Michael A Goodrich

• Ph.D.
• Chair at Brigham Young University

There are powerful tools for modelling swarms that have strong spatial structures like flocks of birds, schools of fish and formations of drones, but relatively little work on developing formalisms for other swarm structures like hub-based colonies doing foraging, maintaining a nest or selecting a new nest site. We present a method for finding low-...

Grammatical evolution can be used to learn bio-inspired solutions to many distributed multiagent tasks, but the programs learned by the agents often need to be resilient to perturbations in the world. Biological inspiration from bacteria suggests that ongoing evolution can enable resilience, but traditional grammatical evolution algorithms learn to...

A hub-based colony consists of multiple agents who share a common nest site called the hub. Agents perform tasks away from the hub like foraging for food or gathering information about future nest sites. Modeling hub-based colonies is challenging because the size of the collective state space grows rapidly as the number of agents grows. This paper...

As AI integrates into human societies, its ability to engage in collective action is increasingly important. Human social systems have large and flexible strategy spaces, conflicting interests, power asymmetry, and interdependence among members, which together make it challenging for agents to learn collective action. In this paper, we explore the...

Temporal logic can be used to formally specify autonomous agent goals, but synthesizing planners that guarantee goal satisfaction can be computationally prohibitive. This paper shows how to turn goals specified using a subset of finite trace Linear Temporal Logic (\(LTL_{f}\)) into a behavior tree (BT) that guarantees that successful traces satisfy...

Proficiency self-assessment (PSA), which is the ability to estimate how likely one can complete a task, is a beneficial property for autonomous robots. Prior work developed the assumption-alignment tracking (AAT) method for PSA, which estimates the probability that a robot will successfully complete a task. This paper refers to the prediction mad...

While the design of autonomous robots often emphasizes developing proficient robots, another important attribute of autonomous robot systems is their ability to evaluate their own proficiency. A robot should be able to assess how well it can perform a task before, during, and after it has attempted the task. How can autonomous robots be designed to...

Goal-based agents need to be resilient to perturbations in the world. Existing resilience definitions emphasize maintenance-type goals and, consequently, describe how well systems can recover and return to a desirable operating state after a perturbation. An alternative formulation of resilience is required for achievement-type goals that empha...

An agent’s autonomy can be viewed as the set of physically and computationally grounded algorithms that can be performed by the agent. This view leads to two useful notions related to autonomy: behavior potential and success potential, which can be used to measure of how well an agent fulfills its potential, call fulfillment. Fulfillment and succes...

Autonomous systems, although capable of performing complicated tasks much faster than humans, are brittle due to uncertainties encountered in most real-time applications. People supervising these systems often rely on information relayed by the system to make any decisions, which places a burden on the system to self-assess its proficiency and comm...

Swarm robotics is an emerging field that is expected to provide robust solutions to spatially distributed problems. Human operators will often be required to guide a swarm in the fulfillment of a mission. Occasionally, large tasks may require multiple spatial swarms to cooperate in their completion. We hypothesize that when latency and bandwidth si...

Artificial intelligence (AI) agents will need to interact with both other AI agents and humans. Creating models of associates help to predict the modeled agents' actions, plans, and intentions. This work introduces algorithms that predict actions , plans and intentions in repeated play games, with providing an exploration of algorithms. We form a g...

Artificial intelligence (AI) agents will need to interact with both other AI agents and humans. Creating models of associates help to predict the modeled agents' actions, plans, and intentions. This work introduces algorithms that predict actions, plans and intentions in repeated play games, with providing an exploration of algorithms. We form a ge...

Algorithms used in networking, operation research and optimization can be created using bio-inspired swarm behaviors, but it is difficult to mimic swarm behaviors that generalize through diverse environments. State-machine-based artificial collective behaviors evolved by standard Grammatical Evolution (GE) provide promise for general swarm behavior...

Swarm robotic systems are gaining in interest with the prospect of their use for various applications, including monitoring, tracking, infrastructure support, and protection. Prior human-swarm system research investigated transparency for these systems, but assumed perfect communication scenarios. Real-world human-swarm systems will not have perfec...

Animals such as bees, ants, birds, fish, and others are able to perform complex coordinated tasks like foraging, nest-selection, flocking and escaping predators efficiently without centralized control or coordination. Conventionally, mimicking these behaviors with robots requires researchers to study actual behaviors, derive mathematical models, an...

A key element of system transparency is allowing humans to calibrate their trust in a system, given the implicit inherent uncertainty, emergent behaviors, etc. As robotic swarms progress towards real-world missions, such transparency becomes increasingly necessary in order to reduce the disuse, misuse and errors humans make when influencing and dir...

Since Alan Turing envisioned Artificial Intelligence (AI) [1], a major driving force behind technical progress has been competition with human cognition. Historical milestones have been frequently associated with computers matching or outperforming humans in difficult cognitive tasks (e.g. face recognition [2], personality classification [3], drivi...

Robot swarms modeled after hub-based colonies, such as ants and bees, potentially offer fault-tolerant capabilities at very favorable cost margins. However, relatively little is known about how to harness the potential of these swarms through command-and-control systems. In this paper, we study how to merge operator input with the underlying swarm...

Since Alan Turing envisioned Artificial Intelligence (AI) [1], a major driving force behind technical progress has been competition with human cognition. Historical milestones have been frequently associated with computers matching or outperforming humans in difficult cognitive tasks (e.g. face recognition [2], personality classification [3], drivi...

An important part of expressing human intent is identifying acceptable tradeoffs among competing performance objectives. We present and evaluate a set of graphical user interfaces (GUIs), that are designed to allow a human to express intent by expressing desirable tradeoffs. The GUIs require an algorithm that identifies the set of Pareto optimal so...

There is a growing need to develop effective interaction methods that enable a single operator to manage a team of multiple robots. This paper presents a novel approach that involves treating the team as a moldable volume, in which deformations of the volume correspond to changes in team shape. The team possesses a level of autonomy that allows the...

n a problem where a human uses supervisory control to manage robot path-planning, there are times when human does the path planning, and if satisfied commits those paths to be executed by the robot, and the robot executes that plan. In planning a path, the robot often uses an optimization algorithm that maximizes or minimizes an objective. When a hu...

An important problem in human-robot interaction is for a human to be able to tell the robot go to a particular location with instructions on how to get there or what to avoid on the way. This paper provides a solution to problems where the human wants the robot not only to optimize some objective but also to honor “soft” or “hard” topological const...

The search for invariants is a fundamental aim of scientific endeavors. These invariants, such as Newton’s laws of motion, allow us to model and predict the behavior of systems across many different problems. In the nascent field of Human-Swarm Interaction (HSI), a systematic identification of fundamental invariants is still lacking. Discovering an...

This paper examines the dynamics of particle swarm optimization (PSO) by modeling PSO as a feedback cascade system and then applying input-to-state stability analysis. Using a feedback cascade system model we can include the effects of the global-best and personal-best values more directly in the model of the dynamics. Thus in contrast to previous...

Many robotic tasks require solutions that maximize multiple performance objectives. For example, in path-planning, these objectives often include finding short paths that avoid risk and maximize the information obtained by the robot. Although there exist many algorithms for multi-objective optimization, few of these algorithms apply directly to rob...

Increased use of autonomy also increases the need for humans to interact with or manage autonomy. We propose a new variation of sliding autonomy useful for planning problems over a spatial region. With this approach, the user can influence the behavior of the autonomous system via spatial constraints and temporal constraints. We present a set of us...

This chapter presents a synthesis of past work on Unmanned Aerial System (UAS) autonomy and user interface design and uses this synthesis to motivate emerging themes in near-term and far-term UAS developments. Issues include user interface design, UAS autonomy, UAS teaming, operator workload, and payload management. This chapter is a fusion of wor...

The role of humans in aviation and other domains continues to shift from manual control to automation monitoring. Studies have found that humans are often poorly suited for monitoring roles, and workload can easily spike in offnominal situations. Current workload measurement tools, like NASA TLX, use human operators to assess their own workload aft...

Human interaction with bio-inspired collectives provides an interesting setting for studying shared control. A human will often have knowledge of global objectives and high-level plans, but the collective will often have more detailed lower-level knowledge about the particulars of the situation at hand. Thus it is important to understand how contro...

One way for a human and a robot to collaborate on a search task is for the human to specify constraints on the robot's path and then allow the robot to find an optimal path subject to these constraints. This paper presents an anytime solution to the robot's path-planning problem when the human specifies a path constraint and an acceptable amount of...

Improvements in robot autonomy are changing the human-robot interaction from low-level manipulation to high-level task-based collaboration. For a task-oriented collaboration, a human assigns sub-tasks to robot team members. In this paper, we consider task-oriented collaboration of humans and robots in a cordon and search problem. We focus on a path...

During unmanned aerial vehicle (UAV) search missions, efficient use of UAV flight time requires flight paths that maximize the probability of finding the desired subject. The probability of detecting the desired subject based on UAV sensor information can vary in different search areas due to environment elements like varying vegetation density or...

Leveraging the abilities of multiple affordable robots as a swarm is enticing because of the resulting robustness and emergent behaviors of a swarm. However, because swarms are composed of many different agents, it is difficult for a human to influence the swarm by managing individual agents. Instead, we propose that human influence should focus on...

Cognitive engineering is an interdisciplinary approach to the analysis, modeling, and design of engineered systems or workplaces in which humans and technologies jointly operate to achieve system goals. As individuals, teams, and organizations become increasingly reliant on information technology and automation, it is more important than ever for s...

Models of swarming and modes of controlling them are numerous; however, to date swarm researchers have mostly ignored a fundamental problem that impedes scalable human interaction with large bio-inspired robot swarms, namely, how do you know what the swarm is doing if you can't observe every agent in the collective? We examine swarm models that exh...

Unmanned aerial systems (UASs) often require multiple human operators fulfilling diverse roles for safe correct operation. Although some dispute the utility of minimizing the number of humans needed to administer a UAS (Murphy and Burke 2010), minimization remains a long-standing objective for many designers. This paper presents work toward underst...

In this review, we explore how teleoperation could potentially be applied to the management of humanoid robots, with an emphasis on humanoid robots that are used in assistive roles, including clinical therapies. Since there are very few examples of the remote operation of a full humanoid, the review emphasizes technologies that are potentially rele...

A swarm is a group of uninformed individuals that exhibit collective behaviors. The group without any information has limited ability to achieve complex goals. Human-swarm interaction methods often allow a human to influence these uninformed individuals through either leadership or predation as informed agents that directly interact with humans. Th...

Currently, a single Unmanned Aerial System (UAS) requires several humans managing different aspects of the problem. Human roles often include vehicle operators, payload experts, and mission managers [1-3]. As a step toward reducing the number of humans required, it is desirable to reduce operator workload through effective distributed control, augm...

Human-swarm interaction methods often allow a human to influence a swarm through either leadership or predation. These methods of influence have two main limitations: (1) although leaders sustain influence over nominal agents for a long period of time, they tend to cause all collective structures to turn in to flocks (negating the benefit of other...

In this paper we evaluate the scalability of human-swarm interaction (HSI) in terms of operator workload and asses the impact of three control methods on swarm performance and operator workload. Specifically, we investigate the ability of HSI to (1) overcome fanout limitations of traditional supervisory control and (2) manage a wide range of team s...

Unmanned Aerial Vehicles (UAVs) are increasingly becoming economical platforms for carrying a variety of sensors. Building flight plans that place sensors properly, temporally and spatially, is difficult. The goal of sensor-driven planning is to automatically generate flight plans based on desired sensor placement and temporal constraints. We prese...

We consider a set of team-based information tasks, meaning that the team's goals are to choose behaviors that provide or enhance information available to the team. These information tasks occur across a region of space and must be performed for a period of time. We present a Bayesian model for (a) how information flows in the world and (b) how info...

Papers from a flagship conference reflect the latest developments in the field, including work in such rapidly advancing areas as human-robot interaction and formal methods. Robotics: Science and Systems VIII spans a wide spectrum of robotics, bringing together contributions from researchers working on the mathematical foundations of robotics, robo...

Learning in the presence of adaptive, possibly antagonistic, agents presents special challenges to algorithm designers, especially in environments with limited information. We consider situations in which an agent knows its own set of actions and observes its own payos, but does not know or observe the actions and payos of the other agents. Despite...

Robots show potential to help people with autism spectrum disorder (ASD). A great obstacle in using robots as part of therapy is customizing robot behavior. Clinicians need a low-cost way to rapidly animate robots. There is a tradeoff between quickly creating animations and creating quality animations, but both aspects are important. Based on clini...

This handbook collects and organizes contemporary cognitive engineering research, drawing on the original research of more than 60 contributing experts. Coverage of human factors, human-computer interaction, and the conceptual foundations of cognitive engineering is extensive, addressing not only cognitive engineering in broader organizations and c...

Robotic systems composed of a large number of entities, often called robot swarms, are envisioned to play an increasingly important role in applications such as search, rescue, surveillance, and reconnaissance operations. Nowadays, many mobile robots that are deployed for such applications are still tele-operated by a single or multiple operators....

Swarm robots use simple local rules to create complex emergent behaviors. The simplicity of the local rules allows for large numbers of low-cost robots in deployment, but the same simplicity creates difficulties when deploying in many applicable environments. These complex missions sometimes require human operators to influence the swarms towards a...

Every year there are numerous cases of individuals becoming lost in remote wilderness environments. Principles of search theory have become a foundation for developing more efficient and successful search and rescue methods. Measurements can be taken that describe how easily a search object is to detect. These estimates allow the calculation of the...

In this paper we propose a bio-inspired model for a decentralized swarm of robots, similar to the model proposed by Couzin [5], that allows for dynamic task assignment and is robust to limited communication from a human. We provide evidence that the model has two fundamental attractors: a torus attractor and a flock attractor. Through simulation an...

It can be difficult for humans to control large numbers (100-200) of robots performing coordinated tasks. Organizational constraints may be imposed that allow a human to issue commands or plays that dictate collective behavior, but naive hierarchical approaches can suffer from robustness issues in cases where key robots or communication channels ar...

form only given. Current practice in Wilderness Search and Rescue (WiSAR) can be viewed as a collaborative system designed to gather and analyze information to find missing persons in remote areas. The system consists of multiple parts - various tools for information management (maps, GPS, etc) distributed across personnel with different skills and...

In wilderness search and rescue, objects not native or typical to a scene may provide clues that indicate the recent presence of the missing person. This paper presents the results of augmenting an aerial wilderness search-and-rescue system with an automated spectral anomaly detector for identifying unusually colored objects. The detector dynamical...

User interface technology and a therapy model create a limited, "low-dose" role for a robot on a therapy team for children with autism spectrum disorder.

In this position paper, we synthesize "within the system" models of human influence over bio-inspired swarms, summarizing observations from previous experiments and identifying methods of influence that have not yet been explored. We describe (a) differences among agents that can be controlled by a human and those that can't, (b) agents that are aw...

In designing and evaluating human-machine systems, cogni-tive models can be used to (a) provide design principles and (b) guide the construction of experiments. In this paper, we present an information processing model of cognition that we have used extensively in designing and evaluating interfaces and autonomy modes. This model uses a conventiona...

In this paper, we formalize the problem of human interaction with bio-inspired robot teams (HuBIRT). The formalism applies to a large class of bio-inspired team dynamics and uses simple algebraic graph theory representations to distinguish between interagent influence, environmental influence, and operator influence. These representations lead to m...

We describe an approach examining multi-level collaboration challenges by integrating social, organizational, and cultural factors for human-robot teams operating in the real world. We discuss the research at three levels of social interaction: within a team, within a social environment, and within a culture. We first describe research exploring ps...

Automated agents for electricity markets, social networks, and other distributed networks must repeatedly interact with other intelligent agents, often without observing associates' actions or payoffs (i.e., minimal information). Given this reality, our goal is to create algorithms that learn effectively in repeated games played with minimal inform...

Satisfìcing, or being “good enough,” is the fundamental obligation of rational decision makers. An operational definition of what it means to be good enough is a prerequisite for making rational choices. In a departure from the traditional notion of satisficing as a species of bounded rationality satisficing is here redefined in terms of a notion o...

Robots show potential to be helpful in therapy for children with autism, but there are open questions on how to control the robots. Because clinicians typically lack programming experience, they must currently ask a programmer to program the robots. We hypothesize that clinicians are able to program robots sufficiently for their needs if the progra...

Robots appear to be engaging to many children with autism, and evidence suggests that engagement can facilitate social interaction not only between child and robot but also between child and another human. To date, no objective evidence has established a link between short-term child-robot interactions and long-term child-human interactions. We rep...

Robots excel at planning and performing tasks in controlled environments, but poor perception often leads to poor performance in unstructured environments. One typical way of improving robot performance is to give more control to a human operator and then design user interfaces that build the operator's situation awareness. As an alternative, human...

We consider the problem of learning in repeated general-sum matrix games when a learning algorithm can observe the actions but not the payoffs of its associates. Due to the non-stationarity of the environment caused by learning associates in these games, most state-of-the-art algorithms perform poorly in some important repeated games due to an inab...

Although much work has been done on designing autonomy and user interfaces for managing small teams of independent robots, much less is known about managing large-scale bio-inspired robot (BIRT) teams. In this paper, we explore human interaction with BIRT teams in an information foraging task. We summarize results from two small experiments that us...

Automated agents for electricity markets, social networks, and other distributed networks must repeatedly interact with other intelligent agents, often without observing associates' actions or payoffs (i.e., minimal information). Given this reality, our goal is to create algorithms that learn effectively in repeated games played with minimal inform...

Robots show potential to be helpful in therapy for children with autism spectrum disorder. In our experience, clinicians occasionally desire to change the robot behavior to suit the needs of different children. Because clinicians typically lack programming experience, they must currently ask a programmer to program the robots. Robots may be more us...

Learning is one way to determine how agents should act, but learning in multi-agent systems is more difficult than in single-agent systems because other learning agents modify their behavior. We introduce a particle-based algorithm called MMM-PHC. MMM-PHC promotes convergence to Nash equilibria in matrix games using the ideas of maxim in strategies...

The goal of a learning agent playing a repeated game is to maximize its payoffs over time. In repeated games with other learning agents, this often requires that an agent must learn to offer and accept profitable compromises. To do so, past research suggests that agents must implement both teaching and following strategies. However, few algorithms...

Robot-based autism therapy is a rapidly developing area of research, with a wide variety of robots being developed for use in clinical settings. Specific, detailed requirements for robots and user interfaces are needed to provide guidelines for the creation of robots that more effectively assist therapists in autism therapy. This paper enumerates a...

This paper explores multi-operator supervisory control (MOSC) of multiple independent robots using two complementary approaches: a human factors experiment and an agent-based simulation. The experiment identifies two task and environment limitations on MOSC: task saturation and task diffusion. It also identifies the correlation between task special...

In Wilderness Search and Rescue (WiSAR), the incident commander (IC) creates a probability distribution map of the likely location of the missing person. This map is important because it guides the IC in allocating search resources and coordinating efforts, but it often depends almost exclusively on the missing person profile, prior experience, and...

Current practice in Wilderness Search and Rescue (WiSAR) is analogous to an intelligent system designed to gather and analyze information to find missing persons in remote areas. The system consists of multiple parts - various tools for information management (maps, GPS, etc) distributed across personnel with different skills and responsibilities....

Video-equipped mini unmanned aerial vehicles (mini-UAVs) are becoming increasingly popular for surveillance, remote sensing, law enforcement, and search and rescue operations, all of which rely on thorough coverage of a target observation area. However, coverage is not simply a matter of seeing the area (visibility) but of seeing it well enough to...

Video-equipped mini unmanned aerial vehicles (mini-UAVs) are becoming increasingly popular for surveillance, remote sensing, law enforcement, and search and rescue operations, all of which rely on thorough coverage of a target observation area. However, coverage is not simply a matter of seeing the area (visibility) but of seeing it well enough to...