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Representing Patterns of autonomous agent dynamics in multi-robot systems
It is proposed that vocabularies for representing complex systems with interacting agents have a natural lattice hierarchical structure. We investigate this for the example
of simulated robot soccer, using data taken from the RoboCup simulation competition. Lattice hierarchies provide symbolic representations for reasoning about systems at appropriate levels. We note the difference between relational constructs being human supplied versus systems that abstract their own constructs autonomously. The lattice hierarchical representation underlies both
Analysing the behaviour of robot teams through relational sequential pattern mining
This report outlines the use of a relational representation in a Multi-Agent
domain to model the behaviour of the whole system. A desired property in this
systems is the ability of the team members to work together to achieve a common
goal in a cooperative manner. The aim is to define a systematic method to
verify the effective collaboration among the members of a team and comparing
the different multi-agent behaviours. Using external observations of a
Multi-Agent System to analyse, model, recognize agent behaviour could be very
useful to direct team actions. In particular, this report focuses on the
challenge of autonomous unsupervised sequential learning of the team's
behaviour from observations. Our approach allows to learn a symbolic sequence
(a relational representation) to translate raw multi-agent, multi-variate
observations of a dynamic, complex environment, into a set of sequential
behaviours that are characteristic of the team in question, represented by a
set of sequences expressed in first-order logic atoms. We propose to use a
relational learning algorithm to mine meaningful frequent patterns among the
relational sequences to characterise team behaviours. We compared the
performance of two teams in the RoboCup four-legged league environment, that
have a very different approach to the game. One uses a Case Based Reasoning
approach, the other uses a pure reactive behaviour.Comment: 25 page
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Dynamic Structures for Evolving Tactics and Strategies in Team Robotics
The autonomous robot systems of the future will be teams of robots with complementary specialisms. At any instant robot interactions determine relational structures, and sequences of these structures describe the team dynamics as trajectories through space and time. These structures can be represented in algebraic forms that are realizable as dynamic multilevel data structures within individual robots, as the basis of emergent team data structures. Such formalisms are necessary for robots to learn new individual and collective behaviours. The theory is illustrated by the example of robot soccer where robot interactions create structures and trajectories essential to the evolution of new tactics and strategies in a changing environment
RoboCup 2D Soccer Simulation League: Evaluation Challenges
We summarise the results of RoboCup 2D Soccer Simulation League in 2016
(Leipzig), including the main competition and the evaluation round. The
evaluation round held in Leipzig confirmed the strength of RoboCup-2015
champion (WrightEagle, i.e. WE2015) in the League, with only eventual finalists
of 2016 competition capable of defeating WE2015. An extended, post-Leipzig,
round-robin tournament which included the top 8 teams of 2016, as well as
WE2015, with over 1000 games played for each pair, placed WE2015 third behind
the champion team (Gliders2016) and the runner-up (HELIOS2016). This
establishes WE2015 as a stable benchmark for the 2D Simulation League. We then
contrast two ranking methods and suggest two options for future evaluation
challenges. The first one, "The Champions Simulation League", is proposed to
include 6 previous champions, directly competing against each other in a
round-robin tournament, with the view to systematically trace the advancements
in the League. The second proposal, "The Global Challenge", is aimed to
increase the realism of the environmental conditions during the simulated
games, by simulating specific features of different participating countries.Comment: 12 pages, RoboCup-2017, Nagoya, Japan, July 201
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