Coalition formation concerns autonomous agents that strategically interact to form self-organized coalitions. When agents lack initial sufficient information to evaluate their preferences before interacting with others, they learn them online through repeated feedback while iteratively forming coalitions. In this work, we introduce online learning in coalition formation from a non-cooperative perspective, studying the impact of collective data utilization where selfish agents aim to accelerate their learning by leveraging a shared data platform. Thus, the efficiency and dynamics of the learning process are affected by each agent's local feedbacks, motivating us to explore the tension between semi-bandit and bandit feedback, which differ in the granularity of utility information observed by each agent. Under our non-cooperative viewpoint, we evaluate the system by means of Nash stability, where no agent can improve her utility by unilaterally deviating. Our main result is a sample-efficient algorithm for selfish agents that aims to minimize their Nash regret under both semi-bandit and bandit feedback, implying approximately Nash stable outcomes. Under both feedback settings, our algorithm enjoys Nash regret and sample complexity bounds that are optimal up to logarithmic factors.

Coalition formation concerns partitioning agents into disjoint coalitions based on their preferences for one another. In online learning of coalition structures, agents’ true preferences may be initially unknown. Thus, coalitions are repeatedly formed based on preferences learned online from iterative feedback derived from interactions in those coalitions. This work introduces a new fairness-oriented approach to online learning in coalition formation, relying only on partial noisy feedback observed after agents interact. We analyze the system in terms of envy-based fairness notions. Envy-freeness is a popular criterion, where no agent prefers another agent’s coalition over her own. While trivial envy-free solutions exist for unconstrained number of coalitions and coalition sizes, constraints may make envy-free partitions unattainable. We thus present a new envy-freeness-based metric into hedonic games: minimax envy partitions, which minimize the maximum envy experienced by any agent. We devise an algorithm designed to minimize maximum envy, proven to attain sublinear envy regret.

Coalition formation involves self-organized coalitions generated through strategic interactions of autonomous selfish agents. In online learning of coalition structures, agents' preferences toward each other are initially unknown before agents interact. Coalitions are formed iteratively based on preferences that agents learn online from repeated feedback resulting from their interactions. In this paper, we introduce online learning in coalition formation through the lens of distributed decision-making, where self-interested agents operate without global coordination or information sharing, and learn only from their own experience. Under our selfish perspective, each agent seeks to maximize her own utility. Thus, we analyze the system in terms of Nash stability, where no agent can improve her utility by unilaterally deviating. We devise a sample-efficient decentralized algorithm for selfish agents that minimize their Nash regret, yielding approximately Nash stable solutions. In our algorithm, each agent uses only one utility feedback per round to update her strategy, but our algorithm still has Nash regret and sample complexity bounds that are optimal up to logarithmic factors.

We study the friendship-based online coalition formation problem, in which agents that appear one at a time should be partitioned into coalitions, and an agent’s utility for a coalition is the number of her neighbors (i.e., friends) within the coalition. Unlike prior work, agents’ friendships may be uncertain. We analyze the desirability of the resulting partition in the common term of optimality, aiming to maximize the social welfare. We design an online algorithm termed Maximum Predicted Coalitional Friends (MPCF), which is enhanced with predictions of each agent’s number of friends within any possible coalition. For common classes of random graphs, we prove that MPCF is optimal, and, for certain graphs, provides the same guarantee as the best known competitive algorithm for settings without uncertainty.

Recent advances in technology have large teams of robots with limited computation skills work together in order to achieve a common goal. Their personal actions need to contribute to the joint effort, however, they also must assure that they do not harm the efforts of the other members of the team, e.g., as a result of collisions. We focus on the distributed target coverage problem, in which the team must cooperate in order to maximize utility from sensed targets, while avoiding collisions with other agents. State of the art solutions focus on the distributed optimization of the coverage task in the team level, while neglecting to consider collision avoidance, which could have far reaching consequences on the overall performance. Therefore, we propose CAMS: a collision-avoiding version of the Max-sum algorithm, for solving problems including mobile sensors. In CAMS, a factor-graph that includes two types of constraints (represented by function-nodes) is being iteratively generated and solved. The first type represents the task-related requirements, and the second represents collision avoidance constraints. We prove that consistent beliefs are sent by target representing function-nodes during the run of the algorithm, and identify factor-graph structures on which CAMS is guaranteed to converge to an optimal (collision-free) solution. We present an experimental evaluation in extensive simulations, showing that CAMS produces high quality collision-free coverage also in large and complex scenarios. We further present evidence from experiments in a real multi-robot system that CAMS outperforms the state of the art in terms of convergence time.

Hedonic games model cooperative games where agents desire to form coalitions, and only care about the composition of the coalitions of which they are members. Focusing on various classes of dichotomous hedonic games, where each agent either approves or disapproves a given coalition, we propose the random extension, where players have an independent participation probability. We initiate the research on the computational complexity of computing the probability that coalitions and partitions are optimal or stable. While some cases admit efficient algorithms (e.g., agents approve only few coalitions), they become computationally hard (#P-hard) in their complementary scenario. We then investigate the distribution of coalitions in perfect partitions and their performance in majority games, where an agent approves coalitions in which the agent is friends with the majority of its members. When friendships independently form with a constant probability, we prove that the number of coalitions of size 3 converges in distribution to a Poisson random variable.

This paper studies the problem of Competitive Ant Coverage, in which two ant-like robots with very limited capabilities in terms of sensing range, computational power, and knowledge of the world compete in an area coverage task. We examine two variants of the problem that differ in the robot's objective: either being the First to Cover a Cell (FCC), or being the Last to Cover a Cell (LCC). Each robot's goal is to acquire (by visiting first or last, respectively) more cells than the opposing robot, and by that win the game. We examine the problem both theoretically and empirically, and show that the main strategy for dominance revolves around the ability to pursue: in LCC, we wish to pursue the opposing robot, whereas in FCC, we wish to create a scenario wherein the opposing robot pursues us. We find that this ability relies more heavily on knowledge of the opponent's strategy than on the robot's sensing capabilities. Moreover, given the robot's limited capabilities, we find that this knowledge-gap cannot be easily mitigated by learning.

Motivated by the use of robots for pest control in agriculture, this work introduces the Multi-Robot Dynamic Swarm Disablement problem, in which a team of robots is required to disable a swarm of agents (for example, locust agents) passing through an area while minimizing the cumulative time of the swarm members (equivalent to the cumulative damage they cause) in the area. Showing that the problem is hard even in naive settings, we turn to examine algorithms seeking to optimize the robots' performance against the swarm by exploiting the known movement pattern of the swarm agents. Motivated by the poor performance when a weak group of robots attempts to catch a large swarm of agents, whether it is a significant numerical minority or poor speed gaps, we suggest the use of blocking lines: the robots form lines that block the agents along their movement in the environment. We show by both theoretical analysis and rigorous empirical evaluation in different settings that these algorithms outperform common task-assignment-based algorithms, especially for limited robots versus a large swarm.

A network of robots can be viewed as a signal graph, describing the underlying network topology with naturally distributed architectures, whose nodes are assigned to data values associated with each robot. Graph neural networks (GNNs) learn representations from signal graphs, thus making them well-suited candidates for learning distributed controllers. Oftentimes, existing GNN architectures assume ideal scenarios, while ignoring the possibility that this distributed graph may change along time due to link failures or topology variations, which can be found in dynamic settings. A mismatch between the graphs on which GNNs were trained and the ones on which they are tested is thus formed. Utilizing online learning, GNNs can be retrained at testing time, overcoming this issue. However, most online algorithms are centralized and work on convex problems (which GNNs scarcely lead to). This paper introduces novel architectures which solve the convexity restriction and can be easily updated in a distributed, online manner. Finally, we provide experiments, showing how these models can be applied to optimizing formation control in a swarm of flocking robots.

Unmanned Aircraft System Traffic Management (UTM) becomes a highly relevant complex challenge, as the UAV activity is rapidly growing bringing more amateur and professional drones to the urban skies. The main concern of managing such a system is safely navigating and controlling hundreds or thousands of drones simultaneously, flying in a crowded dense environments. This paper introduces an innovative approach of hybrid path planning, which tries to make the best out of the commonly used centralized and decentralized planning approaches. The Hybrid Path Planner (HPP) defines two configuration spaces: the Local Zone, which represents the crowded city zone with many obstacles and constrains, and the Global Zone, which represents the outer suburban zone, mostly open space with predefined flight corridors. The HPP server communicates with each UAV, assigning it a close-to-optimal path in the global zone, while leaving the relatively heavy-duty local zone path planning task to be performed by the UAV, mostly using stochastic methods like RRT*. This approach reduces the complex path panning task of the centralized server to a simpler task of calculating only the entry and exit points to and from the global zone. This robust approach supports handling a high number of UAVs, while keeping close to optimal performance.

Black-Box Optimization (BBO) methods can find optimal policies for systems that interact with complex environments with no analytical representation. As such, they are of interest in many Artificial Intelligence (AI) domains. Yet classical BBO methods fall short in high-dimensional non-convex problems. They are thus often overlooked in real-world AI tasks. Here we present a BBO method, termed Explicit Gradient Learning (EGL), that is designed to optimize high-dimensional ill-behaved functions. We derive EGL by finding weak spots in methods that fit the objective function with a parametric Neural Network (NN) model and obtain the gradient signal by calculating the parametric gradient. Instead of fitting the function, EGL trains a NN to estimate the objective gradient directly. We prove the convergence of EGL to a stationary point and its robustness in the optimization of integrable functions. We evaluate EGL and achieve state-of-the-art results in two challenging problems: (1) the COCO test suite against an assortment of standard BBO methods; and (2) in a high-dimensional non-convex image generation task.

This paper presents the multi-robot containment and disablement (CAD) problem. In this problem, a team of (ground or aerial) robots are engaged in a cooperative task of swarm containment and disablement (for example, locust swarm). Each team member is equipped with a tool that can both detect and disable the swarm individuals. The swarm is active in a given physical location, and the goal of the robots is twofold: to contain the swarm members such that the individuals will be prevented from expanding further beyond this area (this is referred to as perfect enclosure), and to fully disable the locust by reducing the size of the contained area (while preserving the perfect enclosure). We determine the minimal number of robots necessary to ensure perfect enclosure, and a placement of the robots about the contained area such that they will be able to guarantee perfect enclosure, as well as a distributed area reduction protocol maintaining perfect enclosure. We then suggest algorithms for handling the case in which there are not enough robots to guarantee perfect enclosure, and describe their performance based on rigorous experiments in the TeamBots simulator.

This paper introduces the competitive coverage problem, a new variant of the robotic coverage problem in which a robot R competes with another robot O in order to be the first to cover an area. In the variant discussed in this paper, the asymmetric competitive coverage, O is unaware of the existence of R, which attempts to take that fact into consideration in order to succeed in being the first to cover as many parts of the environment as possible. We consider different information models of R that define how much it knows about the location of O and its planned coverage path. We present an optimal algorithm for R in the full-information case, and show that unless R has information about O's initial location, it is as if it has no information at all. Lastly, we describe a correlation between the time it takes R to reach O's initial location and the coverage paths quality, and present a heuristic algorithm for the case in which R has information only about O's initial location, showing its superiority compared to other coverage algorithms in rigorous simulation experiments.

There is a growing need for coverage of large maritime areas, mainly in the exclusive economic zone (EEZ). Due to the difficulty of accessing such large areas, the use of satellite based sensors is the most efficient and cost-effective way to perform this task. Vessel behavior prediction is a necessary ability for detection of moving vessels with satellite imagery. In this paper we present an algorithm for selection of the best satellite observation window to detect a moving object. First, we describe a model for vessel behavior prediction and compare its performance to two base models. We use real marine traffic data (AIS) to compare their ability to predict vessel behavior in a time frame of between 1–24 hours. Then, we present a KINGFISHER, maritime intelligence system which uses our algorithm to track suspected vessels with satellite sensor. We also present the results of the algorithm in operational scenarios of the KINGFISHER.

Plan recognition is the task of inferring the plan of an agent, based on an incomplete sequence of its observed actions. Previous formulations of plan recognition commit early to discretizations of the environment and the observed agent's actions. This leads to reduced recognition accuracy. To address this, we first provide a formalization of recognition problems which admits continuous environments, as well as discrete domains. We then show that through mirroring---generalizing plan-recognition by planning---we can apply continuous-world motion planners in plan recognition. We provide formal arguments for the usefulness of mirroring, and empirically evaluate mirroring in more than a thousand recognition problems in three continuous domains and six classical planning domains.

Leader-Follower is a hierarchical form of multi-robot formation control, where each robot aims to maintain specific predefined angle and distance from one or more robots in the team (referred to as its local leaders), while a single robot is selected to lead the entire formation to a desired destination. When the robots are given a specific formation to maintain, their goal is usually to minimize the deviation from this desired formation (maximizing the accuracy) during their journey. Previous work has considered optimality in an uncertain environment only in centralized setting (or using perfect, or almost perfect communication). In this paper we examine the problem of optimal multi-robot formation control in a distributed setting, while accounting for two challenges: sensory uncertainty and absence of communication. Specifically, we present an algorithm that allows each individual robot to estimate the overall formation accuracy of the other robots in their field of view via a tree reconstruction algorithm. The algorithm is used to select the most accurate local leader, or to generate virtual local leader via a weighted average of all visible robots. We provide both theoretical analysis and an extensive empirical evaluation (in ROS/Gazebo simulated environment) showing the effectiveness of the two approaches.

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.

The presence of robots in areas containing threats is becoming more prevalent, due to their ability to perform missions accurately, efficiently, and with little risk to humans. Having the robots handle adversarial forces in missions such as search and rescue, intelligence gathering, border protection and humanitarian assistance, raises many new, exciting research challenges. This paper describes recent research achievements in areas related to robotic mission planning in adversarial environments, including multi-robot patrolling, robotic coverage, multi-robot formation, and navigation, and suggests possible future research directions.

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 adversarial patrolling is a well studied problem, investigating how defenders can optimally use all given resources for maximizing the probability of detecting penetrations, that are controlled by an adversary. It is commonly assumed that the adversary in this problem is rational, thus uses the knowledge it has on the patrolling robots (namely, the number of robots, their location, characteristics and strategy) to optimize its own chances to penetrate successfully. In this paper we present a novel defending approach which manipulates the adversarial (possibly partial) knowledge on the patrolling robots, so that it will believe the robots have more power than they actually have. We describe two different ways of deceiving the adversary: Window Deception, in which it is assumed that the adversary has partial observability of the perimeter, and Scarecrow Deception, in which some of the patrolling robots only appear as real robots, though they have no ability to actually detect the adversary. We analyze the limitations of both models, and suggest a random-based approach for optimally deceiving the adversary that considers both the resources of the defenders, and the adversarial knowledge.

Asimov's three laws of robotics, which were shaped in the literary work of Isaac Asimov (1920-1992) and others, define a crucial code of behavior that fictional autonomous robots must obey as a condition for their integration into human society. While, general implementation of these laws in robots is widely considered impractical, limited-scope versions have been demonstrated and have proven useful in spurring scientific debate on aspects of safety and autonomy in robots and intelligent systems. In this work, we use Asimov's laws to examine these notions in molecular robots fabricated from DNA origami. We successfully programmed these robots to obey, by means of interactions between individual robots in a large population, an appropriately scoped variant of Asimov's laws, and even emulate the key scenario from Asimov's story "Runaround," in which a fictional robot gets into trouble despite adhering to the laws. Our findings show that abstract, complex notions can be encoded and implemented at the molecular scale, when we understand robots on this scale on the basis of their interactions.

This work deals with the problem of strategic path planning while avoiding detection by a mobile adversary. In this problem, an evading agent is placed on a graph, where one or more nodes are defined as safehouses. The agent's goal is to find a path from its current location to a safehouse, while minimizing the probability of meeting a mobile adversarial agent at a node along its path (i.e., being captured). We examine several models of this problem, where each one has different assumptions on what the agents know about their opponent, all using a framework for computing node utility. We use several risk attitudes for computing the utility values, whose impact on the actual performance of the path planning algorithms is highlighted by an empirical analysis. Furthermore, we allow the agents to use information gained along their movement, in order to efficiently update their motion strategies on-the-fly. Analytic and empiric analysis show that on-the-fly updates increase the probability that our agent reaches its destination safely.

This work discusses the problem of adversarial coverage, in which one or more robots are required to visit every point of a given area, which contains threats that might stop the robots. The objective of the robots is to cover the target area as quickly as possible, while maximizing the percentage of covered area before they are stopped. This problem has many real-world applications, from performing coverage missions in hazardous fields such as nuclear power plants, to surveillance of enemy forces in the battlefield and field demining. Previous studies of the problem dealt with single-robot coverage. Using a multi-robot team for the coverage has clear advantages in terms of both coverage time and robustness: even if one robot is totally damaged, others may take over its coverage subtask. Hence, in this paper we describe a multi-robot coverage algorithm for adversarial environments that tries to maximize the percentage of covered area before the team is stopped, while minimizing the coverage time. We analytically show that the algorithm is robust, in that as long as a single robot is able to move, the coverage will be completed. We also establish theoretical bounds on the minimum covered area guaranteed by the algorithm and on the coverage time. Lastly, we evaluate the effectiveness of the algorithm in an extensive set of environments and settings.

Multi robot formation is a canonical problem in robotic research. The problem has been examined in neutral environments, where the robots' goal is usually to maintain the formation despite changes in the environment. The problem of multi robot formation has been motivated by natural phenomena such as schools of fish or flocks of birds. While in the natural phenomena the team behavior is responsive to threats, in robotics research of team formation, adversarial presence has been ignored. In this paper we present the problem of adversarial formation, in which a team of robots travels in a connected formation through an adversarial environment that includes threats that may harm the robots. The robots' goal is, therefore, to maximize their chance of traveling through the environment unharmed, where the formation may be used as a mean to achieve this goal. We formally define the problem, present a quantitative measure for evaluating the survivability of the team, and suggest possible solutions to a variant of the problem under certain threat characteristics, optimizing different team survivability criteria. Finally, we discuss the challenges raised by transitioning the discrete representation to a continuous environment in simulation.

In the robotic coverage problem, a robot is required to visit every point of a given area using the shortest possible path. In a recently introduced version of the problem, adversarial coverage, the covering robot operates in an environment that contains threats that might stop it. Previous studies of this problem dealt with finding optimal strategies for the coverage, that minimize both the coverage time and the probability that the robot will be stopped before completing the coverage. However, these studies assumed that a map of the environment, which includes the specific locations of the threats, is given to the robot in advance. In this paper, we deal with the online version of the problem, in which the covering robot has no a-priori knowledge of the environment, and thus has to use real-time sensor measurements in order to detect the threats. We employ a frontier-based coverage strategy that determines the best frontier to be visited by taking into account both the cost of moving to the frontier and the safety of the region that is reachable from it. We also examine the effect of the robot's sensing capabilities on the expected coverage percentage. Finally, we compare the performance of the online algorithm to its offline counterparts under various environmental conditions.

Peer Designed Agents (PDAs), computer agents developed by non-experts, is an emerging technology, widely advocated in recent literature for the purpose of replacing people in simulations and investigating human behavior. Its main premise is that strategies programmed into these agents reliably reflect, to some extent, the behavior used by their programmers in real life. In this paper we show that PDA development has an important side effect that has not been addressed to date -- the process that merely attempts to capture one's strategy is also likely to affect the developer's strategy. The phenomenon is demonstrated experimentally, using several performance measures. This result has many implications concerning the appropriate design of PDA-based simulations, and the validity of using PDAs for studying individual decision making. Furthermore, we obtain that PDA development actually improved the developer's strategy according to all performance measures. Therefore, PDA development can be suggested as a means for improving people's problem solving skills.

Past research has investigated a number of methods for coordinating teams of agents, but with the growing number of sources of agents, it is likely that agents will encounter teammates that do not share their coordination methods. Therefore, it is desirable for agents to adapt to these teammates, forming an effective ad hoc team. Past ad hoc teamwork research has focused on cases where the agents do not directly communicate. However when teammates do communicate, it can provide a valuable channel for coordination. Therefore, this paper tackles the problem of communication in ad hoc teams, introducing a minimal version of the multiagent, multiarmed bandit problem with limited communication between the agents. The theoretical results in this paper prove that this problem setting can be solved in polynomial time when the agent knows the set of possible teammates. Furthermore, the empirical results show that an agent can cooperate with a variety of teammates following unknown behaviors even when its models of these teammates are imperfect.

Coverage is a fundamental problem in robotics, where one or more robots are required to visit each point in a target area at least once. While most previous work concentrated on finding a solution that completes the coverage as quickly as possible, in this paper we consider a new version of the problem: adversarial coverage. Here, the robot operates in an environment that contains threats that might stop the robot. We introduce the problem of finding the safest adversarial coverage path, and present different optimization criteria for the evaluation of these paths. We show that finding an optimal solution to the safest coverage problem is NP-Complete. We therefore suggest two heuristic algorithms: STAC, a spanning-tree based coverage algorithm, and GSAC, which follows a greedy approach. These algorithms produce close to optimal solutions in polynomial time. We establish theoretical bounds on the total risk involved in the coverage paths created by these algorithms and on their lengths. Lastly, we compare the effectiveness of these two algorithms in various types of environments and settings.

This paper discusses the problem of generating efficient coverage paths for a mobile robot in an adversarial environment, where threats exist that might stop the robot. First, we formally define the problem of adversarial coverage, and present optimization criteria used for evaluation of coverage algorithms in adversarial environments. We then present a coverage area planning algorithm based on a map of the probable threats. The algorithm tries to minimize the total risk involved in covering the target area while taking into account coverage time constrains. The algorithm is based on incrementally extending the coverage path to the nearest safe cells while allowing the robot to repeat its steps. By allowing the robot to visit each cell in the target area more than once, the accumulated risk can be reduced at the expense of extending the coverage time. We show the effectiveness of this algorithm in extensive experiments.

Multi-robot patrol is a fundamental application of multi-robot systems. While much theoretical work exists providing an understanding of the optimal patrol strategy for teams of coordinated homogeneous robots, little work exists on building and evaluating the performance of such systems for real. In this paper, we evaluate the performance of multirobot patrol in a practical outdoor distributed robotic system, and evaluate the effect of different coordination schemes on the performance of the robotic team. The multi-robot patrol algorithms evaluated vary in the level of robot coordination: no coordination, loose coordination, and tight coordination. In addition, we evaluate versions of these algorithms that distribute state information-either individual state, or entire team state (global-view state). Our experiments show that while tight coordination is theoretically optimal, it is not practical in practice. Instead, uncoordinated patrol performs best in terms of average waypoint visitation frequency, though loosely coordinated patrol that shares only individual state performed best in terms of worst-case frequency. Both are significantly better than a loosely coordinated algorithm based on sharing global-view state. We respond to this discrepancy between theory and practice, caused primarily by robot heterogeneity, by extending the theory to account for such heterogeneity, and find that the new theory accounts for the empirical results.

The problem of multiagent patrol has gained considerable attention during the past decade, with the immediate applicability of the problem being one of its main sources of interest. In this paper we concentrate on frequency-based patrol, in which the agents' goal is to optimize a frequency criterion, namely, minimizing the time between visits to a set of interest points. We consider multiagent patrol in environments with complex environmental conditions that affect the cost of traveling from one point to another. For example, in marine environments, the travel time of ships depends on parameters such as wind, water currents, and waves. We demonstrate that in such environments there is a need to consider a new multiagent patrol strategy which divides the given area into parts in which more than one agent is active, for improving frequency. We show that in general graphs this problem is intractable, therefore we focus on simplified (yet realistic) cyclic graphs with possible inner edges. Although the problem remains generally intractable in such graphs, we provide a heuristic algorithm that is shown to significantly improve point-visit frequency compared to other patrol strategies. For evaluation of our work we used a custom developed ship simulator that realistically models ship movement constraints such as engine force and drag and reaction of the ship to environmental changes.

An ad hoc team setting is one in which teammates must work together to obtain a common goal, but without any prior agreement regarding how to work together. In this abstract we present a role-based approach for ad hoc teamwork, in which each teammate is inferred to be following a specialized role that accomplishes a specific task or exhibits a particular behavior. In such cases, the role an ad hoc agent should select depends both on its own capabilities and on the roles currently selected by the other team members. We present methods for evaluating the influence of the ad hoc agent's role selection on the team's utility and we examine empirically how to select the best suited method for role assignment in a complex environment. Finally, we show that an appropriate assignment method can be determined from a limited amount of data and used successfully in similar new tasks that the team has not encountered before.

The interaction of people with autonomous agents has become increasingly prevalent. Some of these settings include security domains, where people can be characterized as uncooperative, hostile, manipulative, and tending to take advantage of the situation for their own needs. This makes it challenging to design proficient agents to interact with people in such environments. Evaluating the success of the agents automatically before evaluating them with people or deploying them could alleviate this challenge and result in better designed agents. In this paper we show how Peer Designed Agents (PDAs) -- computer agents developed by human subjects -- can be used as a method for evaluating autonomous agents in security domains. Such evaluation can reduce the effort and costs involved in evaluating autonomous agents interacting with people to validate their efficacy. Our experiments included more than 70 human subjects and 40 PDAs developed by students. The study provides empirical support that PDAs can be used to compare the proficiency of autonomous agents when matched with people in security domains.

This paper considers the problem of multi-robot patrol around a closed area with the existence of an adversary attempting to penetrate into the area. In case the adversary knows the patrol scheme of the robots and the robots use a deterministic patrol algorithm, then in many cases it is possible to penetrate with probability 1. Therefore this paper considers a non-deterministic patrol scheme for the robots, such that their movement is characterized by a probability p. This patrol scheme allows reducing the probability of penetration, even under an assumption of a strong opponent that knows the patrol scheme. We offer an optimal polynomial-time algorithm for finding the probability p such that the minimal probability of penetration detection throughout the perimeter is maximized. We describe three robotic motion models, defined by the movement characteristics of the robots. The algorithm described herein is suitable for all three models.

This paper discusses the problem of generating patrol paths for a team of mobile robots inside a designated target area. Patrolling requires an area to be visited repeatedly by the robot(s) in order to monitor its current state. First, we present frequency optimization criteria used for evaluation of patrol algorithms. We then present a patrol algorithm that guarantees maximal uniform frequency, i.e., each point in the target area is covered at the same optimal frequency. This solution is based on finding a circular path that visits all points in the area, while taking into account terrain directionality and velocity constraints. Robots are positioned uniformly along this path, using a second algorithm. Moreover, the solution is guaranteed to be robust in the sense that uniform frequency of the patrol is achieved as long as at least one robot works properly.

This paper discusses the problem of building efficient coverage paths for a team of robots. An efficient multirobot coverage algorithm should result in a coverage path for every robot, such that the union of all paths generates a full coverage of the terrain and the total coverage time is minimized. A method, underlying several coverage algorithms, suggests the use of spanning trees as base for creating coverage paths. Current studies assume that the spanning tree is given, and try to make the most out of the given configuration. However, overall performance of the coverage is heavily dependent on the given spanning tree. This paper tackles the open challenge of constructing a coverage spanning tree that minimizes the time to complete coverage. We argue that the choice of the initial spanning tree has far reaching consequences concerning the coverage time, and if the tree is constructed appropriately, it could considerably reduce the coverage time of the terrain. Therefore the problem studied here is finding spanning trees that would decrease the coverage time of the terrain when used as base for multi-robot coverage algorithms. The main contributions of this paper are twofold. First, it provides initial sound discussion and results concerning the construction of the tree as a crucial base for any efficient coverage algorithm. Second, it describes a polynomial-time tree construction algorithm that, as shown in extensive simulations, dramatically improves the coverage time even when used as a basis for a simple, inefficient, coverage algorithm

This paper studies fault-tolerant algorithms for the problem of gathering N autonomous mobile robots. A gathering algorithm, executed independently by each robot, must ensure that all robots are gathered at one point within finite time. In a failure-prone system, a gathering algorithm is required to successfully gather the nonfaulty robots, independently of the behavior of the faulty ones. Both crash and Byzantine faults are considered. It is first observed that most existing algorithms fail to operate correctly in a setting allowing crash failures. Subsequently, an algorithm tolerant against one crash-faulty robot in a system of three or more robots is presented. It is then observed that all known algorithms fail to operate correctly in a system prone to Byzantine faults, even in the presence of a single fault. Moreover, it is shown that in an asynchronous environment it is impossible to perform a successful gathering in a 3-robot system, even if at most one of them might fail in a Byzantine manner. Thus, the problem is studied in a fully synchronous system. An algorithm is provided in this model for gathering $N \geq 3$ robots with at most a single faulty robot, and a more general gathering algorithm is given in an N-robot system with up to f faults, where $N \geq 3f+1$.