343 research outputs found
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
Near-Optimal Adversarial Policy Switching for Decentralized Asynchronous Multi-Agent Systems
A key challenge in multi-robot and multi-agent systems is generating
solutions that are robust to other self-interested or even adversarial parties
who actively try to prevent the agents from achieving their goals. The
practicality of existing works addressing this challenge is limited to only
small-scale synchronous decision-making scenarios or a single agent planning
its best response against a single adversary with fixed, procedurally
characterized strategies. In contrast this paper considers a more realistic
class of problems where a team of asynchronous agents with limited observation
and communication capabilities need to compete against multiple strategic
adversaries with changing strategies. This problem necessitates agents that can
coordinate to detect changes in adversary strategies and plan the best response
accordingly. Our approach first optimizes a set of stratagems that represent
these best responses. These optimized stratagems are then integrated into a
unified policy that can detect and respond when the adversaries change their
strategies. The near-optimality of the proposed framework is established
theoretically as well as demonstrated empirically in simulation and hardware
Simulation Strategic Positioning for Mobile Robot Roccer Wheels
Soccer robot is a combination of sports, robotics technology and multi agent system. The achieve goals in playing the ball, requires individual intelligence, and the ability of cooperation for individual skills. The success of a soccer robot team is influenced by the success of the robot player to enter the ball into the opponent's goal. In entering the ball into the opponent's goal needed an appropriate position and strategic. This research makes the design in finding a strategic position at the time of attack. The design divides the field into several small areas or the main grid, where each region gives different action. The position is said to be strategic if the robot passing success and has a fairly wide perspective against the goal. The strategic position is gained from the greatest opportunity of some of the decisive conditions. The calculation used is to use the Probability NaĂŻve Bayes Classifier by taking the maximum value that serve as a strategic position. So this research resulted in a design in finding strategic position
Multi-robot coordination using flexible setplays : applications in RoboCup's simulation and middle-size leagues
Tese de Doutoramento. Engenharia Informática. Faculdade de Engenharia. Universidade do Porto. 201
Belief-Desire-Intention in RoboCup
The Belief-Desire-Intention (BDI) model of a rational agent proposed by Bratman has strongly influenced the research of intelligent agents in Multi-Agent Systems (MAS). Jennings extended Bratman’s concept of a single rational agent into MAS in the form of joint-intention and joint-responsibility. Kitano et al. initiated RoboCup Soccer Simulation as a standard problem in MAS analogous to the Blocks World
problem in traditional AI. This has motivated many researchers from various areas of studies such as machine learning, planning, and intelligent agent research. The first RoboCup team to incorporate the BDI concept is ATHumboldt98 team by Burkhard et al.
In this thesis we present a novel collaborative BDI architecture modeled for RoboCup 2D Soccer Simulation called the TA09 team which is based on Bratman’s rational agent, influenced by Cohen and Levesque’s commitment, and incorporating Jennings’ joint-intention. The TA09 team features observation-based coordination, layered planning, and dynamic formation positioning
Making High-Performance Robots Safe and Easy to Use for an Introduction to Computing
Robots are a popular platform for introducing computing and artificial
intelligence to novice programmers. However, programming state-of-the-art
robots is very challenging, and requires knowledge of concurrency, operation
safety, and software engineering skills, which can take years to teach. In this
paper, we present an approach to introducing computing that allows students to
safely and easily program high-performance robots. We develop a platform for
students to program RoboCup Small Size League robots using JavaScript. The
platform 1) ensures physical safety at several levels of abstraction, 2) allows
students to program robots using the JavaScript in the browser, without the
need to install software, and 3) presents a simplified JavaScript semantics
that shields students from confusing language features. We discuss our
experience running a week-long workshop using this platform, and analyze over
3,000 student-written program revisions to provide empirical evidence that our
approach does help students.Comment: 8 pages, 7 figures, 4 table
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