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

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UvA Triearn 2001 Team Description

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Uva triearn 2001 team description

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

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

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 model1

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 model2

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

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 scoring1

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

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 strategy1

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

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

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

Dbp framework


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 architecture1

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

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 architecture2

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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