3,242 research outputs found
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
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