31,318 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
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
Towards Structured Analysis of Broadcast Badminton Videos
Sports video data is recorded for nearly every major tournament but remains
archived and inaccessible to large scale data mining and analytics. It can only
be viewed sequentially or manually tagged with higher-level labels which is
time consuming and prone to errors. In this work, we propose an end-to-end
framework for automatic attributes tagging and analysis of sport videos. We use
commonly available broadcast videos of matches and, unlike previous approaches,
does not rely on special camera setups or additional sensors.
Our focus is on Badminton as the sport of interest. We propose a method to
analyze a large corpus of badminton broadcast videos by segmenting the points
played, tracking and recognizing the players in each point and annotating their
respective badminton strokes. We evaluate the performance on 10 Olympic matches
with 20 players and achieved 95.44% point segmentation accuracy, 97.38% player
detection score ([email protected]), 97.98% player identification accuracy, and stroke
segmentation edit scores of 80.48%. We further show that the automatically
annotated videos alone could enable the gameplay analysis and inference by
computing understandable metrics such as player's reaction time, speed, and
footwork around the court, etc.Comment: 9 page
Programming Robosoccer agents by modelling human behavior
The Robosoccer simulator is a challenging environment for artificial intelligence, where a human has to program a team of agents and introduce it into a soccer virtual environment. Most usually, Robosoccer agents are programmed by hand. In some cases, agents make use of Machine learning (ML) to adapt and predict the behavior of the opposite team, but the bulk of the agent has been preprogrammed. The main aim of this paper is to transform Robosoccer into an interactive game and let a human control a Robosoccer agent. Then ML techniques can be used to model his/her behavior from training instances generated during the play. This model will be used later to control a Robosoccer agent, thus imitating the human behavior. We have focused our research on low-level behavior, like looking for the ball, conducting the ball towards the goal, or scoring in the presence of opponent players. Results have shown that indeed, Robosoccer agents can be controlled by programs that model human play.Publicad
OMBO: An opponent modeling approach
In competitive domains, some knowledge about the opponent can give players a clear advantage. This idea led many people to propose approaches that automatically acquire models of opponents, based only on the observation of their input–output behavior. If opponent outputs could be accessed directly, a model can be constructed by feeding a machine learning method with traces of the behavior of the opponent. However, that is not the case in the RoboCup domain where an agent does not have direct access to the opponent inputs and outputs. Rather, the agent sees the opponent behavior from its own point of view and inputs and outputs (actions) have to be inferred from observation. In this paper, we present an approach to model low-level behavior of individual opponent agents. First, we build a classifier to infer and label opponent actions based on observation. Second, our agent observes an opponent and labels its actions using the previous classifier. From these observations, machine learning techniques generate a model that predicts the opponent actions. Finally, the agent uses the model to anticipate opponent actions. In order to test our ideas, we have created an architecture called OMBO (Opponent Modeling Based on Observation). Using OMBO, a striker agent can anticipate goalie actions. Results show that in this striker-goalie scenario, scores are significantly higher using the acquired opponent's model of actions.This work has been partially supported by the
Spanish MCyT under projects TRA2007-67374-
C02-02 and TIN-2005-08818-C04.Also, it has been
supported under MEC grant by TIN2005-08945-
C06-05. We thank anonymous reviewers for their
helpful comments.Publicad
Exploiting Opponent Modeling For Learning In Multi-agent Adversarial Games
An issue with learning effective policies in multi-agent adversarial games is that the size of the search space can be prohibitively large when the actions of both teammates and opponents are considered simultaneously. Opponent modeling, predicting an opponent’s actions in advance of execution, is one approach for selecting actions in adversarial settings, but it is often performed in an ad hoc way. In this dissertation, we introduce several methods for using opponent modeling, in the form of predictions about the players’ physical movements, to learn team policies. To explore the problem of decision-making in multi-agent adversarial scenarios, we use our approach for both offline play generation and real-time team response in the Rush 2008 American football simulator. Simultaneously predicting the movement trajectories, future reward, and play strategies of multiple players in real-time is a daunting task but we illustrate how it is possible to divide and conquer this problem with an assortment of data-driven models. By leveraging spatio-temporal traces of player movements, we learn discriminative models of defensive play for opponent modeling. With the reward information from previous play matchups, we use a modified version of UCT (Upper Conference Bounds applied to Trees) to create new offensive plays and to learn play repairs to counter predicted opponent actions. iii In team games, players must coordinate effectively to accomplish tasks while foiling their opponents either in a preplanned or emergent manner. An effective team policy must generate the necessary coordination, yet considering all possibilities for creating coordinating subgroups is computationally infeasible. Automatically identifying and preserving the coordination between key subgroups of teammates can make search more productive by pruning policies that disrupt these relationships. We demonstrate that combining opponent modeling with automatic subgroup identification can be used to create team policies with a higher average yardage than either the baseline game or domain-specific heuristics
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