Multi-Agent Plan Recognition (MAPR) aims to recognize dynamic team structures and team behaviors from the observed team traces (activity sequences) of a set of intelligent agents. Previous MAPR approaches required a library of team activity sequences (team plans) be given as input. However, collecting a library of team plans to ensure adequate coverage is often difficult and costly. In this paper, we relax this constraint, so that team plans are not required to be provided beforehand. We assume instead that a set of action models are available.

In this paper we propose a periodic solution to the problem of persistently covering a finite set of interest points with a group of autonomous mobile agents. These agents visit periodically the points and spend some time carrying out the coverage task, which we call coverage time. Since this periodic persistent coverage problem is NP-hard, we split it into three subproblems to counteract its complexity. In the first place, we plan individual closed paths for the agents to cover all the points. Second, we formulate a quadratically constrained linear program to find the optimal coverage times and actions that satisfy the coverage objective. Finally, we join together the individual plans of the agents in a periodic team plan by obtaining a schedule that guarantees collision avoidance. To this end, we solve a mixed integer linear program that minimizes the time in which two or more agents move at the same time. Eventually, we apply the proposed solution to an induction hob with mobile inductors for a domestic heating application and show its performance with experiments on a real prototype.

Multi-Agent Plan Recognition (MAPR) aims to recognize dynamic team structures and team behaviors from the observed team traces (activity sequences) of a set of intelligent agents. Previous MAPR approaches required a library of team activity sequences (team plans) be given as input. However, collecting a library of team plans to ensure adequate coverage is often difficult and costly. In this paper, we relax this constraint, so that team plans are not required to be provided beforehand. We assume instead that a set of action models are available.

Consider a search-and-rescue mission to find a hiker lost in the woods. Rescuers might want to deploy a squad of wheeled robots to roam the forest, perhaps with the aid of drones scouring the scene from above. The benefits of a robot team are clear. But orchestrating that team is no simple matter. How to ensure the robots aren't duplicating each other's efforts or wasting energy on a convoluted search trajectory?

NASA is nearly ready to attempt the first flight on another planet. The space agency's small helicopter, called Ingenuity, has been deposited in a flat area on Mars, and it is running through a series of final tests before it tries to lift into the thin Martian air. Ingenuity's first flight was originally slated for April 11, but the mission hit a snag during a pre-flight test. While trying to spin the helicopter's rotors at full speed without leaving the ground, Ingenuity's onboard computer ended the test early. NASA says the helicopter is safe and communicating with Earth.

Many objectives can be achieved (or may be achieved more effectively) only by a group of agents executing a team plan. If a team plan fails, it is often of interest to determine what caused the failure, the degree of responsibility of each agent for the failure, and the degree of blame attached to each agent. We show how team plans can be represented in terms of structural equations, and then apply the definitions of causality introduced by Halpern [2015] and degree of responsibility and blame introduced by Chockler and Halpern [2004] to determine the agent(s) who caused the failure and what their degree of responsibility/blame is. We also prove new results on the complexity of computing causality and degree of responsibility and blame, showing that they can be determined in polynomial time for many team plans of interest.

Multi-Agent Plan Recognition (MAPR) aims to recognize dynamic team structures and team behaviors from the observed team traces (activity sequences) of a set of intelligent agents. Previous MAPR approaches required a library of team activity sequences (team plans) be given as input. However, collecting a library of team plans to ensure adequate coverage is often difficult and costly. In this paper, we relax this constraint, so that team plans are not required to be provided beforehand. We assume instead that a set of action models are available.

Multi-Agent Plan Recognition (MAPR) aims to recognize dynamic team structures and team behaviors from the observed team traces (activity sequences) of a set of intelligent agents. Previous MAPR approaches required a library of team activity sequences (team plans) be given as input. However, collecting a library of team plans to ensure adequate coverage is often difficult and costly. In this paper, we relax this constraint, so that team plans are not required to be provided beforehand. We assume instead that a set of action models are available. Such models are often already created to describe domain physics; i.e., the preconditions and effects of effects actions. We propose a novel approach for recognizing multi-agent team plans based on such action models rather than libraries of team plans. We encode the resulting MAPR problem as a \emph{satisfiability problem} and solve the problem using a state-of-the-art weighted MAX-SAT solver. Our approach also allows for incompleteness in the observed plan traces. Our empirical studies demonstrate that our algorithm is both effective and efficient in comparison to state-of-the-art MAPR methods based on plan libraries.

Many interesting real-world tasks might be most efficiently, effectively and safely achieved with large teams of robots working together. For domains such as the military, rescue response and environmental monitoring, the ability for the team to be spread out in the environment collecting information and taking action is a key enabler. Over an extended period of time, we have developed an infrastructure that can be quickly implemented on a robot or software agent to allow that agent to become part of a team. That infrastructure, called Machinetta, works by implementing a theory of teamwork that knows how to execute Team Oriented Plans. The infrastructure understands how to allocate roles, share information, recover from failures and other routine coordination activities that do not need to be specified in the plan. In most applications of Machinetta, invocation of Team Oriented Plans is the mechanism by which the operator interacts with the team, letting them specify the team activities without worrying about low-level details. Recently we have extended the team oriented plan concept to include situational awareness and mixed initiative markup that tells the GUI what information and options to give to the operator at different points during plan execution. In recent experiments with teams of boats, we have begun including swarming behaviors as a part of the team plan, when useful. The innvocation of swarming behavior from within Team Oriented Plans, offers a new way of interacting with very large robotic teams.

Multi-Agent Plan Recognition (MAPR) seeks to proposed to formalize MAPR with a new model, revealing identify the dynamic team structures and team behaviors the distinction between the hardness of single and multi-agent from the observed activity sequences (team plan recognition, and solve MAPR problems in the model using traces) of a set of intelligent agents, based on a a first-cut approach, provided that a fully observed team library of known team activity sequences (team trace and a library of full team plans were given as input plans). Previous MAPR systems require that team [Banerjee et al., 2010]; etc. traces and team plans are fully observed. In this Despite the success of previous approaches, they either assume paper we relax this constraint, i.e., team traces and that agents in the same team can only execute a common team plans are allowed to be partial. This is an important activity, i.e., coordinated activities of agents are not allowed task in applying MAPR to real-world domains, in a team, or require that the team trace and team plans are since in many applications it is often difficult complete, i.e., missing values (activities that are missing) are to collect full team traces or team plans due not allowed. In many real-world applications, however, it is to environment limitations, e.g., military operation.