RoboCup: A challenge problem for AI and robotics.- Overview of RoboCup-97.- The RoboCup physical agent challenge: Goals and protocols for phase I.- The RoboCup synthetic agent challenge 97.- Playing soccer by modifying and combining primitive reactions.- Learning, deciding, predicting: The soccer playing mind.- Using decision tree confidence factors for multiagent control.- A role-based decision-mechanism for teams of reactive and coordinating agents.- Using an explicit model of teamwork in RoboCup.- Decision making by the characteristics and the interaction in multi-agent robotics soccer.- Real-time vision processing for a soccer playing mobile robot.- A method applied for soccer's behaviors using proper feedback and feedforward control.- A legged robot for RoboCup based on "OPENR".- JavaSoccer.- RoboCup-3D: The construction of intelligent navigation system.- Generating multimedia presentations for RoboCup soccer games.- Football in recent times: What we can learn from the newspapers.- The value of project-based education in robotics.- The CMUnited-97 small robot team.- Development of self-learning vision-based mobile robots for acquiring soccer robots behaviors.- MICROB: The french experiment in RoboCup.- Description of rogi-team.- Autonomous soccer robots.- Vision-based robot learning towards RoboCup: Osaka University "Trackies".- RoboCup97: An omnidirectional perspective.- Omni-directional autonomous robots cooperating for team play.- The spirit of Bolivia: Complex behavior through minimal control.- AT Humboldt - Development, practice and theory.- Refinement of soccer agents' positions using reinforcement learning.- The CMUnited-97 simulator team.- Co-evolving Soccer Softbot team coordination with genetic programming.- Learning cooperative behaviors in RoboCup agents.- Individual tactical play and action decision based on a short-term goal - team descriptions of team Miya and team Niken.- The reactive motion planning in the passive situation.- A reactive architecture for RoboCup competition.- Team: Kasuga-bitos with modulation of playing.- Team sicily.- Team description: Building teams using roles, responsibilities, and strategies.- A multi-layered behavior based system for controlling RoboCup agents.- Using ABC 2 in the RoboCup domain.- Integrating learning with motor schema-based control for a Robot Soccer Team.- Describing soccer game in EAMMO.- Team GAMMA: Agent programming on gaea.- Using reactive deliberation for real-time control of soccer-playing robots.- A Multi-layered planning architecture for soccer agent.

RoboCup-97 : robot soccer World Cup I

CBR is one of the techniques that can be applied to the task of approximating a function over high-dimensional, continuous spaces In Reinforcement Learning systems a learning agent is faced with the problem of assessing the desirability of the state it finds itself in If the state space is very large and/or continuous the availability of a suitable mechanism to approximate a value function – which estimates the value of single states – is of crucial importance In this paper, we investigate the use of case-based methods to realise that task The approach we take is evaluated in a case study in robotic soccer simulation

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CBR for state value function approximation in reinforcement learning

Notions of similarity and distance play an important role in informatics. Different disciplines have developed their own treatment of related measures. We consider this problem under the viewpoint of Case Based Reasoning. While distance and similarity can be considered to be formally equivalent, there exist some differences concerning their intuitive use which have impact to the composition of global measures from local ones.

Similarity and Distance in Case Based Reasoning

The problem of defining robot behaviors to completely address a large and complex set of situations is very challenging. We present an approach for robot's action selection in the robot soccer domain using Case-Based Reasoning techniques. A case represents a snapshot of the game at time t and the actions the robot should perform in that situation. We basically focus our work on the retrieval and reuse steps of the system, presenting the similarity functions and a planning process to adapt the current problem to a case. We present first results of the performance of the system under simulation and the analysis of the parameters used in the approach.

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Retrieving and reusing game plays for robot soccer

Case Based Reasoning is a feasible approach for recognizing and predicting behavior of agents within the RoboCup domain. Using the method described here, on average 98.4 percent of all situations within a game of virtual robotic soccer have been successfully classified as part of a behavior pattern. Based on the assumption that similar triggering situations lead to similar behavior patterns, a prediction accuracy of up to 0.54 was possible, compared to 0.17 corresponding to random guessing. Significant differences are visible between different teams, which is dependent on the strategic approaches of these teams.

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Recognizing and Predicting Agent Behavior with Case Based Reasoning

In this article we present a case-based approach for the selflocalization of autonomous robots based on local visual information of landmarks. The goal is to determine the position and the orientation of the robot sufficiently enough, despite some strongly incorrect visual information. Our approach to solve this problem makes use of case-based reasoning methods.

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Fault-Tolerant Self Localization by Case-Based Reasoning

This report discusses two major views on BDI deliberation for autonomous agents. The first view is a rather conceptual one, presenting general BDI design principles, namely heuristic options, decomposed reasoning and layered planning, which enable BDI deliberation in realtime domains. The second view is focused on the practical application of the design principles in RoboCup Simulation League. This application not only evaluates the usefulness in deliberation but also the usefulness in rapid cooperative implementation. We compare this new approach, which has been used in the Vice World Champion team AT Humboldt 98, to the old approach of AT Humboldt 97, and we outline our ideas for further improvements, which are still under work.

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BDI Design Principles and Cooperative Implementation in RoboCup

Our agent team AT Humboldt 99 (AT stands for “Agent Team”) was developed as extension of our former team AT Humboldt 98, which became vice champion at RoboCup-98. We started to extend it by improved skills, new options and a larger planning horizon, respectively. So the most features of our current team were already part of AT Humboldt 98 which has been briefly described in [3] and extensive described in [5]. A description of our first soccer team AT Humboldt 97, which became world champion at RoboCup-97, can be found in [1].

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Helmut Myritz

Papers

Fault-Tolerant Self Localization by Case-Based Reasoning

BDI Design Principles and Cooperative Implementation in RoboCup

AT Humboldt in RoboCup-99 (Team description)

AT Humboldt in RoboCup-99

Patient Scheduling in Clinical Studies with Multi-Agent Techniques.