrating technologies from different fields can lead to a cost-effective successful customer service center. The challenge is that there is a need for several communities, each with its own language and modeling techniques, different problem-solving methods, and different evaluation methodologies, all of which need to work together. Real cooperation will require the formation of joint methodologies and techniques that could improve the service to customers, but, more importantly, open new directions in cooperation of diverse communities toward solving joint difficult tasks.

Many applications where tasks should be assigned to agents can be modeled as matching in bipartite graphs. In this paper, we consider applications where tasks arrive dynamically and rejection of a task may have significant adverse effects on the requester, therefore performing the task with some delay is preferred over complete rejection. The performance time of a task depends on the task, the agent, and the assignment, and only its distribution is known in advance. The actual time is known only after the task performance when the agent is available for a new assignment. We consider such applications to be one of two arrival types. With the first type, the arrival distribution is known in advance, while there is no assumption about the arrival times and order with the second type. For the first type, we present an LP-based online algorithm with a competitive ratio of 0 . 5. For the second type, we show no online algorithm with a constant competitive ratio. We run extensive experiments to evaluate our algorithm in a real-world dataset, demonstrating the advantages of the LP approach.

Teleoperation of autonomous vehicles has been gaining a lot of attention recently and is expected to play an important role in helping autonomous vehicles handle difficult situations which they cannot handle on their own. In such cases, a remote driver located in a teleoperation center can remotely drive the vehicle until the situation is resolved. However, teledriving is a challenging task and requires many cognitive resources from the teleoperator. Our goal is to assist the remote driver in some complex situations by giving the driver appropriate advice. The advice is displayed on the driver’s screen to help her make the right decision. To this end, we introduce the TeleOperator Advisor (TOA), an adaptive agent that provides assisting advice to a remote driver. We evaluate the TOA in a simulation-based setting in two scenarios: overtaking a slow vehicle and passing through a traffic light. Results indicate that our advice helps to reduce the cognitive load of the remote driver and improve driving performance.

This paper considers the complex problem of a team of UAVs searching targets under uncertainty. The goal of the UAV team is to find all of the moving targets as quickly as possible before they arrive at their selected goal. The uncertainty considered is threefold: First, the UAVs do not know the targets' locations and destinations. Second, the sensing capabilities of the UAVs are not perfect. Third, the targets' movement model is unknown. We suggest a real-time algorithmic framework for the UAVs, combining entropy and stochastic-temporal belief, that aims at optimizing the probability of a quick and successful detection of all of the targets. We have empirically evaluated the algorithmic framework, and have shown its efficiency and significant performance improvement compared to other solutions. Furthermore, we have evaluated our framework using Peer Designed Agents (PDAs), which are computer agents that simulate targets and show that our algorithmic framework outperforms other solutions in this scenario.

Factors that impact (positively and negatively) the advisor-advisee relationship.

Road accidents are the leading causes of death among youths and young adults worldwide. Efficient traffic enforcement is an essential, yet complex, component in preventing road accidents. In this article, we present a novel model, an optimizing algorithm and a deployed system which together mitigate many of the computational and real-world challenges of traffic enforcement allocation in large road networks. Our approach allows for scalable, coupled and non-Markovian optimization of multiple police units and guarantees optimality. Our deployed system, which utilizes the proposed approach, is used by the Israeli traffic police and is shown to provide meaningful benefits compared to existing standard traffic police enforcement practices.

In Boolean games, each agent controls a set of Boolean variables and has a goal represented by a propositional formula. We study inference problems in Boolean games assuming the presence of a PRINCIPAL who has the ability to control the agents and impose taxation schemes. Previous work used taxation schemes to guide a game towards certain equilibria. We present algorithms that show how taxation schemes can also be used to infer agents' goals. We present experimental results to demonstrate the efficacy our algorithms. We also consider goal inference when only limited information is available in response to a query.

This paper considers a new, complex problem of UAV/UGV collaborative efforts to search and capture attackers under uncertainty. The goal of the defenders (UAV/UGV team) is to stop all attackers as quickly as possible, before they arrive at their selected goal. The uncertainty considered is twofold: the defenders do not know the attackers' location and destination, and there is also uncertainty in the defenders' sensing. We suggest a real-time algorithmic framework for the defenders, combining entropy and stochastic-temporal belief, that aims at optimizing the probability of a quick and successful capture of all of the attackers. We have empirically evaluated the algorithmic framework, and have shown its efficiency and significant performance improvement compared to other solutions.

Drones can assist in mitigating traffic accidents by deterring reckless drivers, leveraging their flexible mobility. In the real world, drones are fundamentally limited by their battery/fuel capacity and have to be replenished during long operations. In this paper, we propose a novel approach where police cruisers act as mobile replenishment providers in addition to their traffic enforcement duties. We propose a binary integer linear program for determining the optimal rendezvous cruiser-drone enforcement policy which guarantees that all drones are replenished on time and minimizes the likelihood of accidents. In an extensive empirical evaluation, we first show that human drivers are expected to react to traffic enforcement drones in a similar fashion to how they react to police cruisers using a first-of-its-kind human study in realistic simulated driving. Then, we show that our proposed approach significantly outperforms the common practice of constructing stationary replenishment installations using both synthetic and real world road networks.

Road accidents are the leading causes of death of youths and young adults worldwide. Efficient traffic enforcement has been conclusively shown to reduce high-risk driving behaviors and thus reduce accidents. Today, traffic police departments use simplified methods for their resource allocation (heuristics, accident hotspots, etc.). To address this potential shortcoming, in [23], we introduced a novel algorithmic solution, based on efficient optimization of the allocation of police resources, which relies on the prediction of accidents. This prediction can also be used for raising public awareness regarding road accidents. However, significant challenges arise when instantiating the proposed solution in real-world security settings. This paper reports on three main challenges: (1) Data-centric challenges; (2) Police-deployment challenges; and (3) Challenges in raising public awareness. We mainly focus on the data-centric challenge, highlighting the data collection and analysis, and provide a detailed description of how we tackled the challenge of predicting the likelihood of road accidents. We further outline the other two challenges, providing appropriate technical and methodological solutions including an open-access application for making our prediction model accessible to the public.

Area coverage is an important task for mobile robots, mainly due to its applicability in many domains, such as search and rescue. In this paper we study the problem of multi-robot coverage, in which the robots must obey a strong communication restriction: they should maintain connectivity between teammates throughout the coverage. We formally describe the Multi-Robot Connected Tree Coverage problem, and an algorithm for covering perfect N-ary trees while adhering to the communication requirement. The algorithm is analyzed theoretically, providing guarantees for coverage time by the notion of speedup factor. We enhance the theoretically-proven solution with a dripping heuristic algorithm, and show in extensive simulations that it significantly decreases the coverage time. The algorithm is then adjusted to general (not necessarily perfect) N-ary trees and additional experiments prove its efficiency. Furthermore, we show the use of our solution in a simulated officebuilding scenario. Finally, we deploy our algorithm on real robots in a real office building setting, showing efficient coverage time in practice.

Multi-robot systems have been successfully deployed in modern warehouses where they are required to move merchandise and equipment from one place to another. In such systems, the part of the human worker is often overlooked in the system’s design. In this paper we use a novel approach of utilizing automated advising agents for assisting a human worker to better manage her time and workload while supervising a team of multiple robots in a warehouse environment. We introduce the k-Myopic Advice Optimization (k-MYAO) Heuristic which prioritizes the human’s tasks in human-multi-robot team collaboration environments. This heuristic is an extension of the 1-MYAO heuristic that was already found to be successful in the Search And Rescue (SAR) task in previous work. We exemplify the k-MYAO ’s benefit in designing 2 automated advising agents AID1 and AID2 which are deployed in a simulated warehouse. Our proposed intelligent advising agents were evaluated through an extensive empirical study with 60 human operators, and showed significant improvements in the human-multi-robot team performance.

The number of multi-robot systems deployed in field applications has risen dramatically over the years. Nevertheless, supervising and operating multiple robots at once is a difficult task for a single operator to execute. In this paper we propose a novel approach for utilizing advising automated agents when assisting an operator to better manage a team of multiple robots in complex environments. We introduce the Myopic Advice Optimization (MYAO) Problem and exemplify its implementation using an agent for the Search And Rescue (SAR) task. Our intelligent advising agent was evaluated through extensive field trials, with 44 non-expert human operators and 10 low-cost mobile robots, in simulation and physical deployment, and showed a significant improvement in both team performance and the operator’s satisfaction.

Due to its high radiative stability and superior optoelectronic properties, such as wide direct band gap of similar to 3.4 eV and high exciton binding energy of similar to 60 meV, ZnO is considered for the fabrication of ultraviolet and visible light emitting and laser diodes, solar-blind photodetectors. Although proof-of-concept devices were already demonstrated, further progress in this area is slowed down by the difficulties of doping ZnO p-type. Here, we discuss problems associated with doping of ZnO, cover recent progress in this area, and discuss an alternative approach to increase p-type dopability via anion substitution, replacing oxygen with other group VI elements (S, Se, Te). We also propose that these anion-substituted alloys will be extremely promising for fabrication of photovoltaic devices, such as highly efficient thin film solar cells.

We annealed ZnSe/GaAs heterostructures in the oxygen atmosphere and investigated structural and optical properties of the forming films using X-ray diffraction and photoluminescence. While highly textured ZnO films were synthesized via low-temperature processing (~500 °C), high temperature processing (~800 °C) promoted reaction at the film/substrate interface and Zn loss from the film surface resulting in the polycrystalline ZnGa2O4 and ZnO2.

Wide bandgap II–VI materials and structures based on ZnCdMgSe alloys lattice matched to InP substrates have been investigated. Quantum well (QW) structures designed to emit throughout the entire visible spectral range were constructed by varying the QW layer thickness and/or composition. Light emitting diode (LED) structures were grown, fabricated and tested. Electroluminescence emission in the blue, green, yellow and red regions of the visible spectral range were obtained from lattice-matched or pseudomorphic structures that varied only in the composition or thickness of the QW layer. Lattice-matched distributed Bragg reflector (DBR) structures with reflectivity maxima in the red, green and blue were also grown and characterized. Since the LED and DBR structures are made from the same materials it should be possible to combine these into a single structure for the design of high performance LEDs and vertical cavity surface emitting lasers that operate in the entire visible range.