Uva triearn 2001 team description
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UvA Triearn 2001 Team Description PowerPoint PPT Presentation

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UvA Triearn 2001 Team Description. Built by two Master students at the UoA Decided to develop a team from scratch Earlier team hadn't followed software standards Not well structured source code and not rigorous documentation They had several ideas for improvement of the low-level methods

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UvA Triearn 2001 Team Description

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UvA Triearn 2001 Team Description

  • Built by two Master students at the UoA

  • Decided to develop a team from scratch

    • Earlier team hadn't followed software standards

    • Not well structured source code and not rigorous documentation

    • They had several ideas for improvement of the low-level methods

    • This lead to an advanced synchronization method and accurate estimation techniques for positions and velocities

UvA Trilearn 2001 Team Description

  • Results

    • Fast-play strategy and optimal scoring policy

    • Multi-level log system for quick debugging of the source code

  • They also put a lot of effort into the documentation of the source code


  • The agents are capable of perception, reasoning and acting

  • UvA used a multi-threaded architecture, one thread for each of these tasks

  • To minimize the delay caused by I/O to and from the server, to maximize the time for planning


  • Three-layers:

    • Perception and acting, is the bottom layer

      • Hides the soccer-layer from the other two layers

    • Skill layer, uses the functionalities from the bottom layer to implement the different skills of each player like marking etc.

    • Control layer, determine the best possible action with the info from the skill layer depending on the current world-state

World Model

  • Each agent has a probability representation of the world state, based on past perceptions

  • For each object an estimation of the position and the velocity is stored with an confident value of the accuracy of the estimation

  • The world model is updated each time new information is received

World Model

  • The results:

    • The world model is always according the latest information

    • Objects are updated based on when they last were seen

  • Confidence values are decreased for each cycle an object hasn't been seen

World Model

  • Communication between the agents to improve the accuracy of the world model

  • The agent that has the best view communicate with the nearest agents and they update the parts that isn't visible

  • Low-level state information is used to derive high-level conclusion

  • Average of 17 player that have an up-to-date information about the world (arena)


  • Used to determine the optimal moment (time) to send the action to the server

    • Depends on arrival time of different messages, it changes from cycle to cycle

  • Result is that the latest information is sent to the server

  • Each action is checked to determine if the server actually preformed the action


  • If the action isn't preformed, then isn't it sent again to prevent that the following cycle contain two actions (clatch)

Optimal scoring

  • Determine the optimal shooting point and the probability of scoring at the present point of the ball, with the position of the goal-keeper

  • First problem to do this:

    • Determine the probability for the ball to go into goal from a given position

      • This was based on the assess experiment when a player was placed in front of the goal and moved further and further from the goal

Optimal scoring

  • Second problem is to determine if the ball will pass the goal-keeper, in a given situation

    • The player was placed straight in front of the goal and the goal-keeper was moved to different positions

  • Combining these results are used to determine the optimal scoring point

Team Strategy

  • Main philosophy

    • Keep the ball moving, quickly between the player, preferably forward

    • Pass the ball in front of wing attackers at the side to cutting through the opponents defence

    • Disorganize the other team

Team Strategy

  • Heterogonous players are used on the wings

    • They get tired more quickly, but UvA has found a formula to determine the quality of these players

    • The formula returns the utility-value based on the parameters of the player

    • Also used to determine which type of player to place at a specific position of a formation

    • The behaviour of each agent is based on where on the field the ball is located and where it is on the field

Deep Behaviour Projection Agent Architecture

  • Robo-cup need agents that are capable of several attributes

    • Isn’t enough with reaction in a certain situation

    • Need for a more strategic skills

    • Need for an agent-architecture that can use both high-level and low-level behaviour layer

DBP-agent architecture

The main goal is to develop an agent with increased reasoning behaviour

That the behaviour can “grow” into a higher level and, for every new level, be projected into a more basic behaviour


Translation between symbolic descriptions into low-level specification

Using feedback between emergent behaviour and symbolic descriptions

Have both embedded and emergent levels

Behaviour projection and abilities to use between functionalities of other levels

DBP-agent architecture

  • The feed-back from a emergent behaviour is assessed in a meta-action theory to derive behaviour on a high-level

  • A behaviour can be embedded or emergent

    • Results in that the agent use related behaviour of these types

DBP-agent framework

  • Explore the domain with a update function

  • Revise the information whenever new information becomes available

  • Visual inputs or from communication between team-members

  • This update the belief and revise the beliefs

DBP-agent Architecture

  • Earlier versions didn’t used inter-communication and world-models

  • Relied on reaction behaviour and emergent tactics

  • Communication and world-models has made a distinct improvement of the team-work

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