Multi-agent multi-team systems are commonly seen in environments where hierarchical layers of goals are at play. For example, theater-wide combat scenarios where multiple levels of command and... Show More

Multi-agent multi-team systems are commonly seen in environments where hierarchical layers of goals are at play. For example, theater-wide combat scenarios where multiple levels of command and control are required for proper execution of goals from the general to the foot soldier. Additionally, similar structures can be seen in game environments, where agents work together as teams to compete with other teams. The different agents within the same team must, while maintaining their own “personality”, work together and coordinate with each other to achieve a common team goal. This paper develops strategy-based multi-agent multi-team systems, where strategy is framed as an instrument at the team level to coordinate the multiple agents of a team in a cohesive way. The authors present SiMAMT, a framework for strategy-based multi-agent multi-team systems. The different components of the framework, including strategy simulation, strategy inference, strategy evaluation, and strategy selection are described. A formal specification of strategy and strategy-based multi-agent multi-team systems is provided. An example and experimental results are given to illustrate the proposed framework and its efficacy.

In this paper, the authors consider the planning problem for a system represented as a set of interacting agents evolving along time according to explicit timing constraints. Given a goal, the... Show More

In this paper, the authors consider the planning problem for a system represented as a set of interacting agents evolving along time according to explicit timing constraints. Given a goal, the planning problem is to find the sequence of actions such that the system reaches the goal state in a limited time and in an optimal manner, assuming actions have a cost. In their approach, the planning problem is based on model-checking and controller synthesis techniques while the goal is defined using temporal logic. Each agent of the system is represented using the formalism of Priced Timed Game Automata (PTGA). PTGA is an extension of Timed Automata that allows the representation of cost on actions and the definition of uncontrollable actions. The authors define a planning algorithm that computes the best strategy to achieve a goal. To experiment their approach, they extend the classical Transport Domain with timing constraints, cost on actions and uncontrollable actions. The planning algorithm is finally presented on a marine ecosystem management problem.

One approach to determining whether an automated system is performing correctly is to monitor its performance, signaling when the performance is not acceptable; another approach is to automatically... Show More

One approach to determining whether an automated system is performing correctly is to monitor its performance, signaling when the performance is not acceptable; another approach is to automatically analyze the possible behaviors of the system a-priori and determine performance guarantees. Thea authors have applied this second approach to automatically derive performance guarantees for behavior-based, multi-robot critical mission software using an innovative approach to formal verification for robotic software. Localization and mapping algorithms can allow a robot to navigate well in an unknown environment. However, whether such algorithms enhance any specific robot mission is currently a matter for empirical validation. Several approaches to incorporating pre-existing software into the authors' probabilistic verification framework are presented, and one used to include Monte-Carlo based localization software. Verification and experimental validation results are discussed for real localization missions with this software, showing that the proposed approach accurately predicts performance.

In the domain of E&P petroleum chain, the authors have developed a study and proposed a conceptual framework for inserting direct rock data into reservoir models, through calibration of well logs.... Show More

In the domain of E&P petroleum chain, the authors have developed a study and proposed a conceptual framework for inserting direct rock data into reservoir models, through calibration of well logs. This type of data is often ignored or manual processed in actual petroleum reservoir modeling activities, due to its high cost of acquisition, or due to the high complexity for its modeling, interpretation and extrapolation. Direct rock data is the data that is acquired by direct observation of the rock, like descriptions of well cores, instead of indirect data, like seismic, well logs, etc. Directly observed data is important because it allows calibration of indirect interpretation methods, detection of errors in their evaluations and also to verify rock properties that is not possible when using indirect data. The authors claim that a well defined ontology can help in describing reservoir petrofacies, which are aggregates of rock data that can be detected through a special signature in the registers of indirectly collected rock data and, therefore, they can use to integrate rock data in reservoir models.