A Formalization of the Coach Problem

Coordination is an important aspect of multi-agent teamwork. In the context of robot soccer in the RoboCup Standard Platform League, our focus is on the coach as an external observer of the team, aiming to provide his teammates with effective tactical advice during matches. The coach problem can be approached from different angles: in order to adapt the behaviour of his teammates, he should at first be able to perform plan recognition on their observable actions. Furthermore, in providing them with appropriate advice, he should still adhere to the norms and regulations of the match to prevent penalties for his team. Also, when teammates' profiles and attributes are unknown or the system is only partially observable, coordination should be more 'ad hoc' to ensure robustness of the Multi-Agent System (MAS). In this work, we present a formalization of the problem of designing a coach in robot soccer, employing a temporal deontic logical framework. The framework is based on agent organizations[10], in which social coordination and norms play an important part

Artificial Intelligence and Systems Theory: Applied to Cooperative Robots

This paper describes an approach to the design of a population of cooperative robots based on concepts borrowed from Systems Theory and Artificial Intelligence. The research has been developed under the SocRob project, carried out by the Intelligent Systems Laboratory at the Institute for Systems and Robotics - Instituto Superior Tecnico (ISR/IST) in Lisbon. The acronym of the project stands both for "Society of Robots" and "Soccer Robots", the case study where we are testing our population of robots. Designing soccer robots is a very challenging problem, where the robots must act not only to shoot a ball towards the goal, but also to detect and avoid static (walls, stopped robots) and dynamic (moving robots) obstacles. Furthermore, they must cooperate to defeat an opposing team. Our past and current research in soccer robotics includes cooperative sensor fusion for world modeling, object recognition and tracking, robot navigation, multi-robot distributed task planning and coordination, including cooperative reinforcement learning in cooperative and adversarial environments, and behavior-based architectures for real time task execution of cooperating robot teams

Using Monte Carlo Search With Data Aggregation to Improve Robot Soccer Policies

RoboCup soccer competitions are considered among the most challenging multi-robot adversarial environments, due to their high dynamism and the partial observability of the environment. In this paper we introduce a method based on a combination of Monte Carlo search and data aggregation (MCSDA) to adapt discrete-action soccer policies for a defender robot to the strategy of the opponent team. By exploiting a simple representation of the domain, a supervised learning algorithm is trained over an initial collection of data consisting of several simulations of human expert policies. Monte Carlo policy rollouts are then generated and aggregated to previous data to improve the learned policy over multiple epochs and games. The proposed approach has been extensively tested both on a soccer-dedicated simulator and on real robots. Using this method, our learning robot soccer team achieves an improvement in ball interceptions, as well as a reduction in the number of opponents' goals. Together with a better performance, an overall more efficient positioning of the whole team within the field is achieved

Analysing the behaviour of robot teams through relational sequential pattern mining

This report outlines the use of a relational representation in a Multi-Agent domain to model the behaviour of the whole system. A desired property in this systems is the ability of the team members to work together to achieve a common goal in a cooperative manner. The aim is to define a systematic method to verify the effective collaboration among the members of a team and comparing the different multi-agent behaviours. Using external observations of a Multi-Agent System to analyse, model, recognize agent behaviour could be very useful to direct team actions. In particular, this report focuses on the challenge of autonomous unsupervised sequential learning of the team's behaviour from observations. Our approach allows to learn a symbolic sequence (a relational representation) to translate raw multi-agent, multi-variate observations of a dynamic, complex environment, into a set of sequential behaviours that are characteristic of the team in question, represented by a set of sequences expressed in first-order logic atoms. We propose to use a relational learning algorithm to mine meaningful frequent patterns among the relational sequences to characterise team behaviours. We compared the performance of two teams in the RoboCup four-legged league environment, that have a very different approach to the game. One uses a Case Based Reasoning approach, the other uses a pure reactive behaviour.Comment: 25 page

Multi-Agent Coordination for a Partially Observable and Dynamic Robot Soccer Environment with Limited Communication

RoboCup represents an International testbed for advancing research in AI and robotics, focusing on a definite goal: developing a robot team that can win against the human world soccer champion team by the year 2050. To achieve this goal, autonomous humanoid robots' coordination is crucial. This paper explores novel solutions within the RoboCup Standard Platform League (SPL), where a reduction in WiFi communication is imperative, leading to the development of new coordination paradigms. The SPL has experienced a substantial decrease in network packet rate, compelling the need for advanced coordination architectures to maintain optimal team functionality in dynamic environments. Inspired by market-based task assignment, we introduce a novel distributed coordination system to orchestrate autonomous robots' actions efficiently in low communication scenarios. This approach has been tested with NAO robots during official RoboCup competitions and in the SimRobot simulator, demonstrating a notable reduction in task overlaps in limited communication settings.Comment: International Conference of the Italian Association for Artificial Intelligence (AIxIA 2023) - Italian Workshop on Artificial Intelligence and Robotics (AIRO) Rome, 6 - 9 November, 202

Strategy planning for collaborative humanoid soccer robots based on principle solution

The final publication is available at Springer via http://dx.doi.org/10.1007/s11740-012-0416-4[EN] Collaborative humanoid soccer robots are currently under the lime light in the rapidly advancing research area of multi-robot systems. With new functionalities of software and hardware, they are becoming more versatile, robust and agile in response to the changes in the environment under dynamic conditions. This work focuses on a new approach for strategy planning of humanoid soccer robot teams as in the RoboCup Standard Platform League. The key element of the approach is a holistic system model of the principle solution encompassing various strategies of a soccer robot team. The benefits of the model-based approach are twofold¿it enables intuitive behavioral specification of the humanoid soccer robots in line with the team strategies envisaged by the system developers, and it systematizes the realization of their collaborative behaviors based on the principle solution. The principle solution is modeled with the newly developed specification technique CONSENS for the conceptual design of mechatronic and self-optimizing systems.The specification technique CONSENS was developed in the course of the Collaborative Research Center 614 ‘‘Self-Optimizing Concepts and Structures in Mechanical Engineering’’ funded by the German Research Foundation (DFG) under grant number SFB 614. The first two authors are funded by the Ministry of Higher Education Malaysia under the grant number 600-RMI/ST/ FRGS 5/3/Fst (256/2010) and 600-RMI/ERGS 5/3 (23/2011).Low, CY.; Aziz, N.; Aldemir, M.; Dumitrescu, R.; Anacker, H.; Mellado Arteche, M. (2013). Strategy planning for collaborative humanoid soccer robots based on principle solution. Production Engineering. 7(1):23-34. https://doi.org/10.1007/s11740-012-0416-4S233471Asada M, Kitano H (1999) The RoboCup challenge. Rob Auton Syst 29:3–12Spaan MTJ, Groen FCA (2002) Team coordination through roles, positioning and coordinated procedures. RoboCupLau N, Lopes LS, Corrente G, Nelson F (2009) Multi-robot team coordination through roles, positionings and coordinated procedures. In: 2009 IEEE/RSJ international conference on intelligent robots and systems, October 11–15, St. Louis, USAIocchi L, Nardi D, Piaggo M, Sgorbissa A (2003) Distributed coordination in heterogeneous multi-robot systems. Auton Robots 15:155–168Almeida F, Lau N, Reis LP (2010) A survey on coordination methodologies for simulated robotic soccer teams, multi-agent logics, languages, and organisations federated workshops (MALLOW 2010). Lyon, FranceLückel J, Hestermeyer T, Liu-Henke X (2001) Generalization of the Cascade principle in view of structured form of mechatronic systems. In: IEEE/ASME international conference on advanced intelligent mechatronics (AIM 2001), Villa Olmo, Como, ItalyInternational Council on Systems Engineering (INCOSE) (2007) Systems engineering vision 2020. Incose-TP-2004-004-02, SeptemberGausemeier J, Frank U, Donoth J, Kahl S (2009) Specification technique for the description of self-optimizing mechatronic systems. Res Eng Des 20(4):201–223Cyberbotics Ltd., Webots overview. 20 September 2012 at http://www.cyberbotics.com/overviewBirkhofer H (1980) Analyse und Synthese der FunktionenTechnischerProdukte. Dissertation, TechnischeUniversitätBraunschweigLanglotz G (2000) Ein Beitrag zur Funktionsstrukturentwicklung Innovativer Produkte. Dissertation, Institut fuerr Rechneranwendung in Planung und Konstruktion, Universitaet Karlsruhe, Shaker-Verlag, Band 2/2000, AachenPostel J (1980) User Datagram Protocol. RFC 760, USC/Information Sciences Institut