Counter-Misdirection in Behavior-based Multi-robot Teams * Shengkang Chen, Graduate Student Member, IEEE and Ronald C. Arkin, Fellow, IEEE Abstract—When teams of mobile robots are tasked with misdirection approach using a novel type of behavior-based different goals in a competitive environment, misdirection and robot agents: counter-misdirection agents (CMAs). counter-misdirection can provide significant advantages. Researchers have studied different misdirection methods but the II. BACKGROUND number of approaches on counter-misdirection for multi-robot A. Robot Deception systems is still limited. In this work, a novel counter-misdirection approach for behavior-based multi-robot teams is developed by Robotic deception can be interpreted as robots using deploying a new type of agent: counter misdirection agents motions and communication to convey false information or (CMAs). These agents can detect the misdirection process and conceal true information [4],[5]. Deceptive behaviors can have “push back” the misdirected agents collaboratively to stop the a wide range of applications from military [6] to sports [7]. misdirection process. This approach has been implemented not only in simulation for various conditions, but also on a physical Researchers have studied robotic deception previously [4] robotic testbed to study its effectiveness. It shows that this through computational approaches [8] in a wide range of areas approach can stop the misdirection process effectively with a from human–robot interaction (HRI) [9]–[11] to ethical issues sufficient number of CMAs. This novel counter-misdirection [4]. Shim and Arkin [5] proposed a taxonomy of robot approach can potentially be applied to different competitive deception with three basic dimensions: interaction object, scenarios such as military and sports applications. interaction goal and interaction type. This taxonomy provides I. INTRODUCTION a basic metric of robot deception. Based on this taxonomy, they [6] developed a computational framework to allow the robot’s Humans and Animals commonly use deception to provide deceptive actions to benefit human users. This work was benefits or advantages for themselves. For instance, blue jays extended to a scenario where robots act deceptively to control can mimic the sound of hawks to scatter other birds in the area victims' fear and shock in search and rescue [7]. Additionally, so that they will have fewer competitors for food [1]. In the they [8] have shown that robots can model deceptive behavior same manner, with the growth of robotic intelligence, applying from small animals. Inspired by the caching behavior of a deceptive behaviors may be beneficial to robotic systems. For squirrel, a robotic deceptive strategy was developed. Instead of example, robots could deceive others including humans or going directly to the caching location, the robot moved other robots by sending inaccurate or falsified information between the actual caching location and the fake locations in purposely. This concept is known as robotic misdirection -- order to confuse competing robots. Furthermore, in human– robots misleading other agents to the wrong location that may robot interaction (HRI), researchers have studied deception in be traps or other remote locations to gain advantage over them. robot motion context. Srinivasa's team analyzed and classified To counter this misdirection, robotic counter-misdirection different deceptive robot motions used to approach different strives to stop this misdirection process or negate its effects. goal areas [4]. Although studies on robotic misdirection [1]–[3] exist, the field of robotic counter-misdirection has been understudied. While most research on robotic deception has focused on single robotic systems, few studies considered multi-robot In a competitive multi-robot environment such as systems. Arkin’s group is one of the few research groups that competitions, some robots can use misdirection techniques to studied deception in multi-robot teams. Earlier their research provide them with an edge over others to accomplish their involved studying mobbing behavior where birds harass a goals. As a result, counter-misdirection becomes crucial for predator for deterrence, resulting in a deceptive model for these misdirected robots. Two main components in counter- multi-robot systems [9]. While this deceptive model was misdirection include misdirection detection and misdirection designed specifically for predator deterrence, the misdirection stoppage. Based on these two main components, a new type framework of this paper is intended for group movement of agent called counter-misdirection agent (CMA) is misdirection scenarios. presented. These agents detect the misdirection process by observing misdirected agents' movements and then forming a B. Group Behaviors in Multi-robot Teams “barrier” to stop them. Multi-robot teams are modeled using group behaviors for The main contributions of this research involve providing a different purposes and scenarios. For example, lekking misdirection/counter-misdirection framework for behavior- behavior from birds has been implemented in multi-robot based multi-robot systems and developing a counter- teams for multi-robot groups [10] where members from a multi-robot team follow the leader of the team while * Research supported by the National Science Foundation under CNS S. Chen is in Electrical and Computer Engineering, R.C. Arkin is in EAGER grant #1848653. Interactive Computing, Georgia Institute of Technology, Atlanta, GA 30308, USA (e-mail: schen754/[email protected]). maintaining a certain distance. To model the flocking of shills that move with intention, characterized by their high behaviors of animals, the selfish herd model [11] and the Boids linear velocity movement in a common direction with small model [12], [13] were proposed. Selfish herd model [11] is directional changes, this induces mark agents with low group used to mimic a flocking behaviors of a group of sheep or following thresholds to follow the shills. In this manner, mark ducks. Similar to the selfish herd model, the Boids model [12], agents with higher thresholds will also follow as the number [13] which tries to mimic the behavior of a flock of birds, of agents, including the newly recruited lower threshold enables agents to stay close to nearby agents without collision. marks, that move with intention increases. This paper reports Another group behavior model uses the threshold model [14], on research that counteracts this earlier approach [23], since which models the behavior of a crowd of people. When the in addition to the active misdirection tactics utilized earlier, number of nearby individuals that engage in a similar action counter-misdirection strategies are now investigated to halt or exceeds the "threshold" for a nearby individual, this agent will reduce the induced misdirection. likely undertake this action as well. III. MODEL Robots have been used to drive animals to designated In our misdirection and counter-misdirection simulation locations or divert them away from certain locations based on scenarios, each agent can move in a two-dimensional space and their natural flocking behaviors [15]–[18]. Vanghan et al. [16], ⃗⃗⃗⃗ [18] used a robot as a sheepdog to herd ducks to a desired 푙푖,푡 = [푥푖,푡, 푦푖,푡] represents agent ’s location at time 푡 with a location. Strombom et al. [15] proposed a 2-step algorithm to limited observation range 푟푖. In the behavior-based approach drive unwilling agents to move in the same direction using a [24], the movement of each agent is based on its motion vector ⃗⃗⃗⃗⃗⃗⃗⃗⃗ ⃗⃗⃗⃗ single agent. Researchers have also studied diverting flocks of from the current behavior assemblage: 푙푖,푡+1 = 푙푖,푡 + 푣⃗⃗⃗푖⃗⃗,푡 ∗ 훿푡 birds from airports [19], [20] using the Boids model for the where 훿푡 is the time step. The motion vector 푣⃗⃗⃗푖⃗⃗,푡 is the birds. These methods are considered fear-based ‘push’ weighted sum of behavior vectors provided by the primitive approaches where robots act as predators to force animals to behaviors from the current behavior assemblage: ⃗푣⃗⃗푖⃗⃗,푡 = move away from the shepherding robots. ∑ ⃗⃗⃗ 푗∈퐸푖 휌푗푏푗 where 퐸푖 is the set of all composing primitive Based on the threshold model, Pettinati and Arkin [2] ⃗⃗⃗ behaviors and 푏푗 is the behavior vector of primitive behaviors developed an alternative “pull” approach that attracts target with gain 휌 . The definition of each primitive behavior is agents toward the goal area. Compared with the “push” method 푗 described in Appendix I. which requires at least one agent to act as a predator, the “pull” method deploys shills which are embedded among the mark There are four types of robot agents: mark, shill, leader and (target) agents to misdirect them to the goal area. counter-misdirection agent (CMA). These agents can be divided into three different groups: C. Robotic Misdirection • Mark group: a group of marks which is targeted to be Misdirection is one of the main forms of deception which misdirected, i.e., to be moved to a location not of their can influence the attentions or movements of an agent. own choosing.. Magicians often use misdirection in order to create a magical effect. As a result, misdirection has been an interesting area to • Misdirection team: a team composed of a leader and study in the field of psychology. Psychologists and magicians a certain number of shills with the intention to have developed different taxonomies for misdirection [21]. misdirect marks to their goal area. Among them, Lamont